English to Greek Other IT (Information Technology) management, big data

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?
There’s no substitute for serious engagement by the senior team in establishing such priorities. At one consumer-goods company, the CIO has created heat maps of potential sources of value creation across a range of investments throughout the company’s full business system—in big data, modeling, training, and more. The map gives senior leaders a solid fact base that informs debate and supports smart trade-offs. The result of these discussions isn’t a full plan but is certainly a promising start on one.

Or consider how a large bank formed a team consisting of the CIO, the CMO, and business-unit heads to solve a marketing problem. Bankers were dissatisfied with the results of direct-marketing campaigns—costs were running high, and the uptake of the new offerings was disappointing. The heart of the problem, the bankers discovered, was a siloed marketing approach. Individual business units were sending multiple offers across the bank’s entire base of customers, regardless of their financial profile or preferences. Those more likely to need investment services were getting offers on a range of deposit products, and vice versa.

The senior team decided that solving the problem would require pooling data in a cross-enterprise warehouse with data on income levels, product histories, risk profiles, and more. This central database allows the bank to optimize its marketing campaigns by targeting individuals with products and services they are more likely to want, thus raising the hit rate and profitability of the campaigns. A robust planning process often is needed to highlight investment opportunities like these and to stimulate the top-management engagement they deserve given their magnitude.