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Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.
Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.
Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.
When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.
Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.
A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.
A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.
Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?
  • a)
    Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.
  • b)
    Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.
  • c)
    Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.
  • d)
    Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.
Correct answer is option 'C'. Can you explain this answer?
Most Upvoted Answer
Directions: Read the following passages carefully and identify most a...
In the first line the author mentions, 'Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customer' from this it is clear that the author differentiates between 'Analytics competitors' and 'traditional companies' by saying Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.
Hence, the correct option is (c).
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When people react to their experiences with particular authorities, those authorities and the organizations or institutions that they represent often benefit if the people involved begin with high levels of commitment to the organization or institution represented by the authorities. First, in his studies of people's attitudes toward political and legal institutions, Tyler found that attitudes after an experience with the institution were strongly affected by prior attitudes. Single experiences influence post experience loyalty but certainly do not overwhelm the relationship between pre-experience and post experience loyalty. Thus, the best predictor of loyalty after an experience is usually loyalty before that experience.Second, people with prior loyalty to the organization or institution judge their dealings with the organization's or institution's authorities to be fairer than do those with less prior loyalty, either because they are more fairly treated or because they interpret equivalent treatment as fairer.Although high levels of prior organizational or institutional commitment are generally beneficial to the organization or institution, under certain conditions high levels of prior commitment may actually sow the seeds of reduced commitment. When previously committed individuals feel that they were treated unfavourably or unfairly during some experience with the organization or institution, they may show an especially sharp decline in commitment. Two studies were designed to test this hypothesis, which, if confirmed, would suggest that organizational or institutional commitment has risks, as well as benefits. At least three psychological models offer predictions of how individuals' reactions may vary as a function of (1) their prior level of commitment and (2) the favorability of the encounter with the organization or institution. Favorability of the encounter is determined by the outcome of the encounter and the fairness or appropriateness of the procedures used to allocate outcomes during the encounter. First, the instrumental prediction is that because people are mainly concerned with receiving desired outcomes from their encounters with organizations, changes in their level of commitment will depend primarily on the favorability of the encounter. Second, the assimilation prediction is that individuals' prior attitudes predispose them to react in a way that is consistent with their prior attitudes.The third prediction, derived from the group-value model of justice, pertains to how people with high prior commitment will react when they feel that they have been treated unfavorably or unfairly during some encounter with the organization or institution. Fair treatment by the other party symbolizes to people that they are being dealt with in a dignified and respectful way, thereby bolstering their sense of self-identity and self worth. However, people will become quite distressed and react quite negatively if they feel that they have been treated unfairly by the other party to the relationship. The group-value model suggests that people value the information they receive that helps them to define themselves and to view themselves favorably. According to the instrumental viewpoint, people are primarily concerned with the more material or tangible resources received from the relationship. Empirical support for the group-value model has implications for a variety of important issues, including the determinants of commitment, satisfaction, organizational citizenship, and rule following. Determinants of procedural fairness include structural or interpersonal factors. For example, structural determinants refer to such things as whether decisions were made by neutral, fact finding authorities who used legitimate decision making criteria. The primary purpose of the study was to examine the interactive effect of individuals (1) commitment to an organization or institution prior to some encounter and (2) perceptions of how fairly they were treated during the encounter, on the change in their level of commitment. A basic assumption of the group-value model is that people generally value their relationships with people, groups, organizations, and institutions and therefore value fair treatment from the other party to the relationship. Specifically, highly committed members should have especially negative reactions to feeling that they were treated unfairly, more so than (1) less-committed group members or (2) highly committed members who felt that they were fairly treated.The prediction that people will react especially negatively when they previously felt highly committed but felt that they were treated unfairly also is consistent with the literature on psychological contracts. Rousseau suggested that, over time, the members of work organizations develop feelings of entitlement, i.e., perceived obligations that their employers have toward them. Those who are highly committed to the organization believe that they are fulfilling their contract obligations. However, if the organization acted unfairly, then highly committed individuals are likely to believe that the organization did not live up to its end of the bargain.For summarizing the passage, which of the following is most appropriate

