NEW YORK – In executive suites and digital war rooms across the country, a familiar scene unfolds daily. Managers, armed with glowing dashboards packed with charts and metrics, find themselves paralyzed by an abundance of information but a deficit of direction. They are data-rich but insight-poor, operating under the dangerous illusion that access to data is the same as being driven by it. This critical distinction is not merely semantic; it represents the dividing line between companies that will thrive in the age of artificial intelligence and those that will be left behind, tethered to reactive, legacy thinking.

This is the challenge that keeps leaders like Leslie A. P. Wikener, Chief Data Officer at the sales and marketing giant Advantage Solutions, focused on a mission that is more cultural than technological. The goal is to evolve the enterprise from being “data-reliant”—passively consuming reports—to truly “data-driven,” where data actively shapes behavior, strategy, and financial outcomes. “Just relying on data doesn’t make you data-driven,” Wikener stated in a candid conversation with CDO Magazine. It’s a stark warning that possessing vast datasets and sophisticated business intelligence tools is no longer a competitive advantage; the real edge comes from embedding a proactive, data-literate mindset deep within the organization’s DNA.

At a company like Advantage Solutions, which operates at the hyper-competitive intersection of consumer goods and retail, the stakes are immense. A data-reliant CPG company might see a dashboard indicating a sales dip for a product in a specific region and react by launching a new promotion. A data-driven competitor, however, would use predictive analytics to anticipate that dip based on supply chain signals, competitor pricing, and consumer sentiment, reallocating marketing spend weeks in advance to prevent the decline altogether. The former is playing defense; the latter is orchestrating the market.

For years, the success of data and analytics teams was often measured by outputs: the number of reports generated, dashboards deployed, or data warehouses built. This “build it and they will come” approach has resulted in a sprawling infrastructure of underutilized tools and a growing frustration among business leaders who struggle to connect these investments to the bottom line. Wikener argues for a radical shift in perspective, advocating for measuring data initiatives exclusively by the business outcomes they influence. This means tying every project to tangible metrics like revenue growth, margin improvement, or operational cost savings.

This focus on tangible return on investment is a sentiment echoing across the industry as organizations mature. The research and advisory firm Gartner emphasizes that demonstrating how data and analytics programs contribute to enterprise value is paramount for securing continued investment and executive sponsorship. According to a Gartner report on the subject, leading organizations are moving away from activity metrics and instead creating a direct line of sight between analytics work and key performance indicators like customer lifetime value or supply chain efficiency. This transforms the data team from a cost center into a strategic partner directly involved in value creation.

In the race for agility, data governance is often perceived as a bureaucratic bottleneck—a set of restrictive rules that slows down innovation. However, seasoned data executives contend this view is dangerously shortsighted. Wikener frames governance not as a brake but as the very engine of speed and trust. Without clear data ownership, documented lineage, and certified quality, end-users will inevitably question the insights presented to them. This lack of trust is the silent killer of data-driven initiatives, leading to low adoption rates and a reversion to gut-feel decision-making.

This principle becomes exponentially more critical with the rise of artificial intelligence. An AI model, particularly a complex generative one, is a powerful amplifier. When fed with high-quality, well-governed data, it can uncover profound insights and efficiencies. But when it ingests poorly managed, inconsistent, or biased data, the old adage of “garbage in, garbage out” morphs into what Wikener calls “garbage in, catastrophe out.” Inaccurate AI-driven forecasts can lead to million-dollar inventory errors, while biased algorithms can create significant legal and reputational risk. As a recent MIT Sloan Management Review article notes, the rigor required for enterprise-grade AI is forcing a new level of discipline in data management, making robust governance a non-negotiable prerequisite for any serious AI ambition.

The cultural and strategic shift required to become data-driven has fundamentally reshaped the role of the Chief Data Officer. The first generation of CDOs was often focused on defense—ensuring regulatory compliance and building foundational data infrastructure. Today, the role has evolved into a strategic business function, requiring a leader who is as comfortable discussing go-to-market strategy with a sales leader as they are debating data architecture with an engineer. The modern CDO must be a translator, capable of understanding a core business problem and reframing it as a data science question.

This requires moving beyond simply fulfilling requests for data pulls or dashboard widgets. It’s about asking “why” to understand the underlying business challenge a stakeholder is trying to solve. This consultative approach ensures that the solutions being built are not just technically sound but are designed to drive a specific decision or action. This transition of the CDO from a technical custodian to a strategic business partner is a key marker of a maturing data organization, a trend highlighted in publications like the Harvard Business Review, which points to the most successful CDOs as those who embed themselves deeply within the business units they serve.

Ultimately, the value of any data-driven insight is only realized when it is acted upon. This is the “last mile” problem of analytics: the challenge of delivering the right insight to the right person at the right time and in a format that facilitates immediate action. A beautifully designed dashboard is useless if a frontline manager doesn’t have the time or training to interpret it during their busy workday. The future of data application lies in embedding insights directly into the workflows and tools that employees already use, providing recommendations and alerts that feel intuitive rather than burdensome.

As companies race to harness the power of AI, solving this last-mile problem is becoming the ultimate competitive differentiator. The challenge is no longer just about building a powerful predictive model, but about operationalizing its output at scale. It’s about delivering an AI-generated recommendation for a sales representative directly within their CRM platform or providing a dynamic, AI-optimized inventory reorder point directly within a procurement system. For enterprises like Advantage Solutions and countless others, the journey from being data-reliant to truly data-driven is not about acquiring more data, but about mastering the culture, governance, and last-mile delivery that finally transforms information into decisive action and sustainable value.