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By Bryan Kirschner
By Bryan Kirschner, Vice President, Strategy at DataStax
I’m enthusiastic about the science of finding “next best steps.” As a consumer, that includes applauding companies that aspire to deliver recommendations as consistently on-point as Spotify or Netflix.
As a strategist, I’m a fan of the pioneering work of Dr. Nicole Forsgren on data-driven recommendations for progress toward DevOps excellence that she led at DevOps Research and Assessment (“DORA,” now part of Google Cloud).
In 2020, we took inspiration from that and brought data science to bear on finding “next best steps” toward success as a data-driven enterprise.
A cluster analysis of 72 patterns, practices, and attributes of over 500 companies paints a picture of where companies are on their journey–and surfaces some potentially high-leverage moves.*
Here’s an overview of what we found, with more to come in 2021.
Starting. One in ten U.S. companies are just starting their data-driven journey. While nearly all (94 percent) of respondents from these organizations report they are beyond the “planning” stage, the jury remains out on whether it’s a true priority. Fewer than a third agree that data strategy has buy-in from corporate leadership, and barely more (38 percent) believe it is well-connected to overall corporate strategy.
Scaling. One quarter is in a stage we describe as “scaling.” Respondents from these organizations are no more likely to characterize their company as having a data strategy, but they express more conviction that it matters. Compared to those just starting, twice as many believe the strategy has leadership buy-in. The presence of a data management office jumps from 35 percent to 61 percent of organizations scaling up.
Tackling Culture. The largest single group of companies (31 percent) report wrestling with creating a data-driven culture as a bigger challenge than technology by a two-to-one margin. In comparison to the previous two clusters, they have made significant progress on data portability, scalability, and quality. But translating it into material impact on the business lags: they are about half as likely to attribute more than 10 percent of revenue to data and analytics than those Tackling Tech or Leading, respectively.
Tackling Tech. We heard an interesting variation on the data-driven enterprise journey in conversation with some executives. Their companies had made strides on both culture and business impact, but now faced some heavy lifting in IT. In one case, growth through acquisitions required platform rationalization in order to get an end-to-end view of customer journeys. In another, the next wave of high-impact projects implicated mainframe migration. This shows up in our data as a group of organizations that are neck-and-neck with the leaders at generating material revenue from data and analytics, but–in contrast with every other cluster–identify technology as a bigger challenge than cultural change to further progress.
Leading. One in four respondents from the companies furthest along on their journey attribute 20 percent or more of revenue to data and analytics. How they have achieved this suggests some “unlocks” for others. Respondents gave Leaders a 10-point or greater advantage in strong agreement these characteristics describe the organization on the following characteristics:
On technology, “we’ve optimized for data velocity” and “we’re engineering the modern data stack.”
On culture, “we have clear owners for data products” and “data governance is organized by business domain”
And, on strategy, we’ve “increased our pace of innovation” as a result of adapting to COVID-19 and “we’re increasing our use of open source software.”
*This analysis is based on a survey of 515 executives and technical practitioners in the U.S. conducted in October 2020 by DataStax and ClearPath Strategies.
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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.