Setting Analytics At the Center

Drawing your users in—and tapping the capabilities of different individuals across your organization to do so—is essential. Analytics is a team sport. Decisions about which analyses to employ, what data sources to mine, and how to present the findings are matters of human judgement.

Integrating data alone does not generate value. Advanced analytic models are needed to enable data-driven optimization (for example, of employee schedules or shipping networks) or predictions (for instance, about flight delays or what customers will want or do given their buying histories or Web-site behavior). A plan must identify where models will create additional business value, who will need to use them, and how to avoid inconsistencies and unnecessary proliferation as models are scaled up across the enterprise.

As with fresh data sources, companies eventually will want to link these models together to solve broader optimization problems across functions and business units. Indeed, the plan may require analytics “factories” to assemble a range of models from the growing list of variables and then to implement systems that keep track of both. And even though models can be dazzlingly robust, it’s important to resist the temptation of analytic perfection: too many variables will create complexity while making the models harder to apply and maintain.

What’s needed are intuitive tools that integrate data into day-to-day processes and translate modeling outputs into tangible business actions: for instance, a clear interface for scheduling employees, fine-grained cross-selling suggestions for call-center agents, or a way for marketing managers to make real-time decisions on discounts. Many companies fail to complete this step in their thinking and planning—only to find that managers and operational employees do not use the new models, whose effectiveness predictably falls.

There’s also a critical enabler needed to animate the push toward data, models, and tools: organizational capabilities. Much as some strategic plans fail to deliver because organizations lack the skills to implement them, so too big-data plans can disappoint when organizations lack the right people and capabilities. Companies need a road map for assembling a talent pool of the right size and mix. And the best plans will go further, outlining how the organization can nurture data scientists, analytic modelers, and front line staff who will thrive (and strive for better business outcomes) in the new data- and tool-rich environment.

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