Almost two centuries ago, Lewis Tappan and John M. Bradstreet illustrated the potential for turning data into a profitable product. At the time, businesses and merchants were expanding their operations and needed a reliable way to determine the creditworthiness of potential partners. Bankers and investors were eager for more consistent, objective information in this burgeoning economy to guide their lending and investment decisions. Tappan and Bradstreet established firms dedicated to collecting, analyzing, and selling data, along with the insights they derived from it. Their firms filled a critical gap in the market, eventually merging to form Dun & Bradstreet.
Fast-forward to today, when companies are often awash in data and trying to figure out if they can turn it into a business. The answer isn’t obvious. Building a data business is not for every institution, especially in a field where a few dominant players with massive advantages in data already exist. However, the potential rewards can be immense for companies that can unlock unique data, analytics, or organizational know-how to create a product that addresses an untapped market opportunity.
A European building-materials company identified a new-business opportunity with more than $500 million in enterprise value by turning an internal tool for tracking key performance indicators (KPIs) into a product it could sell externally. Similarly, a telecom company is on track to realize $200 million in new revenue in less than five years by using its data to build a digital lending business. And they’re not alone. McKinsey’s annual survey of business leaders on new-business building found that approximately 40 percent of them expect to create data, analytics, and AI-based in the next five years—the highest of any new-business building category.
How do you know if building a data business can create value for your organization? In this article, we share why now is the right time to consider it, how to assess whether it’s a good fit, and what the critical considerations are for getting started.
Why now
While leaders have sized up data businesses for over a decade, evolving technology capabilities and greater adoption worldwide of AI and analytics have increased the feasibility of monetizing data today. Four technology shifts, in particular, have enabled companies to create new data products faster and less expensively than ever:
Enhanced data-management efficiency: Companies can more efficiently process, manage, access, and reuse data in real time across different platforms thanks to greater sophistication of data tools and technologies. This efficiency is crucial for creating a scalable and sustainable data business.
Generative AI (gen AI): A few years ago, converting unstructured data, such as text, images, and videos, into a standardized form so it could be accessed and analyzed was prohibitively expensive for most companies. Gen AI has made structuring such data more cost-effective, enabling broader use. Combined with the emergence of low-code and no-code analytics platforms that democratize AI and analytics, data businesses can now derive more value from their data.
Increased access to real-world data: As Internet of Things (IoT) adoption accelerates, the costs and barriers associated with implementing sensor technology and capturing real-world data have significantly decreased. Companies can now gather real-world data more quickly and affordably and make it accessible to a broader range of applications.
Growing use of internal data products: Industry leaders are increasingly treating data like a product internally so that a given data set can support many different use cases (see sidebar, “What is a data product?”). This “data packaging” gives them a head start in monetizing their data.
What is a data product?
Additionally, we anticipate the thirst for data-driven decision making to intensify as leaders vie for their share of the up to $17.7 trillion in value potential from data and analytics.2 Add in another $2.6 trillion to $4.4 trillion from gen AI.3 This can create fertile ground for data and AI products. Consider Walmart Data Ventures, which launched a data solution to help suppliers better understand customers’ shopping behavior, among other insights. The company’s product, called Walmart Luminate, filled a gap in the market, enabling the company to achieve strong market adoption and 80 percent quarter-over-quarter growth during its first year.4
Because data businesses often require strong value propositions and unique data advantages to win, we expect a small group of data businesses to emerge and dominate industry-specific markets over the next decade. Those who come later may find it difficult to catch up.
Managing data security, privacy, and intellectual property rights
Data security, privacy, and ownership are significant concerns for any leader. But the potential impact these risks can have on a data company’s business models and ability to expand raises the stakes significantly. As a result, leaders should ensure that their business, technology, cyber, and legal teams collaborate often and early on assessing the opportunities.
Following are four issues that will require early attention:
• Understanding the rights you—and others—have related to data: What are the sources of your data—first parties, vendors, and so on—and how was the data acquired? Are there limits to how you may use the data or concerns about whether it is derived from underlying data sets that have issues (for example, training data for generative AI that may be copyrighted material)? Data businesses should assess their data and closely follow the evolving conversation over data rights, particularly as innovative technology collides with, and spurs, these conversations.
• Developing consistent data privacy principles at inception: Identifying how the business will collect, use, retain, delete, and protect personal data before products launch can shield data businesses from potential setbacks and time-consuming hurdles when introducing new products and features, as well as uphold trust with customers.
• Examining and tracking local laws: Varying country, regional, and sector laws may influence how a data business collects, shares, processes, stores, secures, and manages data. Additionally, some jurisdictions have more clearly defined regulations than others, which leads to greater predictability. Leaders will need to consider their appetite for the uncertainty and risk of operating in areas where regulations are not so clearly defined.
• Prioritizing data governance and security: This is typically the “weakest link” that prevents data businesses from scaling. Data governance and security capabilities, such as quickly identifying and resolving data issues and effectively managing data access and entitlements, are foundational to delivering a quality product to a growing user base.
Building a valuable data set and associated insights can take time, giving those that move first a sizable advantage in seizing untapped market opportunities. But institutions that enter this market should have a unique data set that addresses an unmet customer need and the right capabilities to scale their product. Those who do may not only build a scalable and profitable business but also potentially create an enduring brand.