When people react to their experiences with particular authorities, those authorities and the organizations or institutions that they represent often benefit if the people involved begin with high levels of commitment to the organization or institution represented by the authorities. First, in his studies of people's attitudes toward political and legal institutions, Tyler found that attitudes after an experience with the institution were strongly affected by prior attitudes. Single experiences influence post experience loyalty but certainly do not overwhelm the relationship between pre-experience and post experience loyalty. Thus, the best predictor of loyalty after an experience is usually loyalty before that experience. Second, people with prior loyalty to the organization or institution judge their dealings with the organization's or institution's authorities to be fairer than do those with less prior loyalty, either because they are more fairly treated or because they interpret equivalent treatment as fairer.Although high levels of prior organizational or institutional commitment are generally beneficial to the organization or institution, under certain conditions high levels of prior commitment may actually sow the seeds of reduced commitment. When previously committed individuals feel that they were treated unfavourably or unfairly during some experience with the organization or institution, they may show an especially sharp decline in commitment. Two studies were designed to test this hypothesis, which, if confirmed, would suggest that organizational or institutional commitment has risks, as well as benefits. At least three psychological models offer predictions of how individuals' reactions may vary as a function of (1) their prior level of commitment and (2) the favorability of the encounter with the organization or institution. Favorability of the encounter is determined by the outcome of the encounter and the fairness or appropriateness of the procedures used to allocate outcomes during the encounter. First, the instrumental prediction is that because people are mainly concerned with receiving desired outcomes from their encounters with organizations, changes in their level of commitment will depend primarily on the favorability of the encounter. Second, the assimilation prediction is that individuals' prior attitudes predispose them to react in a way that is consistent with their prior attitudes.The third prediction, derived from the group-value model of justice, pertains to how people with high prior commitment will react when they feel that they have been treated unfavorably or unfairly during some encounter with the organization or institution. Fair treatment by the other party symbolizes to people that they are being dealt with in a dignified and respectful way, thereby bolstering their sense of self-identity and self-worth. However, people will become quite distressed and react quite negatively if they feel that they have been treated unfairly by the other party to the relationship. The group-value model suggests that people value the information they receive that helps them to define themselves and to view themselves favorably. According to the instrumental viewpoint, people are primarily concerned with the more material or tangible resources received from the relationship. Empirical support for the group-value model has implications for a variety of important issues, including the determinants of commitment, satisfaction, organizational citizenship, and rule following. Determinants of procedural fairness include structural or interpersonal factors. For example, structural determinants refer to such things as whether decisions were made by neutral, fact-finding authorities who used legitimate decision-making criteria. The primary purpose of the study was to examine the interactive effect of individuals (1) commitment to an organization or institution prior to some encounter and (2) perceptions of how fairly they were treated during the encounter, on the change in their level of commitment. A basic assumption of the group-value model is that people generally value their relationships with people, groups, organizations, and institutions and therefore value fair treatment from the other party to the relationship. Specifically, highly committed members should have especially negative reactions to feeling that they were treated unfairly, more so than (1) less-committed group members or (2) highly committed members who felt that they were fairly treated.The prediction that people will react especially negatively when they previously felt highly committed but felt that they were treated unfairly also is consistent with the literature on psychological contracts. Rousseau suggested that, over time, the members of work organizations develop feelings of entitlement, i.e., perceived obligations that their employers have toward them. Those who are highly committed to the organization believe that they are fulfilling their contract obligations. However, if the organization acted unfairly, then highly committed individuals are likely to believe that the organization did not live up to its end of the bargain.For summarizing the passage, which of the following is most appropriate

