Emerging Technologies to Help Organizations Own Digital Responsibility and Data Ethics

Written by: Josh Daghir, Sr. Strategist, R/GA Ventures &
Peter Smith, Global Head of Data, R/GA

This article is featured in Kinesso’s Viewpoints publication, a thought leadership, and content platform, providing our most valued stakeholders with a unique perspective from our practice leaders and subject matter experts. See full report here.

The Business Case for Data Ethics

In the race for organizations to become more data-driven, a focus on data ethics can be lost. However, according to a double-blind survey conducted by Cisco, on average, every dollar invested in privacy returned $2.70 in benefit, with 70% of organizations reporting they receive significant business benefits from privacy initiatives, translating directly to operational efficiency, agility, and innovation. Companies that use data ethically win big, every day.

Ethics are a key factor in building consumer trust. The yearly Edelman Trust Barometer, which tracks public trust in various industries and governments, concluded that ethical drivers such as integrity, dependability, and purpose generate 76% of the trust capital of a business, while competence accounts for only 24%. Companies can hire the smartest data scientists in the world, but if they end up building data harvesting applications and experiences with opaque policies, they won’t win the hearts and minds of consumers — in fact, they’ll lose them.

Wall Street has also taken notice. The Global Reporting Initiative, which provides a framework for how to assess ESG investments, added consumer privacy to its reporting standards in 2018. Data shows that ESG funds have weathered the market dynamics of COVID-19 more successfully than conventional funds.

The case for ethical operations goes beyond compliance or goodwill — it’s about creating business resiliency.

The R/GA Ventures Approach

R/GA Ventures has turned to the startup ecosystem to seek out innovators in data ethics. The space is ripe with opportunity: the number of privacy tech vendors grew from 44 in 2017 to 304 at the start of 2020, according to the International Association of Privacy Professionals. Through the Data Venture Studio, R/GA Ventures is helping clients to identify key opportunities to implement digital responsibility across the data lifecycle by collaborating with emerging technology providers on not only privacy solutions, but also tools to enable increased transparency and a more equitable value exchange between brands and consumers. The newest and perhaps most interesting solutions fall into three broad themes that enable better, more innovative data use, while also building consumer trust and safety.

Protecting privacy

Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) allows companies to run computations and analytics against encrypted data without decrypting the data to analyze it. While this technique was first proposed in the 1970s, the immense computational requirements to process encrypted data made it impractical at scale. Today, optimizations to the method have allowed startups to implement commercial FHE, allowing organizations to secure their most sensitive data–even when it’s being used.

Differential Privacy

Differential privacy is a true anonymization method that protects identity by injecting random data (“noise”) into a data set. While this anonymization comes at the cost of accuracy, differential privacy is a technique that enables organizations to remain GDPR compliant without sacrificing valuable data. This is the technique that Google uses to collect anonymous user data in Chrome, and that the US government is implementing in the 2020 Census.

Enabling control

Personal Data Vaults

As marketers seek to capture explicit consent from consumers to use their data, some are turning to personal data vaults that allow users to grant permission for what, when, and how long their data can be used. Innovation in user experience is critical in this arena, as consumers are given unprecedented granularity in their control options. Startups are collaborating with brands to offer incentives that encourage data sharing, forcing marketers to carefully consider a fair value exchange.

While similar data can be sourced indirectly from data brokers and others, personal data vaults allow brands to capture it directly from consumers–so-called “zero party data.” With research by Experian suggesting that on average, 30% of a company’s data on a consumer is wrong (out of date, inaccurate, etc), offering tools like personal data vaults to capture zero party data can help to make marketers’ views of consumers more accurate and more complete.

DSAR Automation

Data Subject Access Request (“DSAR”) is a right enshrined in the GDPR and the CCPA that ensures that consumers can see and access the data that any company has on them. Yet this process is time and capital intensive, with one estimate pegging the average cost to an organization of one DSAR at $200. One contributor to this expense is that an organization must verify the identity of the person requesting the data, to make sure that they aren’t illegitimately using a DSAR to get information on someone else. Startups offering DSAR workflows are some of the highest-profile in this space, with privacy management company OneTrust becoming the first unicorn in the privacy tech space in July 2019.

Providing transparency

Data Mapping and PII Discovery

Hand-in-hand with DSAR automation is data mapping. As marketers and data teams pursue new data sources and experiment with new ways to use them, those tasked with ensuring that their organization is practicing digital responsibility often have little transparency into the full scope of their operations involving data. Data mapping solutions ensure that Data Protection Officers and others charged with ensuring the ethical use of data have complete visibility.

Explainable AI

Bias in AI, a well-researched phenomenon, is exacerbated by the fact that the AI often operates within a black box, offering no transparency into its decision-making process. To understand how we can guide AI to make more accurate and ethical decisions–and most critically, avoid discrimination or harm– we need to better understand how these algorithms operate. In late 2019, Google released a set of tools and frameworks under the label Explainable AI to help developers build interpretable and more inclusive AI models. According to Crunchbase data, 20 startups are now working on this challenge.

Ethical Companies are Winning Big, Everyday

As organizational imperatives shift to focus on privacy, solutions for digital responsibility and data ethics are moving from the theoretical space of academia to the investment spotlight. Most notably, these solutions are already being successfully piloted and put into production by the world’s leading brands and agencies.

The R/GA Data Venture Studio maintains a database of 400+ startups building solutions in these areas and others, ranging from seed stage to unicorns. To learn which companies can help your organization implement more ethical data practices, email us at ventures@rga.com for more information.