Data can fulfill many roles for businesses, from functioning as a “utility” essential for running the business to serving as a source of competitive advantage. Companies today are increasingly focusing on data strategy because it enables them to do the latter. While most companies have some form of data strategy in place, many fail to translate that strategy into a long-lasting source of competitive differentiation.

 

Like many explorations, this one began with two “simple” questions. Number one: Why do data strategies so often fail to create sustained competitive advantages? And number two: What specific capabilities must companies foster to build these advantages? As we set out to answer these questions, we identified several areas that may hold the secret to building competitive advantages, but we’ve narrowed these areas down to the six most critical capabilities for developing data mastery. Building these capabilities requires two things above all: time and determination.

Capability 1: Strategic alignment  

Strategic alignment is the ability to formulate a data strategy from the key elements of the company business strategy. A data strategist who is not well versed in the key elements of the business strategy will have limited success in building a winning data strategy. In addition to having the right talent, this requires a specific set of data strategy tools, approaches and a process—most of which can be borrowed from the discipline of business strategy development. Putting together a data strategy is impossible without a proper planning process—a strategic planning equivalent—for data. Having a well-defined process ensures that the data strategy is tightly connected to the business goals, is pressure-tested and balances a long-term vision with short-term needs.

 

Lack of strategic alignment manifests itself in a few ways. First, companies often fail to define the role and scope of data in driving digital transformation. Second, these companies are reactive, continually responding to short-term business needs or chasing the next shiny new data type in a bid to spur innovation. Third, they miss out on data’s true potential by leveraging it to address key business problems but missing the potential to build new ecosystem partnerships and create new revenue streams.

 

Data leaders should ask themselves: “Am I truly maximizing the potential of my data portfolio?”

 

Capability 2: Data landscape scanning

New data types and sources are proliferating, and they come from unexpected sources that can spring up from anywhere, sometimes very quietly. A company’s ability to understand the data landscape includes its ability to fully leverage the power of external data while also being attuned to the landscape of data providers.  

 

Data that just a few years ago was considered cutting edge—geospatial data, for instance—is now considered table stakes for companies. When COVID-19 first emerged, even the most seasoned companies scrambled to identify the best external signals to forecast their business performance. This highlights the need for companies to have an “always on” data scanning capability.

 

As the competitive landscape of data and research suppliers evolves, it triggers a flurry of partnerships and acquisitions. Such events can have a meaningful effect—either positive or negative—on companies that rely on data from affected companies. The acquisition of a data vendor, for example, could preclude the vendor from providing one of its customers with raw data for a specific business reporting application. In such cases, having up-to-date intelligence on the data landscape will help companies be nimble enough to proactively seek alternatives. Having this data strategy capability also enables large organizations to make strategic investments in data companies.  

 

Capability 3: Data innovation

Master this triumvirate of part-art, part-science capabilities to seize innovation with data: 1. Build proficiency in translating business problems into data-driven solutions, tapping into relevant data signals; 2. Invest in new data sets to test, prototype and fail fast, and scale up solutions that prove successful; 3. Commit to resource allocation, governance and metrics to exploit innovation as a source of revenue growth. Until recently, most companies relied on product innovation as a growth lever, but forward-thinking organizations now view data as a lever for innovation, too.

 

None of this is easy, however, without rapid sourcing of nontraditional and emerging data sets and the freedom to test and fail fast. Organizations with a mature data strategy can support a brave, offensive strategy if they have well-defined sponsorship and resource allocation models to give data-driven innovation the runway it needs to flourish.

 

To evaluate maturity with data innovation, ask yourself: “Does my company have the skills, processes, governance and tools required to execute data innovation?”

 

Capability 4: Data advantages

Companies that build proprietary data advantages go on to create sustainable competitive advantages for the entire enterprise. Companies such as Starbucks, Amazon and Netflix are impressive, time-tested exemplars of using first-party, customer-created data to build competitive advantages. However, companies should know that there are other sources of data advantages, for example: access to data from the marketplace, data partnerships and exclusivities, or creating synthetic data by leveraging advanced analytics on a large portfolio of data assets. This may seem like a daunting task to some, but not every company needs to start from scratch. To start building this capability, companies should assess their potential sources of data advantages that may be hidden, untapped or not effectively scaled.  

 

Capability 5: Data preparedness

A mature enterprise data strategy is forward-looking and nimble, and it should equip a company to respond well to any potential scenario. Data preparedness is about preparing for—not predicting—the future. Data scanning is a key enabling capability of data preparedness.

 

Competence in data scenario planning enables a company to boldly pursue no-regret moves. Whether it’s scenario A, B or C that ultimately materializes, data preparedness readies a company for any outcome. Companies that ran a scenario planning exercise on third-party cookies, for example, will have anticipated the possible loss of this data and shifted to building or strengthening their first-party data—a good move even if third-party cookies weren’t going away.

 

To determine proficiency in this capability, revisit what your company did—and did not do—over the past 12 months. Ask: “Is my company preparing itself for future scenarios that can affect our ability to access, use or maximize value creation from data?”

 

Capability 6: Data asset leverage

Because an asset that is not measured cannot be optimized, a best-in-class strategy carefully builds capabilities for measuring and tracking the value of data. Despite the focus and attention companies give to data value extraction, many still struggle to answer the most fundamental data question: “What is the current value of my enterprise data assets?” More complex questions such as “What’s the maximum potential value of my enterprise data assets?” are critical, especially when thinking about data-driven revenue streams. Yet they are not on the radar of many companies. 

 

A good place to start is to quantify the value of a company’s most commonly used individual data sets. This should gradually extend to measuring the collective value of the entire data portfolio. Data asset leverage can be best understood by focusing on the scope of the application of data, quantifying its overall business impact and considering how else data may be used. Which statement can you stand behind: “We have solid KPIs that measure the value of data, and we take actions against the measurement,” or “We aspire to measure the value that data creates, but we are not there yet.”

Whether your company treats data as a business-essential utility or a source of sustainable competitive advantages, executing your data strategy doesn’t need to be overwhelming. But keep in mind: Data is just one piece of the puzzle. Developing long-lasting competitive advantage through data requires tight alignment of analytics, technology, processes and culture.

 

CEOs, CDOs, CIOs and other senior leaders with ambitious growth strategies are tightening strategic alignment and applying to data strategy the same principles that guide their business strategies. Data strategy successes happening behind the scenes right now may not come to light for some time. If your company aspires to become a leader in areas like Industry 4.0, AI engineering, decision intelligence, digital twins and the multiverse, it will first need to build a robust data strategy and the capabilities to execute it.

 

Has your company mastered these six data strategy capabilities? Use our diagnostic tool to measure your maturity versus other businesses.

 

This article contains contributions from Jared Bosma.