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FAQs on PPT - Organisation of Data - Economics Class 11 - Commerce

1. What is data commerce?
Ans. Data commerce refers to the buying and selling of data as a commodity. It involves the exchange of data between companies, where one party provides data and the other party pays for it. This practice has gained popularity with the rise of big data and the increasing value placed on data-driven insights.
2. How is data organized in data commerce?
Ans. In data commerce, data is organized in a structured manner to ensure its usability and accessibility. This typically involves categorizing and labeling data based on relevant attributes. Common methods of organizing data in data commerce include using databases, data warehouses, and data lakes, where data is stored and indexed for easy retrieval and analysis.
3. What are the benefits of data commerce for businesses?
Ans. Data commerce offers several benefits for businesses. Firstly, it allows companies to monetize their data assets by selling data to other organizations. This can be a new revenue stream for businesses. Secondly, data commerce enables companies to access valuable data from external sources, which can enhance their analytics capabilities and lead to better decision-making. Lastly, data commerce can foster collaboration and partnerships between companies, leading to innovation and new business opportunities.
4. How does data commerce impact data privacy and security?
Ans. Data commerce raises concerns about data privacy and security. When data is bought and sold, there is a risk of unauthorized access, data breaches, and misuse of personal information. To address these concerns, companies engaged in data commerce must prioritize data protection measures, such as implementing strong security protocols, complying with data privacy regulations, and obtaining consent from individuals whose data is being traded.
5. What are some challenges in the organization of data commerce?
Ans. The organization of data commerce can present various challenges. One key challenge is ensuring data quality and accuracy, as unreliable or outdated data can lead to poor decision-making. Another challenge is managing the sheer volume of data, as data commerce often involves large datasets that need to be processed and stored efficiently. Additionally, data standardization and interoperability can be a challenge when dealing with data from different sources, formats, and systems.
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