AI and the need for data readiness
Why every business publication can’t stop talking about AI
Business publications today are filled with discussions about the transformative potential of AI—which isn’t surprising—given AI’s promise to greatly improve productivity, drive innovation, and open up new revenue streams.
Companies across industries are keenly aware that AI can extract value from their vast data reserves, turning raw information into actionable insights—prompting executives to prioritize AI as a key strategic initiative.
A 2023 survey by McKinsey shows that 50% of companies have already adopted AI in at least one business function, up from 20% in 2017. Publications constantly highlight these developments, encouraging businesses to explore how AI can tap into hidden opportunities within their data assets.
Understanding why AI-ready data isn’t optional
Excitement surrounding AI is clear, but there’s a key element that organizations must address before reaping the benefits: data readiness.
AI algorithms require high-quality, structured, and well-governed data to function properly.
Without this foundation, even the most advanced AI tools will struggle to deliver meaningful results. Organizations are increasingly aware of this requirement, recognizing that preparing their data for AI is a strategic must-have.
According to Gartner, poor data quality costs organizations an average of $12.9 million annually. A lack of AI-ready data can lead to faulty insights, misguided decisions, and missed opportunities, pointing to the importance of investing in comprehensive data governance and management practices.
The challenges of implementing effective data governance
The unexciting, but essential, work that drives AI success
Implementing effective data governance is key, but typically lacks the excitement associated with AI itself. This foundational work includes setting up data quality standards, guaranteeing compliance with regulations, and managing data lifecycle processes.
These tasks may not grab headlines, but they are key to the success of any AI initiative. Without them, AI projects are at risk of failure due to inaccurate or incomplete data.
As organizations scale their AI efforts, the complexity of managing data grows exponentially.
Leaders must tackle these challenges head on if they’re to make sure their AI strategies are built on a solid data foundation. It’s unglamorous but necessary work, much like the routine maintenance that keeps a high-performance engine running smoothly.
How data and analytics leaders are impacting businesses
Initially, data and analytics (D&A) leaders were primarily focused on protecting data and guaranteeing its integrity. As organizations began to understand the potential of data as a strategic asset, the focus shifted.
Today, D&A leaders are expected to drive business value through data, doing more than simply safeguard it—drawing attention to the growing prevalence of Chief Data Officers (CDOs) and Chief Data and Analytics Officers (CDAOs).
Approximately 85% of large organizations now have a senior D&A leader, a large increase from a decade ago. These leaders are tasked with aligning data strategies with business goals, developing a data-driven culture, and identifying new opportunities for growth.
Why proving the value of data and analytics feels impossible
One of the most challenging aspects of leading a D&A function is justifying its budget. Unlike revenue-generating departments, D&A is often seen as a support function, making it difficult to quantify its direct impact on the bottom line.
This is further compounded by the fact that data and analytics contribute to a wide range of business outcomes, from improving customer satisfaction to optimizing supply chains.
Isolating the value of D&A from these broader initiatives is nearly impossible, which can make budget discussions frustrating for D&A leaders—who must continually showcase the value of their work, often in qualitative terms, to secure the resources they need to succeed.
Gartner’s research on the impact of D&A practices
How mature data practices can boost your bottom line by 30%
Organizations with well-developed data governance and analytics capabilities can expect to see up to a 30% improvement in key financial performance metrics—attributed to more accurate decision-making, better risk management, and the ability to identify and capitalize on new opportunities more effectively than competitors.
These findings clearly point out the importance of developing and maintaining comprehensive D&A practices, as they have a direct and measurable impact on the organization’s financial health. Companies that neglect this area risk falling behind in an increasingly data-driven world.
How a narrow focus on ROI can sink your data strategy
One of the more striking findings from Gartner’s research is the concept of “death by ROI.” While ROI models are often used to justify investments, Gartner found that applying these models too narrowly in the context of D&A can actually harm firm performance.
“Death by ROI” typically happens because focusing exclusively on the ROI of individual D&A projects can lead organizations to overlook investments with broader, long-term benefits.
Just as focusing only on the short-term gain from a single investment can be detrimental to an overall portfolio, applying a narrow ROI lens to D&A can prevent organizations from seeing the bigger picture—resulting in missed opportunities and suboptimal decisions that ultimately stifles performance.
Gartner’s recommendations for avoiding the “Death by ROI” trap:
Why blurring functional boundaries unlocks business value
Gartner’s research suggests that one of the most effective ways to avoid the “death by ROI” trap is to blur the lines between functional areas. Rather than rigidly defining the value created by each department, successful organizations encourage collaboration across functions—allowing them to tap into the collective expertise and resources of the entire organization, leading to more innovative and effective solutions.
For example, Brita Andercheck, the Chief Data Officer for the City of Dallas, successfully integrated her D&A team with different city departments. As a result, Andercheck’s team was able to identify creative ways to use data to achieve strategic goals.
How to align ROI with the goals that matter most
Rather than focusing on the ROI of individual projects, leaders should consider how D&A investments contribute to achieving core business objectives.
Marcelo Zottolo, a senior executive at Lee Health, advocated for this approach. When faced with nearly $7 million in regulatory fines, Zottolo shifted his D&A team’s focus to help department leaders meet health outcome benchmarks.
Through aligning the D&A function’s efforts with the hospital’s strategic goals, Zottolo’s team reduced fines to less than $1 million, and clearly showcased the value of linking D&A initiatives to organizational priorities, for all to see.
Rethinking how you assess value to focus on the big picture
Effective leaders understand that value assessment should focus on the organization as a whole, rather than on isolated functions. Leaders can then see the broader impact of their investments and make more informed decisions.
Through carefully shifting the framework of value assessment to consider the interconnectedness of different functions, leaders are better able to understand how each part of the organization contributes to overall success. This ultimately helps in justifying investments and makes sure resources are allocated in a way that maximizes the organization’s long-term potential.
Final thoughts
As you reflect on your data and analytics strategy, ask yourself: Are you focusing too narrowly on ROI, potentially stifling broader business growth?
Carefully consider how blurring the lines between functions and aligning your data initiatives with overarching goals could unlock untapped potential. Are you ready to shift your focus to the bigger picture and truly leverage the real-world value of your data?
