Driving Data Governance Past Cultural Roadblocks


Overcoming resistance to change

Mike Wheeler

Recently, I wrote about getting caught in such data governance traps as pursuing quality for the sake of quality and imposing change on an organization without considering the cultural impact. In this post, I’ll explore the issue of corporate culture and what it takes to overcome barriers to change.
By nature, governance institutes change. Mandating change almost inevitably spawns resistance – whether due to fear, dislike for corporate authority or an ingrained mindset that there is no better way than the current way. So how do we overcome resistance to change? It isn’t as simple as making a decree or creating a policy. People live in their comfort zone and will naturally resist change. As an example, a corporation may have spent millions of dollars on BI, reporting and analysis products, yet many users resort to the ongoing use of Microsoft Excel as their “go-to” tool of choice for performing these tasks. It’s comfortable. It’s a known entity. And “that’s the way we’ve always done it.” However, success in data governance requires the establishment of and conformance to new policies and a more rigorous approach to everyday activities.

Let’s look at a classic example to appreciate the magnitude of the problem. How much time do you spend verifying the accuracy of data that has been produced for reports or that will be sent downstream for use in other systems and processes? Now consider how much of that data is found to be correct. Spending time validating something that is already correct is a truly non-value-add activity that costs companies a great deal of time and money. If you then multiply that effort by each of the downstream processes that need data, we begin to see the scale of the problem. The lack of confidence in upstream processes to provide complete and accurate data makes this practice necessary. Imagine the time and money that could be saved if a more prescriptive method of ensuring data quality was in place. The answer seems simple. Put better governance processes in place upstream in the process!

Putting more rigorous controls and processes in place at the earliest point of data entry may seem like a blinding glimpse of the obvious, but here’s the rub: redesigning any business process for the “greater good” without establishing the benefits to all involved parties will be met with roadblocks. If the new processes and controls cause the upstream data entry clerk to take 25% longer to enter complete and accurate information rather than simply entering the minimum required for the system to allow them to move to the next step, they are now 25% less efficient given their current metric. From a bottom-up viewpoint, the greater good of the company is irrelevant, as it is contrary to the individual worker’s metric. What incentive exists for a knowledge worker to begin following a new, perhaps more time-consuming process? None, if the data governance program does not provide the right incentives to create a corresponding cultural shift from activity-based to process-based behavior. Resistance to this type of change is only natural. Why would I change what I do if you can’t demonstrate how it benefits me?

Creating new processes and policies for people to follow isn’t the challenge. The challenge isn’t even to get individuals to understand what the new rules are. The difficulty lies in the ability to effectively communicate the benefits of the new methods in a way that incents them to follow the prescribed path. To have all parts of the organization act toward the greater good, all parts of the organization must be aligned to the greater good. Incentives for overall process outcomes need to replace incentives for event-based execution. Data governance practitioners are learning that the path to increased adoption is through answering the burning question on every impacted worker’s mind: “What’s in it for me?” The correlation between quality of data and business process performance needs to be highlighted and clearly, communicated. Quality for the sake of quality is of unknown value and will, in all likelihood, be met with resistance. However, quality for the sake of improved end-to-end process performance can be a game-changer provided proper motivation is in place to drive a cultural shift toward the greater good. If you want to change the way someone behaves, you must change the way they are measured.

As charters for data governance programs are created and policies are proposed it is crucial to think through the core processes that drive the enterprise, document desired outcomes, and incent individuals to contribute to those outcomes in a holistic, process-centric way. If we are to drive greater adoption of data governance processes, we must ensure alignment of individual incentives with the goals of the greater good.

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