Managing Director & Senior Partner; Global Leader, Technology Advantage Practice
Powerful algorithms have become table stakes and no longer create competitive advantage, as they once did. Today what matters is having the best data for those analytics engines to crunch. Yet many companies still haven’t identified the data that gives them a sustained competitive advantage, and their lack of data maturity hinders their data strategies and business outcomes.
Whether a company is looking to make better use of existing data to achieve specific business outcomes, or wants to gather new data to create an additional line of business, it needs to understand and address various barriers to data maturity. Then it needs to identify its advantaged data and formulate a data strategy to open new sources of competitive advantage—tasks that entail answering several key questions about advantaged data, data collection strategy, data monetization strategy, and operating model.
The Barriers to Data Maturity
We have documented companies’ struggles with data maturity across industries and geographies. Since 2016, BCG has conducted digital transformation surveys of companies to assess their ability to leverage data. In 2019, we found that only 27% of companies had reached the advanced stage of data maturity, which we calculate based on seven elements: vision, use cases, analytics, data governance, data infrastructure, data ecosystem, and change management. (See Exhibit 1.)
We also found that while many companies have high data ambitions, few achieve those ambitions. In 2019, only about 10% of companies reported that they had met the data targets they had set in 2016. Moreover, most were far from achieving their 2021 ambitions, which they set in 2018.
Companies need to accelerate their progress toward data maturity in order to achieve their data ambitions. But it’s not easy. Our interviews with leading companies identified five major pitfalls that companies encounter when trying to reach data maturity:
Meanwhile, however, some companies are avoiding these pitfalls and have successfully improved their data maturity by identifying their advantaged data sets. John Deere put sensors on its large installed base of agricultural equipment and began acquiring data to improve machine performance and provide advanced decision support for farmers.
A leading oil company partnered with BCG to collect data on drilling operations, equipment, and geological characteristics. It used this data to train machine learning algorithms and develop tools to give workers end-to-end visibility into whatever process they are running. This enables them to make quick decisions at the rig so that, for example, they can better manage the circulation and pressure of the fluid used in fracturing the rock, and maintain fast enough drill-bit rotation to prevent the machinery from getting stuck.
Putting a Data Strategy in Motion
Once a company has identified its advantaged data sets—which link closely with the business outcomes it plans to achieve—it will have a much easier time managing the other parts of the data strategy: defining business outcomes, identifying needed analytics, integrating the data, establishing appropriate infrastructure to retrieve the data, and adapting the corporate culture to use the data optimally. (See Exhibit 2.)
Identifying advantaged data sets does not always entail making exceptional efforts to gather new data or create a new line of business. In some instances, the strategy defines the data requirements; in others, the data defines the strategy. But leaders should always think through the two in tandem. By reframing the business outcomes, they can turn existing data into advantaged data sets.
In order to define and leverage advantaged data, company executives need to ask four key questions.
What is my advantaged data? Given a clearly articulated vision for the business, what data sets can bring that vision to life and create a unique competitive edge? (See the sidebar.) For example, does the company want to differentiate itself from its peers by delivering a great customer experience or by maximizing its operational efficiency or by innovating rapidly? Once all stakeholders are aligned on the vision, leaders can work backward to identify the data needed to achieve the targeted business outcomes.
In John Deere’s case, the overall strategy is to extend a traditional manufacturing business into the lucrative realm of agricultural services. Consequently, the advantaged data related to how customers use its equipment, and how that equipment performs under different circumstances.
For the oil company, the advantaged data sets involved a database of old, unused wells (consisting of operations data mudlogs, details about geological characteristics, and the like) along with information that advanced sensors were capturing from live wells (such as operational parameters, drilling parameters several kilometers down the well, and stratigraphic features). This data helped the company optimize its operations.
What is my data collection strategy? After identifying the necessary data, a company can use several methods to collect or acquire the data. This may involve leveraging internal data, or it may involve acquiring external data in various ways:
What is my data monetization strategy? How will the company create new revenue streams with the data? An internal monetization strategy involves using data that the company already captures in new ways (possibly in combination with public data), and then leveraging that data across business units to improve current offerings or develop new ones. An external monetization strategy involves relying on propriety and public data, as well as on data from partnerships and ecosystems, to create a data platform as a service.
Ideally, an organization will sequence its monetization strategy so that it can use an early mix of quick revenue wins to fund bold transformational moves that take longer to generate returns. But even if a well-conceived data strategy requires sustained investment, its transformative, strategic benefits will massively outweigh the interim cash drain. In any event, whether the monetization strategy is internal or external, the organization needs to understand who will leverage the data, where the data will come from, and what the value proposition is. (See Exhibit 3.)
John Deere monetized its investment by creating an open platform that combines proprietary data with data collected from machines, farmers, and external partners. Farmers can use software tools on this platform to manage their fleets, save fuel costs, and decrease equipment downtime.
For its part, the oil company has monetized the data behind its new drilling technology by cutting nonproduction time by 4%, drilling faster (decreasing drilling time by 6%), and doing so more safely.
What operating model do I need? Effective use of advantage data will not materialize unless the company and its culture become data centric. To prepare its organization for this fundamental shift, the company needs to move forward on five fronts:
As capabilities around algorithms and analytics quickly become table stakes, companies must look to advantaged data sets for a sustainable competitive edge. John Deere realized that complacency about its leading market position would give smaller, nimbler players an opening to disrupt the market. So instead it proactively began to define its advantaged data sets and devise a new data strategy. The oil company’s advantaged data sets and new data strategy took shape when its leadership embraced a vision of becoming one of the most innovative, efficient, and safe oil drillers.
The good news is that in many cases such data already resides within the company’s walls and is there for the taking—as long as the organization puts a sound data strategy in place to collect, analyze, integrate, and use the data in daily business decisions. Even if the company must buy or partner to access the right data, its ability to pair that external information with internal proprietary data can be a powerful source of competitive advantage over the long term.