The goals of a data governance program are usually quickly defined and communicated in a management-oriented way: clarify responsibilities; increase transparency; increase trust in data; improve data quality and thereby optimize operational excellence. In short: become a data-driven company and a pioneer in shaping digitization – a catchy target image that everyone can get behind.

However, once the program has been introduced, disillusionment – and sometimes frustration – often quickly set in. The clarification of responsibility deteriorates into endless self-administration, the hoped-for transparency and trust are blighted by the newly created bureaucracy, and data quality soon drops back to its original level.

In our over 25 years of data management and data governance experience, we have seen that these phenomena are not isolated cases; instead, they are a frequently occurring pattern in organizations of all kinds. A pattern that connects.

The following questions inevitably arise:

  • What are the pitfalls that are so often overlooked and that lead to the pattern outlined above?
  • How do you get around them?
  • Why is it that data governance is often successfully introduced into an organization but its application in day-to-day operations fails?
  • Is it the internal constitution of most organizations that leads to the highlighted problems, or is it the propagated data governance approaches?

This article attempts to answer these questions and build on them in order to create a new, sustainable understanding of data governance.

The Genie Is Out of the Bottle – on the (Un)Availability of Data

While you were reading the short introductory paragraph to this article, here’s what has happened: 9,132 people networked on LinkedIn, according to Statista. 695,000 stories were shared on Instagram. 69,000,000 messages were sent via WhatsApp. 197,600,000 emails were sent. The “data” genie is out of the bottle. As the examples show, it has been shaping our lives for some time – both professionally and privately.

The central mantras of the data-producing and data-consuming digital transformation are ubiquity and availability: Ubiquity in the sense of a data economy permeating every sphere of life. And availability in the sense of data production and data use that is fundamentally independent of time and location. That last point in particular has been arousing strong, economically driven interest among the majority of companies in recent years. Data is becoming a “data product,” a key asset. It is an asset that needs to be cultivated, managed and monetized like any other asset.

With these ambitions in mind, numerous companies swiftly asked themselves:

  • What data is actually an asset for us and how do we begin to cultivate it?
  • Where does the data actually come from and where is it used?
  • What do we currently use data for and who is responsible for our data?

Despite the fundamental availability of data, there seemed to be a growing realization that it could not be cultivated and often also that it eluded management control. The main reasons for this are the sheer volume of data as well as the increasing speed of data generation. This could be summed up as “unavailability due to sheer data volume.”

Another complicating factor was that there was no going back. The digital transformation is relentless when it comes to driving data production. A self-reinforcing process can be diagnosed here. In short: It’s also true for organizations that the “data” genie is out of the bottle and can’t be forced back in. A company’s competitiveness, and sometimes even its continued existence, depends crucially on its ability to deal with the genie.

A Typical Reaction Pattern: Data Governance

The mission is clear and unambiguous: The company must regain sovereignty over its data. The way to do it: Design and implement value-added data governance. The project is then often implemented according to the following proven scheme:

A data strategy is designed under the sponsorship of the company’s CFO/CDO. The establishment of data governance, initially with a focus on master data, on the company’s data foundation, is initiated as one of the first projects in implementation of the strategy. Data governance processes and structures are specified and sometimes accompanied by the implementation of appropriate technologies for the future exercise of governance. In the next step, role owners for the new data organization are sought, usually found quickly and then empowered to perform their new duties. In parallel, the first regulations and standards on how to handle the asset “data” in the future begin to emerge. All this with the hope that the company’s data will turn to gold after completion of the project.

This is a typical reaction pattern in companies, which also quickly exhibits success at first. Depending on the maturity of the data, the structural complexity and the (micro-)political situation, these organizational changes are often put in place quickly and smoothly. After six to twelve months, data governance is a reality. The project is deemed successful and is marketed internally as such. It seems like the “data” genie is retreating back into the bottle. For now!

