The AI Boom and the Importance of Data Management
The AI boom has cast fresh light on the critical importance of high-quality data and solid data management practices. Three recent studies provide more grist for that mill.
NetApp’s Data Complexity Report
NetApp’s second annual Data Complexity Report surveyed 1,300 tech and data executives at organizations around the world to gauge the state of their data estates and their preparedness for AI. The report found that organizations with higher investment in data unification say they’re better prepared to reach their AI goals.
Nearly 80% of executives surveyed “recognize the importance of unifying data to achieve optimal AI outcomes,” NetApp says. The report also found that two-thirds of companies worldwide say their data is “either fully or mostly optimized for AI–meaning their data is accessible, accurate, and well-documented for AI-use cases,” NetApp says.
Qlik’s Survey on AI Success
Qlik’s survey identified several reasons for the lack of AI progress and success, with the lack of AI skills and data governance challenges being identified by survey-takers as the number one challenge.
Qlik says 37% of senior managers lack trust in AI, 42% feel that less senior employees don’t trust it, and 21% say their customers don’t trust it. Three out of five (61%) say this lack of trust is reducing AI investments in their businesses.
Ataccama’s Data Trust Report
Ataccama’s Data Trust Report surveyed 300 executives in the US, Canada, and the UK for a report on the state of their data and AI initiatives. The results show that data management is a top issue for would-be AI practitioners.
51% of respondents cited improving data quality and accuracy as an immediate priority, and 30% reported that managing large volumes of data is among the top challenges CDOs face in their organizations today.
Conclusion
Having a well-designed data management system that yields high-quality, trusted data clearly is important for succeeding with AI. There are obviously other challenges too, related to skills, deployment, trust, and budget, among others. But since AI essentially is a distillation of data, there is not a clear path to succeed with AI when you’re starting with bad data.
FAQs
Q: Why is data management important for AI success?
A: Having a well-designed data management system that yields high-quality, trusted data is critical for succeeding with AI. Bad data can lead to inaccurate results and a lack of trust in AI outcomes.
Q: What are the top challenges to AI success?
A: The top challenges to AI success identified in the studies include a lack of AI skills, data governance challenges, and a lack of trust in AI. Additionally, budget and a lack of trusted data can also be barriers to success.
Q: How can organizations overcome these challenges?
A: Organizations can overcome these challenges by prioritizing data quality and accuracy, investing in AI skills and training, and implementing effective data governance practices. Additionally, building trust in AI by providing clear use cases and measurable outcomes can also be key to success.
