Connecting existing studies on the status quo and relevance of smart data governance.
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In our introductory blogpost we gave an overview of what we want to achieve: taking you with us on our fact-finding journey on both sides of the Atlantic on how data governance is linked to business success.
To be able to make a meaningful contribution to the ongoing research and debate in this field, we started to screen the already existing knowledge on data governance and its impact for businesses. To begin with, we focused on grey-literature (everything besides traditional commercial or academic publishing) and screened it for common insights, potential disagreements between studies and findings with regards to necessary or potentially interesting further research.
In the following we provide insights into some of our findings and into our follow-up questions, which we will extend with detailed evaluations of the studies in the coming weeks.
The most mentioned reason for the relevance of data governance is trust. The report by the British Academy and the Royal Society on “Data Management and Use: Governance in the 21st century” emphasizes the internal and external trust in the overall management of data, while the study of Drexel University in Philadelphia emphasizes improved data quality which leads to trust in their data for confident decision-making as key goal and outcome of data governance. This trust is closely related to the compliance with legal regulations and the goal to minimize risk of security breaches, emphasized by all analyzed studies.
A second frequently mentioned reason to implement a data governance program is its importance to enable business outcomes. Of all organizations surveyed, 80% mentioned this as a key-driver in a study by Gartner “The State of Data and Analytics Governance is Worse Than You Think”. We found a difference between US-based studies and EU/Germany-based studies with regards to the understanding of enabling business outcomes. While The EU’s “Survey of Businesses on the Data Economy 2022” and a study by the European industry association bitkom “Datenökonomie – wo steht die deutsche Wirtschaft?” find that the major focus lies on improving internal operations of the business and efficiency gains, the US-based studies emphasize the development of new business models (with data) as key driver for implementing a data governance program. The EU study conceptualizes these findings in the difference between data-enabled businesses vs. data-enhanced businesses.
What are the challenges around using data?
In their survey, the EU investigated to which degree companies store, analyze, and use their data. Over 31% of companies that store data report that they don’t see a use case for analyzing their data and 45% report that they don’t have data worth analyzing. This is potentially a huge unused potential present in a lot of European companies. Asked for the reasons for not using their data, 30% report a lack of human capabilities, 27% a lack of time or resources to collect and maintain data and 26% report that their industry is still a lot about pen and paper. Data sharing as a use case is, according to the EU, not a reportedly common practice amongst enterprises, even though Gartner reports, in their “Sixth Annual Gartner Chief Data Officer Survey” that respondents, who successfully increase data sharing, led teams that were 1.7 times more effective at showing demonstrable, verifiable value. Among German companies, bitkom finds a split between companies seeing data sharing as business enhancing or business threatening. Investigating the reasons for not practicing data sharing Gartner identifies both culture/mindset, evolving from an ownership- to a sharing-mindset, and technological prerequisites as key obstacles. Especially in the EU context the role of regulations (such as GDPR) is often mentioned as a challenge. Bitkom finds that the data sceptic attitude in German society and in politics is posing a challenge for businesses that want to involve themselves in these newer topics. On the other side 37% of the companies asked see a competitive advantage in the GDPR (compared to only 18% seeing it as a disadvantage). An interesting cross-connection can be made to the EU survey which finds that a substantive number of businesses is not informed about novel developments of technology that are in any way related to data governance (they asked about edge infrastructure). Bitkom asked respondents about their knowledge to data spaces, with similar results.
Follow-up questions:
Overall the analyzed studies rather paint a dark picture about the current state of data governance programs in businesses. McKinsey finds a lot of inefficient programs where responsibility was solely put on the IT department, or technology was implemented (e.g. data lakes) with the hope that it solves problems. Gartner finds that while 80% of the surveyed businesses say that data governance is important, nearly half of them do not assess, measure or monitor their data governance programs. Research by the Harvard Business Review Analytic Services (HBR) finds a double-digit gap between aspirations and results when asking executives about the rate of success in achieving goals related to data governance. From the research of Drexel University we can see that data governance programs vary a lot in maturity. From all businesses asked, 64% report to have a data governance program in place, but 49% of these admit to using Excel for data governance. Similarly research by the Harvard Business School on “Data Governance: A Primer for Managers” as well as the research of Drexel and HBR find substantive differences between company sizes and sectors. Interestingly, but maybe also intuitively, the most highly regulated sectors, naming finance and health, have the most mature data governance programs in place. This can nicely be related to the finding, that the goal and outcome of a data governance program is to keep and enhance (user) trust.
A key question when it comes to implementing a data governance program is how to evaluate it and measure results and success. McKinsey underlines the challenge to measure the direct value of data governance, as only a lot of indirect values are easy to identify, like saving costs in data infrastructure, enabling digital products, analytics use cases or process efficiency gains. There exists research, e.g. by Bryjnolfsson and McElheran (2019), which finds a positive relationship between data-driven decision-making and firms’ productivity, but defining concrete KPIs is still a major challenge to companies, as HBR finds. Another source of challenges can be found on the organizational and cultural side. Both McKinsey and Drexel come to the conclusion that implementing a Data Governance structure and becoming a data-driven company means, at least to some degree, rethinking the organizational design of the entire company, which makes it a C-level management task. At the same time McKinsey found in their Insight “Designing data governance that delivers value” (2020) that missing awareness and experience of the C-management is a major challenge to implement data governance structures. To a similar conclusion comes BCG in their publication “Good Data Starts with Great Governance” (2019). By contrast, Drexel University and the company precisely find in their study “Trends in Data Governance and Data Quality” that C-level support is mostly given, and that actually missing cultural awareness and adoption are the main challenges for implementing a data governance system. The relevance of understanding data governance as a management task is underlined by the research of HBS that found that in successful companies, data governance is a leadership task and not an IT task.
Follow-Up Questions:
There is of course a lot more to learn from each one of the used studies. For in-depth insides into a specific study, report or article, we recommend to dive into the exiting research yourself and have posted the in our view most relevant resources below. More importantly, however, we would like to hear from you! We have put together a comprehensive explorative survey for German companies and would like to gather more granular data on all of these questions. The survey takes approx. 30 minutes to complete and we will present the results to all participants in our newsletter. We appreciate your time in clicking through the survey and giving yourself and us a clearer picture of the current state of data governance:
Our Survey:
For in-depth insights into the presented research: