Three reasons for data-driven optimism

By Bryan Kirschner, Vice President, Strategy at DataStax

Doing digital, data-driven business is a nearly universal goal. Ninety-five percent of U.S. companies we surveyed* in October were in some stage of digital transformation and intentional progress toward a data-driven enterprise.

There are reasons this might keep executives, policymakers, or workers up at night. Digital platform competition is a domain where the big tend to get bigger. Artificial intelligence (AI) is on pace to affect millions of jobs: it’s humane to be mindful of the over-under between “enhance or create” versus “replace.” Research has documented increasing risks from falling behind.

And for many, the coronavirus pandemic has exacerbated uncertainty about what lies ahead.

But as we’ve talked with CIOs and CDOs about how they’re adapting and making progress, and then collected data to validate replicable best practices, we’ve found reasons to be hopeful. Three patterns in particular stand out.

New ways of working are a win. Early in the pandemic, shifting to remote work and online collaboration was necessary for business continuity. We are now seeing additional benefits. Three out of four companies now say they have increased their investment in digital transformation. Fully two-thirds of respondents reported increases in productivity and the pace of innovation (68 percent and 69 percent, respectively).

Some CIOs told me they worry that gains may have come at the expense of work-life balance and employee well-being. Mindfulness of managing the downsides now and into recovery is encouraging, and data suggests there’s a proven pattern for “doing well by doing right” by employees.

A longitudinal study of Glassdoor ratings found a jump in employee ratings of honesty and integrity at many companies in April. And a comparison of the companies with the biggest gains in ratings of culture versus those with the biggest losses shows that the leaders excelled at not just communication but also tending to employee welfare and agility.

Leaders are focused on generative innovation. Applying domain knowledge to core processes or critical customer experiences—rather than selling your data—is arguably the best path to creating breakthroughs with data and analytics.

Our data backs this up. Among companies attributing more than 20 percent of revenue to data and analytics, nearly six times as many rate increasing efficiency as a top priority versus selling data to third parties. Seven times as many rate creating new products as a top priority.

I’ve heard repeatedly that helping with a “next best step” is a tractable, broadly relevant AI use case. In scenarios like healthcare, retirement planning, and education, greater efficiency and smarter customer journeys hold the promise of driving growth through better outcomes.

Expanding opportunity is a winning talent strategy. I’ve heard two purposeful and encouraging patterns. One is a playbook for college hiring. Some organizations deal with the challenge of competing for scarce analytics talent with tech companies by going out of their way to hire a few senior roles at a premium, using them as the nucleus of a larger team staffed through aggressive college hiring.

The other is reskilling. For some, investing in making data access easier opens the door to retraining staff with Excel macro skills in Python or low- and no-code tools.

Bringing fresh perspectives into established organizations and giving incumbent employees renewed relevance for “what comes next” look to be constructive paths on the data-driven journey.

Read about how to corral your data here.

*DataStax and ClearPath Strategies surveyed 515 executives, managers, and technical practitioners in U.S. companies during October, 2020.

About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.