Gartner Identifies the Top Trends in Data and Analytics for 2024
“The power of AI, and the increasing
importance of GenAI are changing the
way people work, teams collaborate, and processes operate,” said Ramke Ramakrishnan, VP Analyst at Gartner.
“Amidst this technological revolution, organizations that fail to make the
transition and effectively leverage D&A, in general, and AI, in particular,
will not be successful.”
Gartner analysts presented the top
D&A trends that IT leaders must navigate and incorporate into their D&A
strategy at the Gartner Data &
Analytics Summit, taking place in Mumbai through today.
Trend 1: Betting the Business
As AI continues to revolutionize
industries on a strategic level, D&A leaders must demonstrate a bet-the-business skill set on AI and earn trust to lead the AI
strategy within the enterprise.
“D&A leaders
must demonstrate their value to the organization by linking the capabilities
they are developing and the work they do to achieve the business outcomes
required by the organization,” said Ramakrishnan. “If this is not done, issues
such as misallocation of resources and underutilized investments will continue
to escalate, and D&A will not be entrusted with leading the AI strategy within
the organization.”
With AI changing the way businesses
are run, enterprises are heading towards a cost avalanche. D&A leaders must
act by implementing a FinOps practice to establish and enforce standards and
decrease expenses.
Gartner predicts by 2026, chief data and
analytics officers (CDAOs) that become trusted
advisors to, and partners with, the CFO in delivering business value will have
elevated D&A to a strategic growth driver for the organization.
Trend 2: Managed Complexity
Many D&A systems are delicate,
and their redundancies can cause chaos and added costs. “Leading organizations
are working to turn this chaos into something they can manage - complexity.
Complexity is, by definition, not an easy place to be, but acknowledging it
gives a realistic understanding of the dynamic environment and helps the
D&A teams in taking appropriate actions,” said Ramakrishnan.
D&A
leaders need to embrace complexity by using AI-enabled tools to automate and
improve productivity. This includes investing in augmented data management,
decision automation, and analytics capabilities like natural language
processing (NLP). Gartner predicts, CDAOs will have adopted data fabric as a
driving factor in successfully addressing data management complexity, thereby
enabling them to focus on value-adding digital business priorities by 2025.
Trend 3: Be Trusted
With the
increasing accessibility and efficiency of GenAI, there is a challenge in
navigating a world where data reliability is constantly questioned. Lack of
trust within organizations, concerns about the value and quality of data, and regulations around AI are leading to a deluge of distrust.
“If data is not trusted, it may not
be used correctly to make decisions,” said Ramakrishnan.
“D&A leaders should use decision
intelligence practices to build trust in data and monitor decision-making
processes and outcomes. Additionally, implementing effective AI governance
and responsible AI practices is
crucial in establishing trust among stakeholders. It includes making data AI-ready which means
it is ethically governed,
secure and free from bias and is enriched to ensure more accurate responses.”
Trend 4: Empowered Workforce
“It is important that employees feel
empowered through the use of AI in D&A, rather than causing them to feel
threatened or frustrated by it,” said Ramakrishnan.
Organizations must invest in
developing AI literacy among
employees, use adaptive governance practices for effective governance, and
implement a trust-based approach to managing information assets, helping
individuals understand the provenance of information used by them.
“AI training is not just about
quantity; it also requires a different approach. Recognize that the skill sets
required for expert AI users will be very different from other users,” said
Ramakrishnan. “Gartner predicts, by 2027, more than half of CDAOs will secure
funding for data literacy and AI literacy programs, fueled by enterprise
failure to realize expected value from generative AI.”
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