Last updated on 2024年8月1日

What are the challenges and opportunities of using secondary data in the era of big data and AI?

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Secondary data is any data that is collected by someone else for a different purpose than your own. It can be a valuable source of information for researchers, especially in the era of big data and AI, where massive amounts of data are generated and analyzed by various platforms and tools. However, using secondary data also poses some challenges and opportunities that you need to be aware of and address in your research process. In this article, we will discuss some of the main issues and benefits of using secondary data, and how you can overcome the limitations and leverage the advantages of this type of data.

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Pratik Shirsath

| Research Associate | Qualitative & Quantitative Research | Competitive & Strategic Analysis…
Jyotika Rohilla

change is the only constant.
Ankesh Saxena

32x Top Voices | Podcaster @ The Collaborative Canvas | Do you also hear what people say? | Learn and Grow | Market…

1 Data quality and reliability

One of the most important challenges of using secondary data is ensuring its quality and reliability. Quality refers to the accuracy, completeness, consistency, and relevance of the data, while reliability refers to the extent to which the data can be trusted and replicated. You need to assess the source, methodology, and context of the data collection, and check for any errors, biases, or gaps in the data. You also need to verify the validity and reliability of the data analysis, and how it aligns with your research objectives and questions. To ensure data quality and reliability, you should use multiple sources of secondary data, compare and contrast the results, and triangulate the findings with primary data or other evidence.

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Ankesh Saxena

32x Top Voices | Podcaster @ The Collaborative Canvas | Do you also hear what people say? | Learn and Grow | Market Research | Personal Branding | Consumer Insights | Sales | ESOMAR member | MRSI Member |
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Challenges of using secondary data in the era of big data and AI include ensuring data quality, relevance, and reliability amidst vast volumes of information. Additionally, privacy concerns and legal compliance pose significant challenges. However, leveraging secondary data offers opportunities for cost-effective research, trend analysis, and hypothesis testing. AI algorithms can enhance data analysis capabilities, uncovering valuable insights from complex datasets. Proper data governance and ethical considerations are essential for maximizing the benefits of secondary data in this context.

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Jyotika Rohilla

change is the only constant.
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Advances in AI and machine learning can really help with cleaning up, standardizing, and validating data. Automated tools can quickly spot and fix errors, making secondary data more reliable and easier to use.

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Pratik Shirsath

| Research Associate | Qualitative & Quantitative Research | Competitive & Strategic Analysis | Industry Benchmarking
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Secondary data comes from various sources with the vast amount and in the unstructured from which eventually poses the challenge of reliability, consent, privacy and some times inherent biases associated with it

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Vaibhav Mahajan

Program Director @ Capgemini | Leading AI-driven Industry Development
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Challenges: (1) GenAI has a tendency to hallucinate - e.g., even if it cannot access appropriate secondary data, it can still give answers based on perceptions/opinions, which can be biased. (2) Existing weak AI models doesn't seems to have any strong reasoning ability to validate data sources. (3) There seems to be a lack of technical talent (big data and AI) when it comes to analyzing secondary data. Opportunities: (1) Cos. in retail, telecoms and utilities sitting on huge amounts of user data, can leverage it to generate actionable insights across verticals. (2) IoT devices are providing lots of data that was earlier difficult to capture, and which can be leveraged by cos. to design and develop right products.

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2 Data accessibility and availability

Another challenge of using secondary data is finding and accessing the data that you need. Depending on the type, format, and location of the data, you may face some barriers or restrictions to access or use the data. For example, some data may be proprietary, confidential, or protected by copyright or ethical regulations, and you may need to obtain permission or pay a fee to use it. Some data may be outdated, incomplete, or incompatible with your research design or tools, and you may need to update, supplement, or transform the data. Some data may be difficult to find, locate, or retrieve, and you may need to use specialized databases, search engines, or platforms to access it. To overcome these challenges, you should plan ahead, identify your data needs and sources, and follow the legal and ethical guidelines for using secondary data.

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Pratik Shirsath

| Research Associate | Qualitative & Quantitative Research | Competitive & Strategic Analysis | Industry Benchmarking
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In the age of big data and AI accessibility to large amount of information has became easy but at the same time it poses challenges of sophisticated data management and effective integration methods in order to make strategic decisions

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3 Data analysis and interpretation

One of the main opportunities of using secondary data is the ability to apply advanced data analysis and interpretation techniques, such as big data analytics and AI, to extract valuable insights and patterns from the data. These techniques can help you to explore, visualize, and model the data, and to generate new hypotheses, questions, or solutions for your research problem. They can also help you to enhance, enrich, or augment the data with other sources or methods, and to create new forms of data or knowledge. However, these techniques also require some skills and expertise, and you need to be careful about the assumptions, limitations, and implications of the data analysis and interpretation. You should use appropriate tools and methods for your research purpose and context, and validate and verify the results with other sources or approaches.

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Pratik Shirsath

| Research Associate | Qualitative & Quantitative Research | Competitive & Strategic Analysis | Industry Benchmarking
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While there are several challenges but the integration of AI algorithm with secondary data can create impactful analysis, which can be really helpful to forecast trends and market dynamics with enhanced accuracy

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Jyotika Rohilla

change is the only constant.
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Big data tech and AI can manage huge amounts of data really fast. With real-time processing and advanced analytics, companies can quickly get insights from these large datasets.

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4 Data reuse and sharing

Another opportunity of using secondary data is the potential to reuse and share the data for different purposes or audiences, and to contribute to the advancement of knowledge and innovation. Reusing and sharing secondary data can help you to save time, money, and resources, and to increase the efficiency, effectiveness, and impact of your research. It can also help you to collaborate with other researchers, stakeholders, or communities, and to create new networks, partnerships, or platforms for data exchange and dissemination. However, reusing and sharing secondary data also involves some responsibilities and challenges, and you need to respect the rights, interests, and expectations of the data owners, providers, and users. You should follow the best practices and standards for data management, documentation, citation, and preservation, and ensure the quality, security, and integrity of the data.

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Shina Sehgal, CAMS, CRCMP

Assistant Manager at Forvis Mazars | CAMS certified | Ex-EY | Ex-KPMG
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To respect the rights and interests of data owners and users in secondary research, obtain consent, anonymize data, ensure security, maintain transparency, respect intellectual property, and follow ethical guidelines. Best practices include proper documentation, correct data citation, secure data preservation, quality control, adherence to standards, clear licensing, stakeholder engagement, and ethical considerations. These measures enhance data reusability, ensure compliance, and protect the integrity and privacy of the original data, fostering collaboration and advancing knowledge effectively.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Jyotika Rohilla

change is the only constant.
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With the vast amount of secondary data available, ensuring that this data is used ethically and in compliance with privacy regulations is paramount. Furthermore, the integration of secondary data with primary data sources can provide richer insights and more comprehensive analyses. This hybrid approach can help overcome the limitations of each data type individually, leading to more accurate and actionable results in research and business applications.

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What are the challenges and opportunities of using secondary data in the era of big data and AI?

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1 Data quality and reliability

2 Data accessibility and availability

3 Data analysis and interpretation

4 Data reuse and sharing

5 Here’s what else to consider

Secondary Research

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