Investing in assets such as technology, tools, and data sets; and tackle the intrinsic challenges of securing commitment, reinventing processes, and changing organizational behavior. Our collection of content, which synthesizes key insights drawn from many analytics projects, sets out the key issues, whether you are launching a pilot project or a large-scale transformation.
The impact of “big data” analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound. And if an organization can systematically combine small improvements across bigger, multiple processes, the payoff can be exponential.
As companies develop their big-data plans, a common dilemma is how to integrate their “stovepipes” of data across, say, transactions, operations, and customer interactions. Integrating all of this information can provide powerful insights, but the cost of a new data architecture and of developing the many possible models and tools can be immense—and that calls for choices. Planners at one low-cost, high-volume retailer opted for models using store-sales data to predict inventory and labor costs to keep prices low. By contrast, a high-end, high-service retailer selected models requiring bigger investments and aggregated customer data to expand loyalty programs, nudge customers to higher-margin products, and tailor services to them.
That, in a microcosm, is the investment-prioritization challenge: both approaches sound smart and were, in fact, well-suited to the business needs of the companies in question. It’s easy to imagine these alternatives catching the eye of other retailers. In a world of scarce resources, how to choose between these (or other) possibilities?
A natural impulse for executives who “own” a company’s data and analytics strategy is to shift rapidly into action mode. Once some investment priorities are established, it’s not hard to find software and analytics vendors who have developed applications and algorithmic models to address them. These packages (covering pricing, inventory management, labor scheduling, and more) can be cost-effective and easier and faster to install than internally built, tailored models. But they often lack the qualities of a killer app—one that’s built on real business cases and can energize managers. Sector- and company-specific business factors are powerful enablers (or enemies) of successful data efforts. That’s why it’s crucial to give planning a second dimension, which seeks to balance the need for affordability and speed with business realities (including easy-to-miss risks and organizational sensitivities).
Finally, some planning efforts require balancing the desire to keep costs down (through uniformity) with the need for a mix of data and modeling approaches that reflect business realities.