The Strategic Implication: AI Readiness Versus Appetite
While the appetite for integrating AI into business processes remains high among C-suite executives, the readiness to deploy these technologies is hampered by significant data debt.
This phenomenon, analogous to the better-known concept of technical debt in software development, represents the cumulative burden of maintaining and rectifying inconsistent and often erroneous data accumulated over years of digital evolution. The past decade’s rush to adopt cutting-edge technologies has resulted in a landscape where data is abundant but often "dirty" — corrupt, poorly structured, and inconsistently taxonomized. This state of affairs severely limits the ability of companies to harness the full potential of their digital investments, particularly in leveraging AI for predictive analytics and automated decision-making.
This gap—between appetite and readiness—can lead to misguided endeavors where companies ambitiously invest in AI without addressing foundational data issues, resulting in underwhelming outcomes and wasted resources. The readiness to deploy AI effectively requires an investment in data management practices, where data quality, accessibility, and integrity are prioritized.
Companies need to adopt a phased approach towards AI deployment, beginning with an audit of existing data landscapes, identifying critical data debt issues, and implementing remediation strategies that align with their AI objectives. This approach ensures that AI initiatives are built on a solid data foundation, capable of supporting sophisticated algorithms and producing reliable insights. Furthermore, executive leadership must champion a shift in organizational culture to embrace “data quality” and to promote an understanding across all levels that data quality directly influences AI efficacy and, by extension, business success.
Investing in Data Readiness Ahead of AI Investments
During a recent IT forum, I was struck that it took nearly 50 minutes before the topic of AI was broached, which in today’s technology setting is remarkable in and of itself. The bulk of the discussion instead focused on how IT organizations are struggling with dirty data - specifically inconsistent Master Data Management (MDM). Several IT leaders cited that when teams create data upstream, they seldom (if ever) consider how the data will be consumed and contextualized downstream by other constituents.
This topic was explored in our recent Veeva Consumer Products EU Summit by Alberto Prado, Vice President, Head of Digital R&D at Unilever. “We have implemented a data governance model as well that helps us with the mindset, because up until recently, you know, there was not truly a governance model in the sense of who owns this data,” Prado said. “Who is responsible for ensuring the quality of this data, who is responsible for extracting value? And this is where things get complicated, because in large organizations, the team that is responsible for generating and protecting the quality of the data may not be the ones that actually take advantage of it.”
Organizational teams often handle data creation with a narrow focus on the immediate task at hand, overlooking how this data could be utilized and leveraged downstream. Imagine a construction crew tasked with laying the foundation for a building. They pour the concrete, ensuring it’s level and structurally sound—but they don’t consider the exact specifications needed by the teams who will come later to install plumbing, electrical wiring, or even the final architectural finishes. If the foundation is misaligned, too shallow, or doesn’t account for future needs, it will complicate and delay the entire construction process.
Similarly, in a business context, when data is created without considering its downstream uses, it can result in significant complications later, such as the introduction of errors or inefficiencies, much like a building project that requires costly retrofitting. This is how data debt is created and presents a structural impediment to advancement.
So, it's not surprising that IT leaders are surfacing this issue, as they traverse horizontally across the organization. They’re not siloed, unlike the divisions of the organization that they support.
Executive Imperative to Address Data Debt
The path forward for companies mired in data debt involves four key strategies:
2. Investing in Data Quality: Prioritizing investments in technologies and processes that enhance data quality, including advanced data cleansing tools and employing specialists focused on data quality management.
3. Cultural Shift: Cultivating a data-centric culture within the organization where data hygiene is everyone's responsibility, not just the domain of IT departments.
4. Leveraging External Expertise: Collaborating with external partners who can provide the expertise and technological solutions to bridge the gap between current capabilities and strategic aspirations.
Laying the Data Foundation to Maximize AI Value
The investment to surmount data debt and effectively leverage AI transcends technical hurdles, emerging as a core business priority for leaders in the CPG, FMCG, and F&B industries. This challenge demands a reimagined approach to data management, where data integrity becomes a central tenet of corporate strategy. Emphasizing rigorous governance, continuous investment in data quality, and fostering a pervasive data-centric culture within the organization are essential.
As companies evolve, executive leadership must not only advocate but actively engage in establishing data governance frameworks that are robust and adaptive to the changing digital and regulatory landscape. By doing so, companies will not just manage to keep pace but will set the standards in a data-driven future, leveraging AI not just for incremental gains but as a transformative force that propels the organization to new heights of innovation and market leadership. This strategic focus on data management will be the differentiator that enables businesses to thrive in an era dominated by AI and big data.
