The Five Attributes of a Quantified Core Company


Strategy&, Contributor

February 6, 2015

by Ken Favaro and Ramesh Nair

Many companies have a conflicted relationship with the data they gather about themselves and their customers. They undertake dozens of analytical information-gathering initiatives—on cross-selling, upselling, customer acquisition, new product introduction, or inventory management. Novel and ambitious projects at the outset, they tend to have very short shelf lives. In the end, despite years of investment in data analytics programs and technology, most business leaders would admit that these initiatives have not yet improved performance in any sustained way.

However, a few companies have bucked the trend. The pioneers and leading experimenters in this field—Amazon, Apple, Capital One, Facebook, GE, Google, Pratt & Whitney, and Walmart—are learning to think of data as a long-term strategic asset and not a source of quick hits. They collect and analyze a wide range of data about customers, partners, operations, the marketplace, employee activity, product performance, and competitors—even before they know what to do with that data. They use analytics to spur previously unimagined ideas for customer offerings—and to deliver customized products and services, targeted at their individual interests. Most importantly, these companies deploy data analytics to design the distinctive capabilities that make them stand out from their competitors. Before long they reach a tipping point. Data and insights begin to fundamentally change the leaders’ perception of the enterprise, and become transformative and integral to their survival and growth.

Our name for this kind of data-driven business strategy is the quantified core. It is the enterprise equivalent of the “quantified self” movement, the tracking of individuals’ health and daily life patterns for the sake of improving both. Tens of thousands of “quantified selfers” around the world use an array of sensors to monitor the food they eat, the quality of the air they breathe, workout regimens, sleep patterns, moods, blood pressure, and a host of other metrics. The quantified core similarly uses a variety of sensors to collect data from all parts of the enterprise, and then analyzes that data comprehensively to improve and refine the business.

A quantified core capability has five mutually reinforcing attributes. Together, these represent the building blocks of a self-aware, data-savvy growth company.

1. A catalog of data and applications. You cannot put data to use unless you have a clear idea of what data your company already stores and what it might collect in the future. Although senior executives may understand the parameters of a particular data project or analytical experiment, they rarely have a full view of the range of data throughout the organization that is—or could be—curated, stored, and analyzed. IT departments may be more knowledgeable, but they are rarely attuned to the relationship between their data and leadership’s strategic priorities.

You may thus need to conduct a rigorous, business-focused cataloging effort. One financial-services firm began its company-wide data-for-growth campaign by preparing a comprehensive review of its information assets. The catalog included a roster of internal and external data sources, the current sources and recipients of each, and a plain-English definition of each data collection. Through this exercise, a common data language was created that allowed users throughout the company to see what data was available in the enterprise and the types of applications it was powering. This gave the company a meaningful platform for analyzing opportunities to make the most of its data.

2. Open knowledge sharing. To develop a quantified core, you need to be a right-to-know company. This means adopting an explicit knowledge-sharing model that gives all internal teams the license and means to query, extract, and massage the company’s data at any time, so long as privacy and confidentiality safeguards are in place. The right-to-know model reduces costs and makes the exchange of information much more convenient for employees, thus increasing their interest in using data for day-to-day business purposes and growth-oriented initiatives.

But most corporations find open knowledge difficult. They are need-to-know enterprises, burdened by data gatekeepers who demand use-justification and cost-benefit analyses before granting access to information repositories and analytical models. Often, privacy and compliance restrictions are brought up as arguments against broad-based data distribution. Not surprisingly, the business side is put off by this bureaucracy. People either find a workaround, which usually results in duplication, wasted expenditures, and lost opportunities, or they give up altogether.

Perhaps the biggest challenge for right-to-know companies is how few non-IT managers possess robust data skills. A consumer products company addressed this issue by requiring its MBA hires to go through an intensive orientation on data and its use, which includes placing recruits on the data team for some time. This program has enabled the company’s business side to develop a wellspring of talent in this field: It now has people who understand the company’s available data assets and know how to deploy them in creative ways to drive business decisions.

3. Cross-functional proficiency. A quantified core strategy requires a wide range of skills. Business leaders, department heads, and line managers are supposed to adapt data sets into growth strategies. To use information repositories to develop new products and services, they must be proficient in both analytics and innovation. When data leads information technology specialists to see breakthrough ideas, they must bring them to the operating units’ attention. Rather than producing software to the business specifications, IT specialists must know how to credibly and cogently present opportunities that they have noticed.

An analysis of job postings shows how seriously quantified core companies take this perspective when they are hiring. For example, Amazon’s requirements for a senior marketing manager include not just marketing competence, but technical skills in quantitative analysis and experience with data modeling tools like SQL. A good rule of thumb is that operating units should be able to handle 80 percent of data and analytical needs on their own. Highly specialized technical and analytical experts housed as a central resource for the business units can manage the rest.

4. A growth-oriented CDO. Although it’s a relatively new position, the chief data officer (CDO) role is becoming more common at large companies. By one measure, there are more than 100 CDOs in Fortune 500 firms now, double the number of a couple of years ago. Unfortunately, the CDO role in most of these companies is a gatekeeping function, tasked with ensuring data integrity and compliance. This limits the possibility that data will be actively and widely used for revenue growth. Instead of policing the rules, the CDO should foster connections between the company’s data assets and its businesses—particularly the frontline staffs of marketing, service, and sales. The CDO should focus on helping the business use its data to drive growth, and on ensuring that newer, more valuable data sources are continually identified, sourced, experimented with, and then rolled out through the businesses.

5. Semi-centralized funding. Project funding is one of the biggest and most common barriers to achieving quantified core success. In some cases, funding may suffer from being too decentralized. The costs for data projects in these companies are borne by business units and their IT groups, which diminishes the possibility of sharing the data, leveraging investments across the organization, or harvesting what is learned from individual analytics exercises. In other instances, funding may be too centralized, which inevitably slows decision making and may not provide the critical levels of investment business units need to get the most out of projects. Such shortfalls severely limit the enterprise-wide commercial impact of data projects.

The most effective funding and oversight model is centralized—but only to a point. Data investment strategies in this structure are developed, overseen, and partially funded by a corporate executive team. The team ensures that data and related capabilities are shared across business units, emphasizing both economies of scale and opportunities for collaboration. But the rest of the funding is decentralized. The business units fund data design and modeling, ensuring that there is no duplication of investments. The central authority monitors progress against the company’s stated data strategy, and business and functional leaders are accountable for driving the growth agenda and executing the strategy.

Implemented correctly, the five quantified core attributes can serve as a foundation for market leadership gained from using data on a scalable, sustained basis. But for that to happen, data should be part of a company’s strategic orientation, woven into the DNA of the enterprise. It should be available, accessible, easy to understand, and a constant part of the conversation when growth campaigns are designed. Indeed, it’s startling that so few companies have realized the promise of data in their efforts to grow their businesses—despite the enormous time, trouble, and financial resources that many of them have poured into hundreds of one-off projects. Now is the time to pull your efforts together, build a quantified core capability, and make data central to your business.

This article has been adapted from strategy+business. To learn more, check out their article, “The Quantified Core Goes Corporate.”

This article was written by Strategy& from Forbes and was legally licensed through the NewsCred publisher network.

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