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Source: Harvard Business Review as of 03-03-2020

Thirty-five years after Robert Waterman’s observation in In Search of Excellence that companies were “data rich and information poor,” little has changed. For sure companies are “data richer,” having exponentially more data at their disposal. But they are still information poor, even as leaders have implemented a wide array of programs aimed at exploiting data. Most still struggle to build data into their business strategies and, conversely, to align their data efforts to the needs of the business. There are a host of reasons, from lack of talent to unreasonable expectations to culture. Solving these problems is essential for those that wish to unleash the power of data across their organizations.

It should come as no surprise that data is not yet strategic for many organizations. Business is already complex enough: When setting a company strategy, there are customers to satisfy, competitors to fend off, uncertain regulatory environments to accommodate, and skills gaps that must be closed. Plenty of great ideas — including carbon neutrality, diversity, social responsibility, new technologies, and yes, data — compete for resources and attention. Many success stories confirm data can add enormous value, but it is hard to know where data fits.

How organizations actually view their data assets is all over the map. Managers use it every day, even as they don’t fully trust it. Many find basic statistics confusing. People are rightly proud of their decision-making capabilities and see little need for better analytics or AI. They recoil at the thought of some sort of central oversight to their data, yet are stunned when a data issue creates unforeseen risk. While they know that privacy and security is important, no one has ever made their accountabilities explicit. And they realize that becoming a data-driven organization involves adapting their culture, which is difficult and time-consuming. It is little wonder that data is still far from the business strategy mainstream.

The data side of the business is no less complex. There is no shortage of great opportunities and demands, from analytics and artificial intelligence to data quality, monetization, privacy, small data, and security. Still most data work is of a keep-the-lights-on variety, such as adding new fields to databases, aligning systems that don’t talk, defining metadata, putting low-level governance in place, implementing business intelligence systems, wrangling data to feed machine-learning algorithms, and so on. All require business participation, but those who work with data have trouble engaging the business on these tasks, never mind strategy. When the business does ask for better data controls, data experts may lack the skills or business connections needed to drive an idea forward. The result is that data activities are too low-level, short-term, and poorly connected to business strategy.

But when integrated properly, data can accelerate many — even most — business strategies by improving the processes and empowering the people needed to execute them. Consider the example of a large medical center. The center’s management team understood that better use of data must become a core healthcare practice. But its data programs had fallen short of leadership’s expectations. To figure out why, the Chief Data Officer (CDO) matched each current data initiative to a list of possible scenarios where data can be used to achieve value. It became apparent that the data program was actually a collection of important, but one-off, projects. None were strategically aligned, and collectively, they were not up to the medical center’s needs. Once this issue was identified, the medical center was able to combine various initiatives into strategic initiatives that were tightly focused on business strategy, and then rigorously managed.

Better results across the entire center followed shortly thereafter. Most tangibly, compliance costs and fines were reduced, saving tens of millions of dollars. Improving provider data across all clinics made physicians’ jobs easier and led to better patient care. In turn, patient access improved, with increased visits for routine examinations for diabetes and colon cancer screenings, all while the center still maintained its target operating margins.

So how did this medical center see through all the complexities, find common ground, and establish priorities on which everyone could agree? At its heart, they simplified the problem by employing six data scenarios — ways that companies can derive value from dataWe call these scenarios “value modes,” and they include:

  1. Improved processes
  2. Improved competitive position
  3. New and improved products, stemming from better customer and market data
  4. Informationalization, or building data into products and services
  5. Improved human capabilities
  6. Improved risk management

We find that both business and data leaders understand these value modes well and can use them as a lingua franca to align their respective strategies. Quite generally, value modes facilitate disciplined thinking, help narrow the focus, and drive the right conversations.

We find that value modes are also especially helpful to business leaders trying to sort out the question: How can data help me? A regional bank employed these value modes after it had lost many of its high-wealth clients. The business goal was simple enough: recover market share. It didn’t fully understand why this had happened, so it focused first on understanding the issue from the client’s perspective, looking into its products and services, as highlighted in the third value mode. Advanced analytics revealed no surprises; clients were simply unhappy that their statements weren’t correct and that their transactions were not executed in a timely fashion. In response, the CDO focused on the first value mode — improved processes — and worked to clarify which processes bore on statements and trade data.

Making the needed improvements required coordination across departments and disciplines, which he effected via quality and governance programs. The first step focused on client contact data, the second on trade data, and the third on statements data. These steps removed a major source of client dissatisfaction, and clients stopped leaving the bank. Along the way it also became clear that clients wanted better ways to monitor their portfolios, leading the CDO to make major upgrades to the client portal. In time, the numbers of high-wealth clients recovered.

Value modes also help facilitate communication between business leaders and data experts. They help data experts clarify the potential and limitations in the full range of data options, and businesspeople to see how each option adds value. And both can collaborate to identify areas where data provides the best returns for the organization. What’s more, by using value modes, data experts and leaders alike can filter out the noise and hype that gets in the way of good planning. Today, for example, many people on both sides are smitten by artificial intelligence, causing a rush to adopt the technology before any formal thought is put into strategic benefits or risks. Before leaping in, leaders should sort out the benefits they hope to attain, using value modes to guide the discussion.

Complexity and abstraction are the enemies of a good strategy. Aligning your data efforts and strategy can seem daunting but focusing on these six value modes allows leaders to fit powerful data concepts into the dynamic business picture, and vice versa. There is not silver bullet here — hard work is still the order of the day. But the resulting forward motion of business and data teams finally working together is powerful indeed.

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