Read the following passage carefully and answer the questions that follow.While sabbaticals are still rare inside of corporate America, their presence is increasing rapidly. According to a survey from the Society for Human Resource Management, the percentage of companies offering sabbaticals (both paid and unpai d) rose to nearly 17% of employers in 2017. That's a significant gain from 1977, when McDonald's instituted what was arguably the first corporate sabbatical program in the United States.Since the concept of sabbaticals is most popular in the academic arena, the majority of research done on their effect on employees has been conducted by studying professors. One notable study compared 129 university professors who took a sabbatical in a given term with 129 equally qualified colleagues who didn't. Both groups were surveyed before, during, and after the term to assess stress levels, psychological resources, and even overall life satisfaction. It's not surprising that the researchers found that those who took sabbaticals experienced, upon return, a decline in stress and an increase in psychological resources and overall well-being. What is surprising, however, is that those positive changes often remained long after the sabbatical takers returned to work.The bigger benefit to organizations, however, comes in unexpected ways. Providing sabbaticals or extended leave time to leaders can actually be a means to stress test the organizational chart and give aspiring leaders a chance to grow. In one study, researchers surveyed 61 leaders at five different non-profit organizations with sabbatical programs. Each organization had slightly different requirements, but all required at least three months off and discouraged executives from visiting the office during the sabbatical period.The researchers found that the majority of leaders surveyed said the time away allowed them the space to generate new ideas for innovating in the organization and helped them gain greater confidence in themselves as leaders. They also reported a better ability to collaborate with their board of directors, most likely because the planning and execution of the sabbatical provided a learning experience for everyone involved.At the very least, having people rotate out for an extended period of time allows organizations to stress test their organizational chart. Ideally, no team should be so dependent on any one person that productivity grinds to a halt during an extended vacation. And while it may look good on paper, the only way to know for sure is to test it. For instance, there are many unique vacation/sabbatical policies out there: The Motley Fool's approach, called "The Fool's Errand." Each month leadership of The Motley Fool draws a random name from the company roster and awards that person two weeks of paid time off with a catch: It must be taken in the next month.Whether it's a long-term sabbatical or a surprise vacation, the success of extended time off - for the organization - is an encouragement and a warning. The warning is that most organizations are probably not giving employees enough time away.Q. The tone of the author in the passage can best be described as

Read the following passage carefully and answer the questions that follow.While sabbaticals are still rare inside of corporate America, their presence is increasing rapidly. According to a survey from the Society for Human Resource Management, the percentage of companies offering sabbaticals (both paid and unpai d) rose to nearly 17% of employers in 2017. That's a significant gain from 1977, when McDonald's instituted what was arguably the first corporate sabbatical program in the United States.Since the concept of sabbaticals is most popular in the academic arena, the majority of research done on their effect on employees has been conducted by studying professors. One notable study compared 129 university professors who took a sabbatical in a given term with 129 equally qualified colleagues who didn't. Both groups were surveyed before, during, and after the term to assess stress levels, psychological resources, and even overall life satisfaction. It's not surprising that the researchers found that those who took sabbaticals experienced, upon return, a decline in stress and an increase in psychological resources and overall well-being. What is surprising, however, is that those positive changes often remained long after the sabbatical takers returned to work.The bigger benefit to organizations, however, comes in unexpected ways. Providing sabbaticals or extended leave time to leaders can actually be a means to stress test the organizational chart and give aspiring leaders a chance to grow. In one study, researchers surveyed 61 leaders at five different non-profit organizations with sabbatical programs. Each organization had slightly different requirements, but all required at least three months off and discouraged executives from visiting the office during the sabbatical period.The researchers found that the majority of leaders surveyed said the time away allowed them the space to generate new ideas for innovating in the organization and helped them gain greater confidence in themselves as leaders. They also reported a better ability to collaborate with their board of directors, most likely because the planning and execution of the sabbatical provided a learning experience for everyone involved.At the very least, having people rotate out for an extended period of time allows organizations to stress test their organizational chart. Ideally, no team should be so dependent on any one person that productivity grinds to a halt during an extended vacation. And while it may look good on paper, the only way to know for sure is to test it. For instance, there are many unique vacation/sabbatical policies out there: The Motley Fool's approach, called "The Fool's Errand." Each month leadership of The Motley Fool draws a random name from the company roster and awards that person two weeks of paid time off with a catch: It must be taken in the next month.Whether it's a long-term sabbatical or a surprise vacation, the success of extended time off - for the organization - is an encouragement and a warning. The warning is that most organizations are probably not giving employees enough time away.Q. Whether it's a long-term sabbatical or a surprise vacation, the success of extended time off - for the organization - is an encouragement and a warning. The warning is that most organizations are probably not giving employees enough time away.It can be inferred that the author could have extended the last paragraph to include how many of the following statements? The encouragement is that extended time pays off. How seriously companies take this warning, is yet to be seen. Rewarding sabbatical to employees increases the productivity of the company. The pros of rewarding sabbatical to employees far outweigh the cons.

