Thanks to advances in data-sharing technologies, you can buy and sell potentially valuable information assets in highly efficient, cloud-based marketplaces. Combine this data with a new array of privacy-preserving technologies, such as fully homomorphic encryption (FHE) and differential privacy, and you can now share encrypted data and perform computations on it without having to decrypt it first. This provides the best of all potential worlds: sharing data while preserving security and privacy.
All of this has fueled a promising new trend. Stores of sensitive data lying fallow in servers around the globe due to privacy or regulatory concerns are starting to generate value across enterprises in the form of new business models and opportunities. During the next 18 to 24 months, we expect to see more organizations explore opportunities to create seamless, secure data-sharing capabilities that can help them monetize their own information assets and accomplish business goals using other people’s data.
Though currently in an early stage, this data-sharing trend is picking up steam. In a recent survey, Forrester Research found that more than 70% of global data and analytics decision-makers are expanding their ability to use external data, and another 17% plan to do so within the next 12 months.1
Moreover, the global FHE market alone is growing at an annual rate of 7.5% and is expected to reach US$437 million in value by 2028. Currently, the health care and finance sectors are leading most FHE explorations.2
What accounts for this growth? Simply put, data gains value when it is shared. Gartner™ predicts that by 2023, organizations that promote data-sharing will outperform their peers in most business metrics.3
Consider the following examples of data-sharing in action:
- Using aggregated data to securely achieve common goals. Organizations can work with “frenemies” within a market sector to achieve common goals such as developing deeper customer insights or detecting fraud patterns across an entire sector.
- Increasing efficiency and lowering costs. Across enterprises, data vendors no longer have to provision hardware, maintain databases, and build application programming interfaces (APIs). Customers can push a button to access anonymized, curated data feeds. Within the enterprise, encrypted data makes artificial intelligence (AI) and machine learning (ML) exercises safer, and compliance audits easier.
- Broadening your research collaboration. Sharing basic foundational or early-stage findings can accelerate critical research initiatives without compromising a hard-won competitive advantage.
- Securing intellectual property. Super-sensitive data such as AI training data that may be stored in public clouds can be better protected.
- Encrypting data in motion. In the arenas of high-frequency trading, robotic surgery, and smart factory manufacturing, confidential data flows rapidly across multiple entities. FHE allows users to access critical data quickly without encryption keys.
Opportunities like these to monetize data through sharing and pooling can offer a competitive advantage for first movers—a motivating concern these days across markets. It is not uncommon for new participants in data-sharing ecosystems to experience what has been described as an “oh, sweet Lord moment” upon realizing that their competitors operating on the same platform are doing much more with data assets. In this moment, many resolve to become the best AI- and data-driven organization possible.
Share and share alike
As the lifeblood of digital transformation, data looms large in Deloitte’s Tech Trends reports. In Tech Trends 2021, for example, we discussed how in order to realize their MLOps ambitions, companies must manage their data very differently.4 Today, the data-sharing revolution is making it possible for organizations to access more data, more securely within their own ecosystem and across other organizations. But, once again, reaching this potential requires managing data differently—this time adding innovative technologies and techniques that free information assets from traditional privacy and security restrictions.
This year’s data trend comprises three major dimensions: opportunity, ease of use, and privacy.
Share and thrive: The promise of new business models and opportunities
Shared data can create shared opportunities and new business models. As the data-sharing trend advances, we expect more organizations to engage in “data collaboration” to tackle common challenges and pursue mutually beneficial revenue, operational, and research opportunities. Moreover, the ability to share data safely with external data management service providers can help organizations streamline data management processes and lower related costs. Consider the following opportunities data-sharing can drive:
- Industry vertical marketplaces. Even the fiercest of competitors often share common challenges that are best resolved through collaboration. Take suppliers in the food industry: If they all anonymized sensitive sales and delivery data and pooled it together for analysis, perhaps they could unlock the mystery of supply and demand. Or banks in developing regions could pool anonymized credit data to build an interbank credit risk scoring system. Or one of the biggest opportunities of all: Could pharmaceutical researchers and doctors operating within a secured ecosystem pool data to understand how to bring life-saving innovations to market more quickly?
