The road to true value is data-driven
How many organizations can truly call themselves data-driven?
In my experience working with businesses across industries to enable better data practices, I’ve noticed a contrast between perceived and actual confidence in the use of data. Externally, many companies are quick to share stories of success with specific data and machine learning initiatives. But internally — and particularly as you ascend to the c-suite — the cracks in that confidence become clear.
Consider the finding from a 2019 survey of c-level executives that nearly 70 percent of enterprise leaders do not believe their organization is data-driven.
Roadblocks to meaningful data engagement
That finding suggests a flaw in many enterprises’ data posture. By prioritizing optics — the need to be perceived as data-empowered — over strategic and intentional application, many organizations are laying a shaky foundation for a value-adding data strategy.
In my experience, there are two key roadblocks that stand in the way of an effective strategy:
- Siloes and tribal knowledge: Realizing the value of data across an enterprise demands a cross-functional approach. Often, however, I observe a factionalized approach, with Department A deploying a different set of data definitions and documentation than Department B, which tracks different metrics than Department C, and so forth. This lack of unification creates confusion, breeds mistrust, and leaves data difficult or impossible to analyze across departmental siloes.
- Rigid and inflexible systems: Gleaning insights from data is an iterative process requiring both a “fail fast” and a “show-me” mentality. Unfortunately, this need for agility often collides with engrained enterprise systems that are change-resistant. Without enterprise processes that support discovery and experimentation, an effective strategy quickly falls out of reach.
3 elements of an “insights first” approach
The ultimate goal of defining and implementing a data strategy is to turn information into insight, and insight into business change. But when enterprises face the roadblocks I mentioned above, insights become unattainable and change becomes out of the question.
My work with businesses involves surmounting these roadblocks with an “insights first” approach — one that keeps the focus on vision and values. Here are the three components of this approach I’d recommend every business follow to turn data into actionable insight:
- Start with alignment: A successful enterprise data strategy requires cross-functional alignment on the value of data - in a tangible way. Laying the foundation for cross-functional alignment starts at the c-suite, with top-down communication that identifies which metrics and data align to the firm’s vision and values. This alignment guides the pace and path for data initiatives, from governance to the data platform and from traditional reporting through advanced analytics, including machine learning and AI.
- Take a rapid insights approach: Gleaning true value from data demands organizational introspection. Enterprises need to ask the questions that identify the challenges and opportunities where data can enable better decisions.
This is the beginning of a process we call “rapid insights,” wherein businesses use laser-focused value-oriented questions as the launchpad for a period of insights iteration. During this period, they continuously ingest, model, visualize, review and refine data in weeks-long sprints until they’ve achieved stakeholder agreement on their vision and values.
- Don’t overlook your talent: Without the proper organizational roles and the associated training and change management efforts, even a well-conceived data strategy will become infeasible. To facilitate lasting organizational change, a data strategy has to permeate the culture — something 72 percent of companies haven’t achieved yet.
Gleaning insight from data demands more than an investment in tools. It requires investing in cross-functional alignment, rapid iteration and change management. These ingredients, when combined, create and sustain momentum to turn insights into action.
An iterative, “show-me” approach demonstrates the potential value of data first-hand and motivates individuals and teams to address data quality and availability challenges to reach that potential.