When people react to their experiences with particular authorities, those authorities and the organizations or institutions that they represent often benefit if the people involved begin with high levels of commitment to the organization or institution represented by the authorities. First, in his studies of people's attitudes toward political and legal institutions, Tyler found that attitudes after an experience with the institution were strongly affected by prior attitudes. Single experiences influence post experience loyalty but certainly do not overwhelm the relationship between pre-experience and post experience loyalty. Thus, the best predictor of loyalty after an experience is usually loyalty before that experience. Second, people with prior loyalty to the organization or institution judge their dealings with the organization's or institution's authorities to be fairer than do those with less prior loyalty, either because they are more fairly treated or because they interpret equivalent treatment as fairer.Although high levels of prior organizational or institutional commitment are generally beneficial to the organization or institution, under certain conditions high levels of prior commitment may actually sow the seeds of reduced commitment. When previously committed individuals feel that they were treated unfavourably or unfairly during some experience with the organization or institution, they may show an especially sharp decline in commitment. Two studies were designed to test this hypothesis, which, if confirmed, would suggest that organizational or institutional commitment has risks, as well as benefits. At least three psychological models offer predictions of how individuals' reactions may vary as a function of (1) their prior level of commitment and (2) the favorability of the encounter with the organization or institution. Favorability of the encounter is determined by the outcome of the encounter and the fairness or appropriateness of the procedures used to allocate outcomes during the encounter. First, the instrumental prediction is that because people are mainly concerned with receiving desired outcomes from their encounters with organizations, changes in their level of commitment will depend primarily on the favorability of the encounter. Second, the assimilation prediction is that individuals' prior attitudes predispose them to react in a way that is consistent with their prior attitudes.The third prediction, derived from the group-value model of justice, pertains to how people with high prior commitment will react when they feel that they have been treated unfavorably or unfairly during some encounter with the organization or institution. Fair treatment by the other party symbolizes to people that they are being dealt with in a dignified and respectful way, thereby bolstering their sense of self-identity and self worth. However, people will become quite distressed and react quite negatively if they feel that they have been treated unfairly by the other party to the relationship. The group-value model suggests that people value the information they receive that helps them to define themselves and to view themselves favorably. According to the instrumental viewpoint, people are primarily concerned with the more material or tangible resources received from the relationship. Empirical support for the group-value model has implications for a variety of important issues, including the determinants of commitment, satisfaction, organizational citizenship, and rule following. Determinants of procedural fairness include structural or interpersonal factors. For example, structural determinants refer to such things as whether decisions were made by neutral, fact finding authorities who used legitimate decision making criteria. The primary purpose of the study was to examine the interactive effect of individuals (1) commitment to an organization or institution prior to some encounter and (2) perceptions of how fairly they were treated during the encounter, on the change in their level of commitment. A basic assumption of the group-value model is that people generally value their relationships with people, groups, organizations, and institutions and therefore value fair treatment from the other party to the relationship. Specifically, highly committed members should have especially negative reactions to feeling that they were treated unfairly, more so than (1) less-committed group members or (2) highly committed members who felt that they were fairly treated.The prediction that people will react especially negatively when they previously felt highly committed but felt that they were treated unfairly also is consistent with the literature on psychological contracts. Rousseau suggested that, over time, the members of work organizations develop feelings of entitlement, i.e., perceived obligations that their employers have toward them. Those who are highly committed to the organization believe that they are fulfilling their contract obligations. However, if the organization acted unfairly, then highly committed individuals are likely to believe that the organization did not live up to its end of the bargain.There is only one term in the left column which matches with the options given in the second column. Identify the correct pair from the following table

Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer?
Question Description
Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? for CAT 2025 is part of CAT preparation. The Question and answers have been prepared according to the CAT exam syllabus. Information about Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? covers all topics & solutions for CAT 2025 Exam. Find important definitions, questions, meanings, examples, exercises and tests below for Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer?.
Solutions for Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? in English & in Hindi are available as part of our courses for CAT. 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Here you can find the meaning of Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? defined & explained in the simplest way possible. Besides giving the explanation of Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer?, a detailed solution for Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? has been provided alongside types of Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? theory, EduRev gives you an ample number of questions to practice Directions: Read the following passages carefully and identify most appropriate answer to the questions given at the end of each passage.Any company can generate simple descriptive statistics about aspects of its business-average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greater profit potential and the ones mo.st likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimise their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back to Capital One.Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from·one killer app, but rather from multiple applications supporting many parts of the business and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused: Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, can accurately predict customer defections by examining usage patterns and complaints.When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric,such as "information-based strategy" at Capital One or "information based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share. data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uberanalytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and' marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed: As a result of this consolidation, P&G; can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence. The group at P&G; also raises the visibility of analytical and data-based decision making within the company. Previously, P&G;'s crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P &. G tells to investors, the press and the public.A company wide embrace of analytics impels changes in culture, processes, behavior, and skills for·many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO.·Indeed we found several chief executives who have driven the shift to analytics at their companies over the past few years, including LovC1!18D of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Balcery Group, former CEO Barry Beracba kept a sign on his desk that summed up bispersoll41 and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were makii\g some progress. But we found at these lower-level people lacked the clout, perspective, and the cross-functional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation and a familiarity with the subject.A background in statistics isn't necessary, but those leaders must understand the theory belted various quantitative methods so that they recognize those methods limitations-which factors are being weighed and which ones aren't When the CEOs need help grasping quantitative techniques, they rum to experts who understand the business and bow analytics can be applied to it We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not . to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practice. Of course, no.t all decisions should be grounded in analytics - at least not wboUy so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.Q. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?a)Analytics competitors have a centralised, multi functional approach to data management and encourage data sharing whereas traditional companies have a departmental, multiple databases approach.b)Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MITs but Analytics competitors hire the best analytical minds.c)Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics.d)Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics.Correct answer is option 'C'. Can you explain this answer? tests, examples and also practice CAT tests.
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