is the Chief Economist and Head of XLAB at Fabuwood Corp., an Adjunct Professor at New York University’s Tandon School of Engineering, and President of 1Ekaroni, a consulting and services company. He was formerly the Chief Data Officer of IBM Global Services and the Chief Data and Analytics Officer of Seattle Children’s Healthcare System. He has also co-founded three digital technology and healthcare startups.
is the General Manager for North America for Planck. He’s the former Chief Data Officer and Head of Information Management at AIG. Leandro holds an MBA from the Kellogg School of Management at Northwestern University, graduating
magna cum laude
, a graduate certificate in applied mathematics from Columbia University, and a B.Sc. in mechanical engineering from University of Sao Paulo, Brazil.
is the managing member of BLC Strategic Advisors. She previously served as the first Chief Data Officer for the State of New York, having led its successful open data initiative for Governor Andrew Cuomo. Prior to that, she was Executive Counsel/HHS Connect Data Interoperability Initiative under Mayor Bloomberg, as well as served in multiple leadership positions in NYS agencies and Office of the NYS Governor.
is the Chief Data Officer for the Commonwealth of Virginia. Prior to his appointment, Rivero served as Chief Data Officer and Chief Enterprise Architect for the U.S. Department of Transportation’s Federal Transit Administration in Washington, D.C.
Shortly after itsuse 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.