On Pitfalls in Data Governance Application – or When the Solution Is the Problem

The first signs that the “data” genie cannot be tamed as originally hoped often become apparent soon after the project is completed. For example, business processes are still stalling because of data quality issues, despite numerous new regulations and standards. Even the longed-for added value from cultivating the company’s data is usually modest. In some cases, a deterioration can even be seen. For example, the newly introduced data governance structures and processes seem to have a negative impact on transparency due to their inherent complexity. Sometimes who is responsible for what data is less clear now than before data governance was introduced.

These and other challenges can be traced back to the following pitfalls that are regularly observed in the operational application of data governance in a range of organizations:

  • Data governance as an end in itself: Data governance is implemented by colleagues who are passionate about it. These tend to be the ones who have already exercised data governance informally prior to its introduction. They are now the key players when it comes to data governance. Consequently, all topics are viewed through the lens of “data governance.” In many cases, there is no reference to organizational value creation. Data governance is implemented and optimized for its own sake. Another silo has been created in the company!
  • The unchanging data governance organization: Roles, responsibilities, decision-making and directive powers, committees – in short, the data governance organization – are elaborately defined during implementation, aligned with the existing organizational structures and built up. And there they remain, while the company changes and goes through one reorganization after another. The original data governance organization remains untouched, and decoupling occurs in the medium term, with the consequence that data governance becomes less effective.
  • Data governance as a nice pretense: Data governance is exercised because it meets the expectations of some stakeholders. Expectations both inside and outside the company are the benchmarks for data governance, and not organizational value creation. The consequence is ineffective data governance due to alignment with the wrong standards.
  • Data governance as a toothless paper tiger: Processes, organizational structures, concepts, regulations, etc. are specified and communicated in an elaborate and detailed manner. However, they are not applied in practice. Data governance therefore exists only on paper and produces reams of policies, guidelines and standards – which, in turn, means more new data. The propagated solution of the data problem thus becomes another driver of the problem. In other words, the solution is the problem!

Principles of Data Governance 2.0

The pitfalls outlined above and the resulting undesirable developments following the introduction of data governance require a fundamental rethink. What’s required is holistic, practice-oriented data governance geared to corporate value creation: Data Governance 2.0! Data Governance 2.0 that is characterized by the following guiding principles for introduction and sustainable, successful operational implementation:

  • The design focus of data governance lies on value creation, i.e. on the products, services or generally the value created by a company, and not on data governance itself! The value contribution that data governance makes by cultivating the asset of data must be continuously coordinated and shaped in interaction with the company’s internal and external stakeholders.
  • The data governance organization is a learning organization. Changes occurring both inside and outside the company must be continuously monitored and reflected upon with regard to the effectiveness of the data governance organization. They provide valuable input for developing the data governance organization. In short: The ability to question itself on a regular basis is critical to the success of any data governance organization!
  • The central reference point for evaluating the effectiveness of data governance is organizational value creation. The actions and decisions that deliver added value are specific to each company. Consequently, the exercise of data governance must be oriented to a company’s circumstances and specific logic. Not to theoretical models and specifications.
  • Data governance is a practical discipline and goes beyond mere control via standards and regulations. Rather, value-oriented and coordinated management is required to continuously create the prerequisites for effective data governance.
Pitfalls data governance
Fig. 1: The design, implementation and operation of data governance is associated with typical pitfalls, which causes inappropriate simplifications and inefficiency.

For those looking to invest competitively in the future and to turn their data into gold, the need for action . This applies to all companies, regardless of whether data governance already exists or not. The outlined pitfalls and listed principles can provide orientation when a company plans to further develop its data governance or to introduce new data governance. Orientation to successfully navigate the path to becoming a data-driven company and orientation to become a pioneer when it comes to actively shaping the digital transformation of society. At the same time, it has been proven that an external view during the introduction or adaptation of data governance can accelerate implementation processes, reduce costs and create sustainable added value.

If you want to learn more about how to establish Data Governance 2.0 in your organization, feel free to contact our experts.


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