While today's organizations have access to previously unimaginable volumes of data—on consumers, clients, themselves—too few have managed to utilize this data in a way that drives tangible, outcomes-based improvements to their own operations.
In our view, this corporate inertia can be chalked up to a three-pronged misalignment. Resolving these issues will require making strategic changes that align the company's incentive and accountability structures, people and data sets.
Follow the money
You need to incentivize cross-team cooperation. Most enterprises are organized into teams, each tasked with either optimizing their own profit-and-loss statement or nurturing their own set of key performance indicators. In theory, this division of labor should drive efficiencies through specialization, but in practice, the end result often veers closer to Balkanization.
To avoid the pitfalls of P&L-oriented teams, enterprise stakeholders must detail the ways in which a de-siloed, data-driven approach will support the company's long-term customer-centric objectives. This is what the leadership team at luxury design marketplace 1stdibs did when the company evolved from an aggregated "storefront" lead-generation business, whose revenue depended almost exclusively on a Craigslist-like advertising model, to an e-commerce platform that leveraged centralized data as a single source of truth to successfully create and drive three distinct revenue streams.
Combat the fear of obsolescence
Aligning teams' accountability structures can cause anxiety among employees. It's natural for people to fear broad-based organizational change; they might fear becoming irrelevant, or not having the right skills, or losing authority premised on P&L-oriented domains. It's up to leadership to allay these fears.
One of the best ways to do this is to invest in teaching employees new skills. It will prevent fear-stricken employees from ignoring—or even actively undermining—mandated organizational changes; foster employee buy-in; and ultimately create a stronger, more productive workforce.
AT&T has taken this approach. After discovering that nearly half of its 250,000 employees lacked the technical skills to move the company forward, it invested $1 billion in Future Ready, according to a report from CNBC. The multiyear initiative includes online courses; partnerships with learning management systems and universities; and an internal career center. By 2020, the report said, AT&T plans to have retrained around 100,000 employees in a way that facilitates the company's ongoing evolution.
To be effective, developing new skill sets must be embedded within a larger organizational commitment to growth and learning, and a company culture that accommodates this cycle of self-improvement.
Encourage data sharing and integration
Even within a small organization, data scientists typically spend 80 percent of their time bringing disparate data sets together and only 20 percent of their time performing actual insights-producing analyses. At an enterprise scale, this gap tends to be even wider.
If teams either aren't willing or able to share their proprietary data sets with each other, the enterprise's analytics professionals will be forced to spend the bulk of their time searching for the information they need. Furthermore, inadequate data sharing opens the door to analytical inconsistencies that further reinforce organizational (and data) silos.
Bringing multiple internal narratives into alignment was the primary impetus behind Experian's recent organization-wide technological consolidation, according to a case study from software company Okta. For years, the credit reporting agency relied on six different identity management tools from five different providers, creating confusion and ample opportunity for operational inconsistency. Two-and-a-half years ago, the study says, Experian adopted a single cloud-based tool, and has since connected all of its internal systems, guaranteeing that each of its 16,000 employees will always be working with the same information as their colleagues. This consolidation has also saved the company more than $1 million a year, the study noted.
For many enterprises, emulating Experian's success will require changing the way stakeholders think about and use data: Enterprise leadership should make it abundantly clear that its intention is not to use analytics as a "report card," but as a "lesson plan."
In keeping with a self-improvement-friendly company culture, enterprises must frame data analytics as a tool that enables teams to understand and learn from their mistakes, and deploy those insights the next time around. When data becomes a mechanism of improvement rather than judgment, teams are far more likely to willingly share what information they have with their colleagues.
In the end, organizations that manage to seamlessly pair data-driven decision-making with enterprise scale will be well-positioned to dominate their industries for the foreseeable future.
