Estimating ROI of Data
Sharing for Enterprises

Organizations engage in data sharing to enhance data analytics and artificial intelligence efforts, to innovate, to create competitive advantage or to collaborate for economic or social benefit.

More and more, organizations are using their own data to power use cases like hackathons, vendor evaluation, commercialization of data-driven products and customer record matching; joining data with partner data for smarter marketing programs or more personalized customer experiences. But often it is difficult to estimate the return on investment (ROI) of these data sharing initiatives.

Before an enterprise organization decides to engage in data sharing, they need to figure out why they’re doing it and what success looks like. That is, what value are they expecting from their investment.

The value derived for organizations can be both quantitative or qualitative and organizations should consider all use case outcomes as potential sources of value and combine them to estimate the total ‘return’. To calculate the associated investment, enterprises typically look at people, technology infrastructure and time costs. These days enterprises often leverage purpose-built data sharing software to make the sharing of data safer and simpler. These software solutions are powerful, but the value is realised in how they are used.

There are many approaches to calculate return on investment, let’s explore some common ways organizations determine ROI.

Quantitative benefits are returns that can be measured, including:

Increase in revenue

Cost savings

Efficiency savings

Whereas, qualitative benefits include variables that are often observed rather than measured, including:

Brand reputation

Risk mitigation

Customer experience

Quantitative Benefits

Increase in revenue

The commercialization of new data-driven data products is a revenue generating use case. The revenue generated can be variable, often ranging from ~US$3.5k- $48k per product per end user, with scope to scale revenue to the size and value creation opportunity of the data product end user. The size of the opportunity is only limited by the size of the total addressable market.

Geografia, a consultancy specializing in demography, economics and spatial planning, utilized the Data Republic software to build the data product Spendmapp, which leverages transactional credit card data to assist in economic decision making for local governments. Spendmapp is a subscription based service sold to local governments for up to US$25k /annum, but with a total addressable market of >500 councils, the revenue target can reasonably be in the millions of dollars, representing a potential 10X ROI based on typical infrastructure costs, including Data Republic licensing fees.

Key factors in reducing resource requirements:

Standardized Legal frameworks

Standardized Governance protocols

Repeatable and streamlined workflows

Scalable usage (only capped by licensing terms rather than technical limitations that may exist)

Comprehensive training materials

Ongoing support for users

A Top Tier Health Insurer has truly scaled their innovation efforts, with 500+ Guests and 3X ecosystem growth YoY. What’s more they have achieved this with ⅕ of their original resourcing.

This rich ecosystem of developers and 3rd party technology companies has seen the insurer lift productivity by 95% with 1,000,000+ data science minutes logged (equivalent to ~9 FTEs).

Efficiency savings

To innovate faster, organizations need to reduce the time it takes to set up their data sharing use case, or improve use case productivity.

Data sharing use cases that involve a technology vendor are often associated with lengthy onboarding times due to drawn-outlegal negotiations and complex technology integrations.

Data Republic’s Data Sandbox gives data owners the confidence to trial new vendors without the need for data surrender or vendor implementation within the data owner’s environment. On average, the use of the Data Sandbox reduces vendor onboarding time by 93%, from an average of 18 months ‘go-live’ to 4 weeks.

One data sharing use case that is typically considered to have a material impact on productivity is hackathons; where teams of data scientists work intensely on a problem over a short period of time.

The HyperHack Hackathon, run by Temasek over a 2 day event, saw 52 hours of hacking from 60 participants, equivalent to ~2 FTEs over 9 months.

Qualitative benefits

Qualitative benefits, though observable, are hard to measure and can often be overlooked, but can still have a massive impact on an organization.

Brand reputation

Data sharing can enable social good; helping to address important social issues such as economic growth, disaster response, financial crime, and social welfare. Done safely, these use cases can have a significant impact on brand reputation; attracting more customers at a lower cost per acquisition and increasing customer retention.

Risk mitigation

Risk mitigation for an enterprise involves taking proactive action to avoid or reduce risk.

Data sharing, in a safe way, can reduce both:compliance risk and business risk.

Compliance risk

Data sharing via an intermediary removes the need for data surrender and provides a standard channel for data externalization, allowing for monitoring and controls that help to prevent data leaks and reduce the risk of data misuse that could result in heavy fines.

Legal frameworks and trusted security measures that come with purpose-built data sharing software better mitigate this risk than an untested new build, as well as managing liability of data leakages across involved parties and providing neutrality in data and IP hosting responsibilities.

Governance controls, independent audit logs and privacy preserving techniques leveraged by organizations enables easier regulation adherence, evidence of compliance and the ability to adapt to emerging changes to regulation such as the GDPR.

Business risk

The outcomes, rather than the chosen method, of data sharing can also play an important role in risk mitigation. Matching data between organizations, in a privacy preserving and secure way, can provide the additional data points on customers that are necessary to streamline business operations such as underwriting, fraud detection, and credit risk modelling.

Mitigating the risk of compliance errors and fraudulent activity for an enterprise can save millions of dollars, potential 10X ROI based on typical Data Republic licensing fees.

Customer experience

Matching and combining data at an individual level enables organizations access to an enriched profile of their customers. This can be used for product personalisation and targeted messaging, optimising ROMI (return on marketing investment).

Enhanced customer profiles also enables the development of new products based on increased knowledge of customer needs and preferences, and the additional intelligence can generate more cross-sell and upsell opportunities to boost sales and revenue.

So although revenue is the simplest way to calculate the value derived for an organization, not all use cases are revenue generating.

Estimating an organization’s return on investment needs the benefits to be evaluated holistically, considering both quantitative and qualitative measures, with the latter also often resulting in cost savings or efficiency savings down the track.

Enabling organizations to scale their efforts is key to improving ROI, with the magnitude of expected gains increasing as more use cases are carried out.

Getting started however can be the biggest hurdle and too often organizations don’t consider the cost of doing nothing. The ability to innovate and continue to innovate over time plays a critical role in staying ahead of competitors and ensuring enterprises can thrive in the long run.

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About the Author

Imy Briscoe

Imy works within Data Republic’s Customer Success team, working with enterprise clients to enable data collaboration and innovation opportunities. With end-to-end project delivery experience, Imy can advise organisations on data sharing best practice, including legal frameworks, governance protocols, solution design and analytical requirements.

Estimating ROI of Data Sharing for Enterprises