The challenge isn’t lack of data or dexterity with advanced analytics. It’s that many leadership teams are still struggling to build and maintain...
The challenge isn’t lack of data or dexterity with advanced analytics. It’s that many leadership teams are still struggling to build and maintain robust yet flexible data management foundations—which is significantly impeding efforts to unlock value from emerging data and AI initiatives.
Across industries and in companies of all sizes, investment in big data and AI initiatives is surging, fueled by a desire to drive innovation and deliver more compelling customer and partner experiences. IT and business leaders see powerful potential in the combination of data and AI to drive operational efficiencies, lower costs, and create a foundation for new business models. The 2021 NewVantage Partners Big Data and AI Exec survey found investment in big data and AI to be ubiquitous, cited by 99% of respondents, while 92% of respondents said the pace of investment is accelerating.
Despite the focus and investment, however, many organizations still struggle to wring more value out of their data, AI, and analytics initiatives. Leadership teams may find themselves at a crossroads, grappling with how to align data strategies to core business objectives, spin insights into action, and shift organizational culture. The 2021 NewVantage Partners survey illustrates several lagging efforts to create sustainable business advantage using data:
What’s driving the disconnect between AI and data investments and tangible results? Like many transformation imperatives, it comes down to a confluence of factors, from the sheer complexity of the data landscape and IT infrastructure to the reluctance of organizations to methodically embrace change.
“Compared to infrastructure or applications, data is a more complex environment to manage because it’s constantly changing,” explains Naveen Kamat, Executive Director, Data and AI Services at Kyndryl. “It’s not like deploying a monitoring or performance management tool and then you’re done. Data is more dynamic and complex, and requires an end-to-end view as well as management on an ongoing basis.”