Executives who want to sharpen their analytic edge and manage a data-driven company need to start with themselves, then move on to their team and finally to their organization. Such a transformation requires actions along four critical dimensions, as well as a double-game perspective. (See Exhibit 2.)
Leadership: An Analytics-Driven Business Mindset. The best data-driven companies have a data-driven culture. Executives at these companies—ranging from digital attackers, such as Amazon, to century-old companies like Procter & Gamble—make decisions on the basis of rich, near-real-time data. Data analytics have evolved into a core general-management skill, similar to corporate finance and cost accounting. While executives would never base their decisions entirely on such information, they also would not proceed without it. They are comfortable leading discussions and teams centered on data analytics.
Data-driven companies embrace the democratization of data. Managers and employees can access and interpret a wide range of company data using intuitive self-service tools. They explore topics and generate answers more quickly and with less friction than if they had to go to specialized data analytics or business intelligence teams.
At Amazon, many managers spend 5% to 10% of their time working directly with databases, constantly striving to define and measure the right metrics. In meetings, they are expected to condense their findings into concise, data-rich documents that will frame subsequent discussions and decisions. This culture extends all the way to recruiting, with candidates for management positions assessed on their skills and eagerness to get their hands dirty with data.
At P&G, managers have access to standardized datasets, powerful data visualization tools, intuitive dashboards, and immersive conference rooms with large wall-screen data displays. More than 50,000 employees also have access to a “decision cockpit” that displays critical data in near real time. These methodologies collectively form a sort of common language.
Organization: Cooperative and Agile Ways of Working. The organizational challenges of creating a data-driven company fall into two main areas:
- How to balance the advantage of scale with local entrepreneurship and knowledge of specific businesses or local markets
- How to reduce friction between business owners and quants in order to become more effective and faster
A company’s choices will depend on its specific capabilities and its need to boost entrepreneurial activity, reduce friction, and foster cooperative performance. A global beverage company wanted to retain local entrepreneurship, its core strength, while leveraging analytics to improve the customer experience and its own go-to-market activities. Local teams, however, lacked data analytics skills and scale. Headquarters could provide the data analytics but was too far removed from regional operations.
Ultimately the company decided to create a global SWAT team consisting of ambitious, business-savvy executives paired with analytics and IT executives. This team worked with local teams to launch projects for specific use cases in individual markets and was responsible for platform development and knowledge transfer. The approach balanced speed, scale, and cross-regional fertilization with strong local buy-in and skills building.
A conglomerate whose many different businesses each required specific know-how faced a related centralization-versus-decentralization challenge. Being more technology savvy, this company embedded dedicated analytics teams into every business unit and also created a unit in the center. The headquarters unit provided support by staying attuned to the latest in algorithmic research and technology, building common technology platforms, and supporting the businesses through decentralized skills development.
In parallel, companies should improve the cooperation between business owners and quants by embracing agile principles that originated in software development two decades ago. Agile is becoming the method of choice for organizations aiming to transform areas where time to market is critical.
At a leading retailer, for example, the analytics team developed new applications with business owners by forming small, multidisciplinary project teams. These teams had authority to act independently. Working in two-week “sprints,” they created working versions of an application and then sought direct user feedback that they used to build the next version. This iterative approach drastically improved mutual understanding, shortened development time, and reduced delivery risks.
Skills: Simultaneously Hiring and Transforming. Organizations need to evolve and refresh their skills to address the fast pace and changing requirements of data analytics and other technologies. In doing so, they need to build up their dedicated analytics teams—predominantly by hiring—as well as train their core workforce.
AIG, a leading insurance company, created a “science team,” hiring 90% of its 130 members externally. Recognizing the need to blend data analytics into the fabric of the business, the team recruited behavioral economists, psychologists, and change management experts in addition to analytics specialists. This cross-functional team created not only sophisticated novel solutions but also creative ways to implement them. (See “How AIG Moved Toward Evidence-Based Decision Making,” Harvard Business Review, October 1, 2014.)
Other companies have hired “data driven” officers, ranging from middle managers to senior leaders, as a way to create a new mindset. An executive from a major online retailer put it bluntly: “Experienced external hires for management positions are often not used to drilling down to the raw data. They come from the ‘aggregated’ world. I rather focus on hiring young and clever people.”
In addition to building dedicated units of analytics specialists, leading consulting companies have become models for how to train the broader workforce. For them, the scarce resource is generalist consultants at all levels, who can bridge the gap between data analytics and business opportunities, not data scientists and IT specialists. These companies are making heavy use of modern intuitive analytical and visualization tools and are rapidly expanding and tailoring their development programs so that consultants can conduct rigorous data analysis and tightly frame the tougher challenges for the specialists. When data analytics is widely applied, innovation and entrepreneurship start to flourish.
Systems: Investing in Data and Steering the New Technologies. Historically, discussions about data and IT systems have been tedious and technical, delivery has been slow and expensive, and productivity results disappointing. So why bother?
The answer is rather simple. With data analytics developing into a source of competitive advantage, and with speed, ease of use, and machine intelligence changing the role of IT, executives have no choice but to embrace the topic.
Data has become a form of currency that companies use to generate business value. P&G, for instance, is constantly investing in new sources of data and improving data quality. Its approach varies by market. In mature markets, P&G receives high-quality data from retailers via data warehouses. In some emerging markets, mom-and-pop shops are still a major distribution channel, and they cannot afford to make large technology investments. So P&G leverages mobile phones to provide support in ordering, store design, and product placement, while concurrently collecting business data.
Building the system infrastructure to support data analytics can be tricky on many fronts. The technology is new and rapidly evolving. And companies must make critical choices about the optimal technology stack and the best vendors. To master these challenges, they must take several critical steps:
- Establish priorities that are based on the value of concrete business use cases and derive their technological requirements over the short to medium term.
- Invest in IT expertise. Hire outside data analytics specialists who align with the changing role of IT departments. These outsiders often provide fresh ideas about better, faster ways to do things.
- Refrain from lengthy and costly cross-integration of legacy systems. Instead, leverage modern technology to extract and clean data, and deposit it in a common location—for example, a “data lake”—from which multiple systems can extract it.