Digital transformation programs are being hampered by slow data modernization efforts, new research suggests, with only 10% of IT leaders describing their data stacks as “modern.”
Alteryx analysis showed that many IT leaders fear being left behind in long-term AI modernization and adoption due to their inability to improve data quality.
More than half (54%) of respondents said they rated their data maturity levels as “good” or “advanced.” Similarly, 76% described trusting their data; However, despite this, a fifth of respondents highlighted current challenges.
Nearly a quarter (22%) of respondents noted data bias issues, while 20% cited data quality issues within their organizations, with both issues thwarting the ability to use generative AI systems.
The study found that a degree of inflexibility on the part of IT teams was a major cause of impeding modernized data stacks and innovative practices.
Although IT teams have responsibility in terms of where they spend their budget, 54% of respondents stated that if “other priorities, projects or spending needs” arise after budgets are allocated, then there is little room for adjustments.
Given the speed at which AI is changing as a technology, this leaves little to no room for the “agility” needed to adapt, the study suggested.
IT leaders step up digital transformation and technology stack improvements
Many of the IT leaders surveyed expressed a desire to accelerate improvements in their technology stacks, with 47% describing themselves as “actively working” to modernize systems and improve data outcomes.
23% of respondents reported that “better data quality” was the top desired outcome of investing in new technology.
IT leaders' push to determine the structure of their stacks came in several forms: IT infrastructure, data sources, and technical expertise took the top three spots, ahead of business outcomes, which came in fifth. place.
“Now that generative AI is reaching the peak of the hype cycle, business leaders and IT teams across the UK need to realize that one clear differentiator can make or break a business: data,” said Jason Janicke, Senior Vice President EMEA at Alteryx.
Data modernization requires a cultural change within companies
According to Alteryx, another notable barrier to preventing the successful launch of generative AI was poor data culture.
The research noted that fundamental mismanagement of data teams is a key obstacle. However, for many companies the problems run deeper. Nearly half (41%) of respondents said their organization lacked a centralized data or analytics function.
Without this, an organization cannot effectively maintain data as a shared resource for the broader business, but instead creates an environment where individual departments focus solely on their own data.
48% of respondents reported instances of these environments within their organizations, known as “data silos,” noting that this has impeded organizational alignment on data strategies.
There was also a considerable lack of consensus about the position of the data owner within a particular organization, with respondents suggesting several different locations for the role.
22% of respondents cited the data owner as the chief data officer, 11% as the board of directors, and 8% as senior executives of a company, making the process of accessing or managing data be much less clear.
“To succeed in this era of automated data-driven intelligence, modern data stacks and a data-skilled workforce must be combined to make the most of available data, compute and automation resources,” Janicke said.
