Research by Alteryx finds that data silos and quality pose (surmountable) challenges as IT teams around the globe prepare their data stacks for generative AI
KSA – 16 April 2024 – New research launched today by Alteryx, the AI platform for enterprise analytics, reveals that organisations aren’t ready to unlock value from generative AI due to their data. The ‘Data Stack Evolution: Legacy Challenges and AI Opportunities’ report, which surveyed 3,100 global IT leaders, found consistent barriers preventing successful rollouts of generative AI including the management of data stacks, tech strategy and business cultures.
Lack of confidence across data stacks
The research highlights a key disconnect between global IT leaders’ confidence in their data and the reality of their data stacks. Although 54% rated their data maturity as “good” or “advanced”, and 76% have trust in their data, a fifth highlighted ongoing challenges, including data bias (22%) and data quality (20%).
These underlying issues suggest that the foundations of current data stacks are not up to scratch if they want to roll out generative AI successfully. Indeed, only 10% say they have a “modern” data stack. This could be due to the drivers for determining the structure of the data stack, with IT infrastructure, data sources and technical expertise identified as the top three, outweighing business outcomes which came in fifth.
Inflexible tech strategies
That’s not to say that businesses aren’t striving to do better. Almost half (47%) say they are actively working to modernise their systems to improve data outcomes. Encouragingly, improved data quality (23%) emerged as the leading desired outcome from new technology investment amid the potential data-related challenges hindering AI adoption.
However, while global IT leaders recognise the importance of investing in new technology, inflexibility could be hindering innovation. IT teams have responsibility for where they spend their budget, but 54% state that if other priorities, projects, or spending needs arise after budgets are allocated, they cannot be adjusted. This leaves little room for the agility needed for impactful innovation given how fast AI is evolving.
Data culture isn’t embedded
The research also highlights a barrier to innovation is how data teams are managed and organised. Almost half (41%) of global IT leaders say they don’t have a centralised data or analytics function that maintains data as a shared resource for the business; instead, individual departments are responsible for managing their own data. As a result, 48% report data siloes where data is kept within the department that generates it.
This is unsurprising given that the research found a lack of consensus about where the job of the data owner sits with an organisation. Respondents cite several different roles including the Chief Data Officer (22%), board of directors (11%) and senior executives (8%), which is concerning since data access and management are requirements for successful generative AI implementation.
“With generative AI now reaching the peak of the hype cycle, business leaders and IT teams across Saudi Arabia must realise that one clear differentiating element can make or break a business: data”, said Karl Crowther, VP of MEA at Alteryx. “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. The modern data stack must accelerate the data journey while empowering everyone to solve business challenges and deliver decision intelligence, not just the tech workers. The best measure of success of such a stack is the number of teams it empowers to make data-driven decisions faster and more effectively, ahead of the competition.”
To learn more about the Data Stack Evolution: Legacy Challenges and AI Innovation, download the full report from Alteryx.
