• Analytics
  • McKinsey & Co.

Mobilizing the C-Suite for Big-Data Analytics

Leadership-capacity constraints are undermining many companies’ efforts. New management structures can be part of the solution.

ChiefDataOfficer_510x175Leaders at one large transportation company asked its chief strategy officer to take charge of data analytics. To stretch the thinking and boost the knowledge of top managers, the CSO arranged visits to big data-savvy companies. Then he asked each business unit to build data-analytics priorities into its strategic plan for the coming year. That process created a high-profile milestone related to setting real business goals and captured the attention of the business units’ executives. Before long, they were openly sharing and exploring ideas and probing for new analytics opportunities — all of which helped energize their organizations.

Defining a data-analytics strategy
Like any new business opportunity, data analytics will underdeliver on its potential without a clear strategy and well-articulated initiatives and benchmarks for success. Many companies falter in this area, either because no one on the top team is explicitly charged with drafting a plan or because there isn’t enough discussion or time devoted to getting alignment on priorities. At one telecommunications company, the CEO was keen to move ahead with data analytics, particularly to improve insights into customer retention and pricing. Although the company moved with alacrity to hire a senior analytics leader, the effort stalled just as quickly. To be sure, the analytics team did its part, diving into modeling and analysis. However, business-unit colleagues were slow to train their midlevel managers in how to use the new models: they didn’t see the potential, which, frankly, wasn’t part of “their” strategic priorities.

As we have argued previously, capturing the potential of data analytics requires a clear plan that establishes priorities and well-defined pathways to business results, much as the familiar strategic-planning process does. Developing that plan requires leadership. At a North American consumer company, the CEO asked the head of online and digital operations, an executive with deep data knowledge, to create the company’s plan. The CEO further insisted that it be created in partnership with a business-unit leader who was not familiar with big data. This partnership — combining a data and analytics expert and an experienced frontline change operator — ensured that the analytics goals outlined in the plan were focused on actual, high-impact business decisions. Moreover, after these executives shared their progress with top-team counterparts, their collaborative model became a blueprint for the planning efforts of other business units.

Determining what to build, purchase, borrow or rent
Another cluster of decisions that call for the authority and experience of a senior leader involves the assembly of data and the construction of advanced-analytics models and tools designed to improve performance. The resource demands often are considerable. With multitudes of external vendors now able to provide core data, models, and tools, top-management experience is needed to work through “build versus buy” trade-offs. Do strategic imperatives and expected performance improvements justify the in-house development and ownership of fully customized intellectual property in analytics? Or is reaching scale quickly so important that the experience and talent of vendors should be brought to bear? The creation of powerful data assets also can require the participation of senior leadership. Locking in access to valuable external data, for instance, may depend on forging high-level partnerships with customers, suppliers, or other players along the value chain.

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