Report explores key challenges facing users of diverse data and describes innovative practices from successful organizations
TDWI Research has released its newest TDWI Best Practices Report: Harnessing the Power of Diverse Data for Business Growth. This original, survey-based report focuses on the factors driving today’s organizations to use diverse data and how they can successfully manage, analyze, and govern it.
The report’s author, TDWI’s vice president and senior research director for advanced analytics, Fern Halper, explains that those companies analyzing and using diverse data tend to be more successful with analytics. However, enterprises are struggling to unify diverse data for analysis, govern the data, and manage complex pipelines.
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In the report, Halper points out that “respondents cite numerous opportunities for diverse data, [including] having more accurate analytics for better customer insights, improving operational efficiencies, creating a more data-driven culture, and improving innovation and collaboration.” The report explains the most common challenges enterprises are facing in managing and analyzing diverse data, and also explores the value of overcoming those challenges.
The report discusses the most common types of diverse data that enterprises are collecting and working with and the implications of new technologies for data analysis, including generative AI.
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Report Highlights
Among this comprehensive report’s key findings:
- Most respondents are collecting structured data; text data is already mainstream while other unstructured data types are only beginning to enter mainstream adoption
- One-third (30%) of respondents are using data marketplaces to source diverse data; these respondents are more likely to monetize their data
- More than half of respondents (59%) are struggling with data quality issues; 42% said they lack necessary skills for analyzing diverse data
- Forty-eight percent of respondents agreed that generative AI might be a game-changer for analyzing unstructured data
The report concludes with best practice recommendations for ensuring success in using new data types.
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