Digital transformation programs are being stunted by sluggish data modernization efforts, new research suggests, with only 10% of IT leaders describing their data stacks as “modern”.
Analysis from Alteryx showed that many IT leaders fear they will fall behind with modernization and long-term AI adoption due to their inability to improve data quality.
More than half (54%) of the survey respondents said they rated their data maturity levels as “good” or “advanced”. Similarly, 76% described themselves as having trust in their data - yet despite this, one-fifth of respondents highlighted ongoing challenges.
Nearly one-quarter (22%) of respondents pointed towards issues of data bias while 20% cited issues of data quality within their organizations, with both of these issues frustrating the ability to use generative AI systems.
The study found that a degree of inflexibility on behalf of IT teams was an important root cause in preventing the existence of modernized data stacks and innovative practices.
Though IT teams have the responsibility in terms of where they spend their budget, 54% of those surveyed stated that if “other priorities, projects, or spending needs” arise after budgets are allocated, then there is little scope to adjust.
Given the speed at which AI is changing as a technology, this leaves little to no space for the “agility” required to adapt, the study suggested.
IT leaders ramp up digital transformation and tech stack improvements
Many of the IT leaders surveyed expressed their desire to accelerate improvements to their tech stacks, with 47% describing themselves as “actively working” to modernize systems and improve data outcomes.
“Improved data quality” was reported by 23% of respondents as the leading desired outcome of new technology investment.
The drive for IT leaders to determine the structure of their stacks came in various forms, with IT infrastructure, data sources, and technical expertise ranking top three ahead of business outcomes, which came in fifth.
“With generative AI now reaching the peak of the hype cycle, business leaders and IT teams across the UK must realize that one clear differentiating element can make or break a business: data,” said Jason Janicke, SVP EMEA at Alteryx.
Data modernization requires a cultural change within businesses
Another notable barrier to preventing the successful rollout of generative AI was poor data culture, according to Alteryx.
The research pointed toward fundamental mismanagement of data teams as a key stumbling block. For many enterprises, however, the issues run deeper. Nearly half (41%) of respondents said their organization lacked a centralized data or analytics function.
Without this, an organization is unable to effectively maintain data as a shared resource for the business at large, instead creating an environment where individual departments focus only on their own data.
48% of respondents reported instances of these environments within their organizations, known as “data silos”, noting that this has stunted organizational alignment on data strategies.
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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 function.
22% of respondents cited the data owner as the Chief Data officer, 11% pointed to the board of directors, and 8% pointed to the senior executives within a company, making the process of data access or management much less clear.
“To succeed in this era of automated data-driven intelligence, modern data stacks and a data-skilled workforce should be brought together to take full advantage of available data, compute and automation resources,” Janicke said.
