Daniel Wu Contributor Dan Wu is a Privacy Counsel & Legal Engineer at Immuta, an automated data governance platform for analytics. He’s advocated for data ethics, inclusive urban innovation, and diversity in TechCrunch, Harvard Business Review, and FastCompany. He’s helped Fortune 500 companies, governments, and startups with ethical & agile data strategies. He holds a…
Shortly after its use exploded in the post-office world of COVID-19, Zoom was banned by a variety of private and public actors, including SpaceX and the government of Taiwan. Critics allege its data strategy, particularly its privacy and security measures, were insufficiently robust, especially putting vulnerable populations, like children, at risk. NYC’s Department of Education, for instance, mandated teachers switch to alternative platforms like Microsoft Teams.
This isn’t a problem specific to Zoom. Other technology giants, from Alphabet, Apple to Facebook, have struggled with these strategic data issues, despite wielding armies of lawyers and data engineers, and have overcome them.
To remedy this, data leaders cannot stop at identifying how to improve their revenue-generating functions with data, what the former Chief Data Officer of AIG (one of our co-authors) calls “offensive” data strategy. Data leaders also protect, fight for, and empower their key partners, like users and employees, or promote “defensive” data strategy. Data offense and defense are core to trustworthy data-driven products.
While these data issues apply to most organizations, highly-regulated innovators in industries with large social impact (the “third wave”) must pay special attention. As Steve Case and the World Economic Forum articulate, the next phase of innovation will center on industries that merge the digital and the physical worlds, affecting the most intimate aspects of our lives. As a result, companies that balance insight and trust well, Boston Consulting group predicts, will be the new winners.
Drawing from our work across the public, corporate, and startup worlds, we identify a few “insight killers” — then identify the trustworthy alternative. While trustworthy data strategy should involve end users and other groups outside the company as discussed here, the lessons below focus on the complexities of partnering within organizations, which deserve attention in their own right.
Insight-killer #1: “Data strategy adds no value to my life.”
From the beginning of a data project, a trustworthy data leader asks, “Who are our partners and what prevents them from achieving their goals?” In other words: listen. This question can help identify the unmet needs of the 46% of surveyed technology and business teams who found their data groups have little value to offer them.
Putting this to action is the data leader of one highly-regulated AI health startup — Cognoa — who listened to tensions between its defensive and offensive data functions. Cognoa’s Chief AI Officer identified how healthcare data laws, like the Health Insurance Portability and Accountability Act, resulted in friction between his key partners: compliance officers and machine learning engineers. Compliance officers needed to protect end users’ privacy while data and machine learning engineers wanted faster access to data.
To meet these multifaceted goals, Cognoa first scoped down its solution by prioritizing its highest-risk databases. It then connected all of those databases using a single access-and-control layer.
This redesign satisfied its compliance officers because Cognoa’s engineers could then only access health data based on strict policy rules informed by healthcare data regulations. Furthermore, since these rules could be configured and transparently explained without code, it bridged communication gaps between its data and compliance roles. Its engineers were also elated because they no longer had to wait as long to receive privacy-protected copies.
Because its data leader started by listening to the struggles of its two key partners, Cognoa met both its defensive and offensive goals.