Ask your CEO, and they will say that your greatest asset is your people. Ask your marketing chief, and the answer will be your brand. And ask your CFO, your value is determined by your revenue or EBITDA. But is it possible that they may all be wrong…or may soon be? (Hint: how would your CIO or CTO answer?)

The profusion of systems has led to a bumper crop of data. Facebook, LinkedIn, X/Twitter, YouTube, and all of the other social media sites track the beginning of the journey, while marketing automation tracks the latter part of it. CRM systems track prospect and client activity, while ERP systems track both“delivery” and financials. Other systems track post-sales service activity, while the organization’s websites (internet, extranet, and intranet) generate even more data. Finally, data can be shared from suppliers, clients, and other partners, or purchased from third parties to further enrich what you already have.

While the term big data has been around for a number of years already, the reality for most organizations is that they have a big data mess. To begin extracting value from this asset, consider your organization’s data analytics maturity:

• Level Zero: Does not collect data. Relies on gut to make decisions. (And sporadic market research.)Since AI is not connected to corporate data, if AI is used, it has zero organizational context: risk is high
• Level I: Collects data within separate systems; reviews data sporadically. Relying on AI is still risky, as the training data is based only on some systems–not all of them.
• Level II: Data architecture that combines and“relates” all of the data from the disparate systems, so that it can later be used. Data is cleansed, and duplicate records are removed. Unfortunately, at Level II, the data is not generally accessible, except through difficult programming or special requests. Level II is only possible when a specific person has been assigned the role of VP of data strategy, and is given the authority and budget to make it happen. Level II makes a data-driven AI strategy possible.
• Level III: Specific slices of the data are exposed in real-time for better decision-making. Each user role may have a different dashboard, depending on their responsibilities, while standard reports are used throughout the organization. At this level, AI is“wired in”, and people are beginning to use it. But the type of use is more tactical, eg faster and more efficient workflows, AI-driven chatbots and customer service, and opportunistic analysis.
• Level IV: The data is proactively plumbed for insights, risks, and opportunities by people, but also by AI (and Agentic AI). Internal and external data are married to provide higher order insights and competitive advantage.

Most organizations are at Level Zero or I, yet aspire to Level IV without doing the heavy lifting required of Levels II and III. Building competitive advantage through data is just not possible without an investment in people, systems, an ongoing investment in data quality, and data analytics.

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