The AI boom hasn’t generated a prodigious number of real-world successes just yet, but it has succeeded in doing one thing: casting fresh light on the critical importance of high quality data and solid data management practices. Three recent studies released today provide more grist for that mill.
First up is NetApp and its second annual Data Complexity Report, which you can download here. The data storage provider 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, and came away with some interesting conclusions.
For instance, NetApp’s survey 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 in a press release.
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. But that doesn’t mean they’ll be resting on their laurels, as 40% of executives report that “unprecedented investment in AI and data management will be required for their companies in 2025.”
Graphic courtesy of NetApp “Data Complexity Report”
“As organizations accelerate their adoption of AI, the complexity of managing data has emerged as both a challenge and an opportunity,” said Steve McDowell, chief analyst and founder at NAND Research, which conducted the survey on behalf of NetApp. “NetApp’s 2024 Data Complexity Report underscores a critical shift: Businesses that embrace intelligent data infrastructure and prioritize security are not only future-proofing but also gaining a competitive edge in the AI era.”
The next report comes courtesy of Qlik, the data management and analytics provider. The company tapped 3Gem to survey 4,200 senior decision makers and large organizations around the world to determine their preparedness for AI.
Qlik’s survey identifies 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 (both 23%) followed by deploying AI after development (22%), and budget and a lack of trusted data (21% each).
Trust is another major challenge that needs to be overcome before organizations achieve widespread AI success. 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.
“Business leaders know the value of AI, but they face a multitude of barriers that prevent them from moving from proof of concept to value creating deployment of the technology,” James Fisher, Qlik’s chief strategy officer, says in a press release. “The first step to creating an AI strategy is to identify a clear use case, with defined goals and measures of success, and use this to identify the skills, resources and data needed to support it at scale. In doing so you start to build trust and win management buy-in to help you succeed.”
The third piece of data on AI comes out of Ataccama, the data management software firm out of Toronto, Ontario with a name that evokes the Chilean desert. The company tapped Hanover Research to help it survey 300 executives in the US, Canada, and the UK for a report on the state of their data and AI initiatives.
The results, which you can read in the Ataccama Data Trust Report, show that data management is a top issue for would-be AI practitioners (which is a theme we see again and again).
“Trustworthy AI relies on clean, quality data, so it’s unsurprising that heads of data cite improving data quality and accuracy (51%) to be an immediate priority, and also report that managing large volumes of data (30%) is among the top challenges CDOs face in their organizations today,” the company says in its press release.
Ataccama noted some industry differences when it comes to the importance of data quality, which was deemed a “top data management priority” for 51% of all respondents. However, 68% of data decision-makers in the insurance business cited data quality as a top priority. Healthcare organizations cited their struggles with integrating legacy systems as a top challenge.
“Do not ignore the critical role your data plays in delivering on the promise of AI,” Ataccama CEO Mike McKee says in a press release. “Businesses that don’t leverage AI with data they can trust will fail. The winners have already established data trust to support AI-powered initiatives to improve customer experience, product innovation, and sales and marketing performance.”
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. If nothing else, the current AI boom has showed us this.
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