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.
Insight-killer #2: “Data strategy is too slow.”
Trustworthy data leaders should consider which action provides the most value, adjusting for speed and risk. Prioritization helps address the critique posed by 62% of surveyed technology and business teams, which found their data groups work too slowly.
Consider the prioritization strategy of one high-growth legaltech startup to advance both data defense and offense. Data strategists at this legaltech startup initially placed comprehensive risk mitigation above project speed and benefit. For instance, they tried to tackle dozens of data analytics projects and identify all possible risks in one go. The burdens on its partners were immense, who needed to source a number of data projects, stalling the project. Furthermore, new iterations were delayed due to the scale and complexity of the projects involved.
However, when the startup built off one pre-existing, validated customer analytics data project, the project moved forward. It did what agile product expert Matt LeMay calls “scouting and scaling.” Neither a new project had to be sourced nor did new data had to be collected. Instead, marketing and customer success leaders got excited about combining their existing datasets with product metrics data already in the existing data project. The project owner sought to prioritize customer needs and product features better, furthering data offense and customer-centricity.
Yet to meet defensive goals, data strategists could not give anyone access to sensitive customer information. To address this legal risk without making the project too unwieldy, the strategists started with a scoped data policy that only controlled a few datasets, which reduced both the complexity and risk of the project. In particular, the policy allowed internal analysts to access data automatically when they met predefined access rules relating to their function and when they limited their use of data to the less-risky purpose of internal customer analytics. This was faster than an alternative proposal where analysts would have to request access to the data and receive manual approval. After the startup validated the utility of this policy, its no-code architecture could allow it to improve and scale the policy to more datasets, without significant technical and governance staffing.
Ultimately, prioritizing one project allowed the legaltech startup to advance not just privacy, a core defensive goal, but also an understanding of customer needs, a core offensive goal.
Insight-killer #3: “Data strategy makes my workflow more difficult.”
Finally, trustworthy data leaders should consider what their partners enjoy and hate about their current workflow. 46% of technology and business teams find their data groups too difficult to work with. When data strategy becomes unbearably difficult, other teams will circumvent these rules to do their work, multiplying risks to customers and organizations.
To deal with problems like these, HotelTonight developed a data workflow to help multiple partners collaborate better, furthering both defensive and offensive goals. HotelTonight’s Chief Data and Strategy Officer diagnosed that the company’s data strategy was hampered by “the same problem you have in product” — diverse functional partners with different opinions about what should be prioritized. While this diversity of thought is core to offensive data strategies like product innovation, without appropriate management, misalignment leads to shifting priorities, confused workflows, wasted resources— and distrust. For the data team, this distrust ultimately meant that its internal partners “questioned its [data’s] accuracy, a core component of data defense.
To address these issues, HotelTonight’s chief data officer developed a workflow that would make partners’ lives easier, not harder. First, she ensured one person owned the data product and process, fostering clear accountability to drive improvements. With a clear line of ownership in place, the data strategy team then sharply defined goals and metrics, fashioning shared vocabulary around offensive metrics like “revenue” and “website visit.” Armed with accountability and a north star, she built a workflow that brought in partners at the right time to use their time effectively. The first priority was accuracy; the quality assurance team would now validate the quality of the data as it was ingested. Next, her team developed workflows to prioritize new issue backlogs and loop in teams responsible for technical solutions and user needs.
Her work paid off. Now, even if internal partners may not like the data, “no one” asks her team if the data is accurate.
Ultimately, by diagnosing problems in its data workflow, HotelTonight’s chief data strategist improved accuracy, a key defensive issue, and an understanding of how to advance offensive goals like revenue.
Unfortunately, only 23% of companies commit resources to adoption-oriented workflow testing and learning like HotelTonight’s, which McKinsey found is common to 90% of successful data programs. The best data strategies learn and evolve.
Ultimately, move fast and uplift your employees and customers.
At its heart, a successful data strategy requires organizational change. Leaders can accelerate change by drawing from the stories and emotions of their employees and customers.
Take the experience of Dr. Reddy’s, one pharmaceutical company, to become more innovative and patient-centered. The CEO started by listening to the stories of diverse employees across the company, from janitorial staff to scientists. His questions asked what mattered most to them. Synthesizing these stories, the CEO developed a new motto: “Good health can’t wait.”
A new brand identity based on this motto unified the CEO’s efforts to bolster data-driven innovations serving Dr. Reddy’s customers. The company prioritized projects that would lead to quick wins, so that employees could sense how they were serving their patients and improving their health. This service “gives meaning to work, conjures individual emotion, and incites collective action,” argues IDEO’s managing director Bryan Walker and Stanford Business Professor Sarah Soule.
Moving forward, third-wave innovators can also anchor their data strategies to a mission that serves others, drawn from their own employee and customer stories. Whether they are improving the highly-regulated fields of healthcare, finance, or housing, these innovators can use data strategies that protect, fight for, and empower critical, but easily-forgotten, stakeholders, like users and workers.
To guide how leaders can apply these lessons to their initiatives, request access to the data strategy template here. This template helps leaders identify key problems, sharpen them, and identify methods to create cross-functional support for trustworthy, yet customer-centric data strategy.
Moving forward, data leaders must learn from Zoom — and not become victims of their own successes. Zoom’s quick response to pause the development of new features and instead prioritize privacy and security issues illustrated agile data leadership.
To avoid common data insight killers, leaders can ensure their strategies identify key needs, prioritize, make their partners’ workflows better — and ultimately serve customers.
Don’t move fast and break things.
Move fast and uplift people.