As enterprises embrace accelerated digital transformation, new ways of sharing data across organizations without compromising privacy have emerged.
As enterprises continue to embrace accelerated technology transformation in response to the disruption presented by COVID-19, some have emerged as digital vanguards, finding new and creative ways to put emerging tech to work. At the center of their pioneering success are innovative ways of sharing data across and between organizations without compromising privacy.
As COVID-19 turns toward the endemic phase, continuing challenges echo throughout the global economy. Persistent labor and resource shortages, hybrid work models, an explosion of new devices, and supply chain disruptions, to name a few, require new solutions.
The 13th annual Deloitte Tech Trends Report, Data Sharing Made Easy, shows that pioneering organizations are navigating this volatility by automating, abstracting, and outsourcing many of their historically in-house capabilities to the cloud. Nowhere is this shift more pronounced than in the ascendance of cloud-based data platforms.
Effective data management is at the core of success in this disrupted marketplace. One trend in Deloitte’s Tech Trends Report explains how new technologies are giving rise to advanced business models by simplifying the mechanics of data sharing across and between organizations – without compromising privacy.
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Unlocking the Possibilities of Shared Data
Making innovative use of shared data isn’t a pipe dream. For advanced organizations, this is reality.
For example, when COVID-19 vaccines became widely available in the spring of 2021, CVS Health (CVSH) leveraged external data from vaccine suppliers and the Centers for Disease Control and Prevention (CDC) to forecast supply and demand.
The team established governance immediately to prioritize data protection and compliance with privacy and data security laws and then fed this information into internal systems that enabled patients to schedule appointments, partners to set up clinics, and analysts to measure campaign effectiveness.
The team also shared data externally with research agencies and universities to help gauge vaccination rates in the population. As the vaccine rollout continued, CVSH used demographic and demand data to identify underserved areas to facilitate access to vaccines where they were most needed.
Connecting more easily with existing partners is only part of the story. Cloud-native data platforms also encourage organizations to seek out and leverage external data that has been traditionally out-of-scope or otherwise off-limits to open a new arena of data-driven opportunities.
For example, industry data marketplaces can allow otherwise fierce competitors to resolve common challenges through collaboration. Consider banks in developing regions: Together, they could pool anonymized credit data to build an interbank credit risk model, unlocking new insights and opportunities to the shared benefit of all.
In the same vein, many manufacturers and retailers already purchase consumer data from third-party data brokers, but that data is often low-quality or too limited to make a significant business impact. Shared high-quality data between willing parties can allow every partner in the value chain, from suppliers to manufacturers to marketers, to pool customer data and create a higher-resolution picture of demand.
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Acquiring External Data Can Be Easy and Valuable
The recent proliferation of cloud-based data-sharing platforms allow organizations to buy and sell data more easily than in years past. The data sharing-as-a-service model allows subscribers to manage, curate, and tailor data and aggregate and sell access to that data to other subscribers.
These models have already succeeded in music streaming and social media, where vendors provide easy-to-use platforms and customers provide content for sharing. Similar systems are at hand for businesses, and the data marketplace sector is in the midst of a “gold rush” as startups join incumbents to stake their claims on the marketplace.
This model is promising and has driven a significant surge in demand for high-quality, externally sourced data. No longer just a tool to inform high-level decision-making, data is increasingly being considered a business-critical asset that can be explicitly valued and monetized. This sea change underscores the need for savvy companies to explore what data marketplaces might be able to do for their bottom lines.
Still, despite the frenetic acceleration in this space, it remains early days. Governance, security, and pricing models continue to evolve as the business and technology community iterate in response to supply and demand. That said, as new participants continue to join the fray, the volume, variety, and value of these emerging data marketplaces only stands to grow.
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Keeping Privacy Issues in Check
Positive projections aside, every emerging technology trend carries the potential for risk. Privacy policies; competitive secrecy; and myriad safety, security, and governance concerns have historically hampered companies’ ability to share their data openly.
Enter a new class of computational approaches known together as privacy-preserving computing, which can make it possible to reap the benefits of data sharing without compromising discretion.
Emerging privacy-preserving techniques are complex and multifaceted:
- Fully homomorphic encryption allows encrypted data to be shared and analyzed, without first decrypting it.
- Zero-knowledge proofs enable users to prove their knowledge of a value without having to reveal the value itself.
- The federated analysis technique allows companies to share insights from their analysis without sharing the data itself.
- Differential privacy adds noise to datasets, making it impossible to reverse-engineer the original inputs.
At their core, each of these techniques, and others like them, enable rich collaboration without compromising competitive secrecy or data privacy. This “best of both worlds” approach can help mitigate data security risks and earn buy-in from customers, partners, and other stakeholders alike.
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How to Take the Next Step
What does this mean for businesses moving forward? First, enterprise leaders must stay focused on today’s data management initiatives — even those that can’t be solved by data sharing. Strong data governance, quality, and metadata initiatives, for example, are still essential hygiene for success in the modern marketplace.
Second, technology and business leaders alike must recognize that these new tools and approaches, while potentially disruptive, won’t change their organizational culture overnight. Companies of all sizes have deeply entrenched processes and standards for managing and accessing data.
Established companies may have strict, fixed practices, while startups and digital natives may presume a more relaxed approach. Some businesses may be less willing to share data, or be inherently wary, even with anonymized data. Others may need to take a long look in the mirror and adapt accordingly to overcome these cultural challenges.
Still, these aren’t reasons to avoid exploring and, when ready, embracing the trend. Organizations at the vanguard are already reaping benefits and leaving their less-aware and/or less-prepared competitors in the dust. Enterprises in every industry have an increasingly liquid asset at the ready. Now might be the time to make the most of it.
About the Authors:
Mike Bechtel, Chief Futurist, and Nitin Mittal, AI Leader, at Deloitte Consulting LLP