- Partners in a value chain. Many manufacturers and retailers purchase consumer data from third-party data brokers, but as is often the case, there is not enough quality data to really make an impact. What if systems of partners within a value chain—from suppliers to manufacturers to marketers—pooled their customer data to create a more nuanced picture of demand?
- Let somebody else do the AI model training. AI models are often considered highly sensitive forms of intellectual property. Because they can typically fit on a thumb drive, they also represent high security risks, so many organizations have traditionally performed their own modeling in-house. Thanks to encryption technologies, this may be about to change. With modeling data secured, chief data officers can safely outsource AI modeling and training to third parties.
- Data providers streamline deliveries. On data-sharing platforms, buying access to real-time market or logistics data is as simple as pushing a button. Data providers will no longer need to provide APIs or ship files.
Acquire external data easily at the push of a button
Cloud-based data-sharing platforms are helping organizations seamlessly share, buy, and sell data. These heavily virtualized, high-performance data marketplaces are typically structured in a data-sharing-as-a-service model in which, for a fee, service subscribers can manage, curate, and tailor data. They can also secure their data to a degree by using platform-provided “clean rooms,” safe spaces with defined guidelines where organizations can pool their data assets for analysis. Finally, subscribers can aggregate and sell access to their data to other subscribers. Data buyers get à la carte or custom views into different aspects of markets, products, or research.
The fundamental business strategy underpinning this “sharing-as-a-service” model has already demonstrated its effectiveness in other high-profile information and content-sharing arenas such as music file-sharing and social media. In these, a vendor provides an easy-to-use data-sharing platform, and customers provide the content (data).5
The data marketplace sector is currently in an early gold rush phase, with startups such as Databricks, Datarade, Dawex, and Snowflake, and hyperscale cloud providers such as AWS, Azure, Google, and Salesforce racing to stake their claims in this promising market. And promising it is: The nexus of data growth and democratization, along with digital transformation, is helping create a revolution in which demand for external data is skyrocketing.6 No longer merely a tool for informing executive decision-making, data is now a business-critical asset to be sold, bought, traded, and shared. And the platform that facilitates this exchange most easily and effectively could eventually become the standard for data-sharing in industry data verticals or even across entire markets.
We’re seeing data-sharing use cases—and in some areas, success stories—proliferate as more organizations begin pursuing opportunities to monetize and expand their data assets. For example:
- During the early days of the COVID-19 pandemic, fiercely competitive global pharmaceutical firms explored ways to share pre-clinical research data via data-sharing platforms.7
- COVID-19 vaccine administrators used centralized state-operated platforms to share daily micro-level vaccination and testing data with public health care agencies.8
- Investment managers at a global financial services firm capture and analyze data from their back, middle, and front offices in real time. As a result, the time required to begin sharing investment data with clients shrinks from “months to minutes.”9
It remains to be seen how certain aspects of the data-sharing platform market will evolve. While there will eventually be some consolidation and standardization, multiple platform markets could also take root. For example, there could be systems of partners in private data marketplaces, or perhaps public marketplaces targeting unique needs will spring up organically. Whatever shape data marketplaces eventually take, we anticipate that the gold rush will continue to pick up steam, particularly as vendors develop ironclad security and more organizations sign up for these platforms, thus expanding the volume of external data available for consumption.
Share data without compromising privacy
Data gains value when we share it. Yet data privacy policies and competitive secrecy demands have historically placed a damper on our ability to realize this value. Today, a new class of computational approaches collectively known as privacy-preserving computing (or confidential computing) is poised to liberate organizations and their data from privacy’s shackles. Approaches such as FHE, differential privacy, and functional encryption make it possible for organizations to reap the benefits of data-sharing without sacrificing privacy (figure 1).