Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. In a recent Harvard Business Review article we explore how companies require three mutually supportive capabilities to fully exploit data and analytics: an ability to identify and manage multiple sources of data, the capacity to build advanced analytic models, and the critical management muscle to transform the organization.
Getting started on a successful data and analytics journey, however, is a continuing challenge for many leaders and they often struggle for a clear strategy that ties data and analytics to improved performance. We took a close look at companies that have recently launched big data strategies to shed further light on the tough road C-level executives face. From these experiences, we have distilled four principles to defining a strategy and getting started:
1. Size the opportunities and threats
Opportunities may range from improving core operations to creating new lines of business — even in the same industry. For example, insurance companies can use big data to improve underwriting performance now, while over the longer term they can use it to serve formerly unprofitable customers and ultimately even develop entirely new risk-based businesses. The key is to establish a clear-eyed view of the business impact expected at each stage of implementation in order to better focus efforts, and determine priorities.
In the case of a retailer we studied, data and analytics were 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 company's revenues and denting its margins. At the heart of the threat was the new competitor's ability to gather and analyze consumer data to generate recommendations across millions of customers while becoming a platform where vendors could sell excess inventory at a discount by using publicly-available price data. Responding to this threat required both debate on 'what business are we in' and investment to use data and analytics to drive important performance improvements
2. Identify big data resources . . . and gaps
Framing the basics of a big data strategy naturally leads to discussions about the kinds of information and capabilities required. For example, a review will have to consider access to analytical talent as well as potential partnerships that might help fill gaps. We often find that consideration of required internal and external data will often spark "aha" moments — as executives identify "data gems" cloistered inside their business units or recognize the value of creating the right kind of partnership.
The retailer mentioned above found that the company gathered volumes of data but wasn't using it to potential. This information on product returns, warranties, and customer complaints contained a wealth of information on consumer habits and preferences. The review also revealed that none of the information was integrated with customer identification data or sufficiently standardized to share within or outside the company. Happily, the company had a team that could help solve these problems: in-house data analysts whose siloed efforts were underused.
3. Align on strategic choices
Once companies identify an opportunity and the resources needed to capitalize on it, many rush immediately into action-planning mode. This is a mistake. Data strategies are likely to be deeply intertwined with overall strategy and therefore require thoughtful planning when a company decides how its resources should be concentrated to achieve the desired results.
It's also important to view data and analytics in the context of competing strategic priorities. In the case of a telecom provider, a cross-functional executive committee was created to oversee the analytics team and ensure that its efforts were aligned with the company's strategy. The committee focused the team's efforts on two questions: "How competitive are our brands in the minds of users when they make purchase decisions?" and "What key buying factors matter for users, and how well positioned are we to communicate with customers about these factors?"
The team then combined customer data from several sources to surface actionable insights — for instance, sports and other premium TV programming was a key differentiator in purchasing decisions, and customers would be more inclined to purchase a "triple play" service offering (television, high-speed Internet, and voice telephony) if the company de-emphasized voice telephony in its marketing messages. This was the opposite of what consumers had indicated in traditional market research interviews. The analysis also underscored — and helped quantify for executives — the importance of a bigger strategic imperative: the need to add mobile telephony as a fourth service to complete a "quadruple play."
4. Understand the organizational implications
Finally, it's important to note that the threats and opportunities associated with big data often have organizational implications that only concerted senior-executive attention can address. For example, at another telecom player, the consumer data-insights team learned that two things led to the most rapid spread of negative word of mouth about the company on social-media and microblogging sites: network outages and any perception by customers that the company had made false advertising claims about its products or network. Initially, the marketing and network organizations, rather than cooperate, blamed one another for the findings. Only when senior executives forced the two sides to work more closely together and build trust could the company capitalize on the information, by tailoring marketing messages to better explain new-product rollouts and network upgrades.
Finally, we often see stresses on technical and analytic resources as a company seeks to capitalize on data and analytics. Thus, whether a company is planning a single, large initiative or multiple smaller ones, its senior team must stay mindful of the resources required (technological and otherwise) to shift quickly from pilot to "at scale" implementation.
- Can You Live Without a Data Scientist?
- How to Repair Your Data
- Ignore Costly Market Data and Rely on Google Instead? An HBR Management Puzzle
- Three Questions to Ask Your Advanced-Analytics Team