Seizing the potential of ‘big data’
Companies are learning to use large-scale data gathering and analytics to shape strategy. Their experiences highlight the principles – and potential – of big data.
October 2011 – Jacques Bughin, John Livingston, and Sam Marwaha
Source: Business Technology Office
Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. While the moves of companies such as Amazon. com, Google, and Netflix grab the headlines in this space, other companies are quietly making progress.
In fact, companies in industries ranging from pharmaceuticals to retailing to telecommunications to insurance have begun moving forward with big data strategies in recent months. Together, the activities of those companies illustrate novel strategic approaches to big data and shed light on the challenges CEOs and other senior executives face as they work to shatter the organizational
inertia that can prevent big data initiatives from taking root. From these experiences, we have distilled four principles that we hope will help CEOs and other corporate leaders as they try to seize the potential of big data.
1. Size the opportunities and threats
AstraZeneca’s ‘big data’ partnership
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Many big data strategies arise when executives feel an urgent need to respond to a threat or see a chance to attack and disrupt an industry’s value pools. At AstraZeneca, for example, executives recognized the power that real-world data (such as medical claims) gave the pharmaceutical company’s customers in evaluating the cost effectiveness of its products (for more, see sidebar, “AstraZeneca’s ‘big data’ partnership”).
In the case of a retailer we studied, big data was part of a difficult battle for market share. The company’s strategy had long been predicated on matching the moves of an efficient big-box rival, yet now a different online player was draining the retailer’s revenues and denting its margins. At the heart of the threat was the competitor’s ability to gather and analyze consumer sentiment and generate recommendations across millions of customers – a capability that was neutralizing the retailer’s sales force. Meanwhile, the competitor was becoming a platform where vendors could sell excess inventory by using publicly available price data aggregated across the industry to help pinpoint the size of discounts the vendors could offer to customers. The retailer’s board asked whether it could leverage its own information resources to counter these challenges.
Data-related threats and opportunities can also be more subtle. After using an innovative product-bundling approach to improve market share, for example, a European telecom company saw large-scale data analysis as a way to boost momentum. The company’s executives believed it could press its newfound advantage by pinpointing exactly where its sales approach could make further gains and by studying the behavior of customers to see what factors motivated them to choose one brand or product over another. Doing so would require interpreting two massive and growing volumes of information: online search data and real-time information – shared by consumers across social networks and other Web-based channels – about the company’s products and services.
2. Identify big data resources. . . and gaps