Calculating the True Value of Effective Data Governance
3 minutes reading time
As part of our ‘Clever Ways to Unlock More Value in Your Organisation’s Data’ series, we'll explore key areas such as data governance, metadata, and decision-making processes to help you discover innovative ways to derive value from your data and make data-driven decisions that drive investment and growth.
What is data governance?
Let’s begin with data governance, a term that is often misunderstood and underutilised within the business world. Why is this the case? Well, one significant issue is how we present and sell the concept of data governance.
Data governance means making sure data is secure, private, accurate, accessible and usable. It covers the actions that we take, the processes we must follow and the technology that supports us throughout the data journey.
Typically, businesses approach this from a risk perspective. However, when focusing on risk, we have a natural tendency to invest the minimum effort to reach an acceptable risk level. This is especially true when risks are seen as shared rather than individual concerns. Consequently, data governance often becomes a checkbox exercise, failing to deliver the value it promises.
Data governance can sometimes be confused with data quality, which limits our perspective. To reframe our understanding, let's view data governance as a framework and a set of processes designed to enhance the value of your data. While risk remains a crucial factor, it doesn't have to be the driving force. Thinking of data governance as a way to create value rather than just a way to manage risk, can significantly change perceptions.
The value of data governance
When we discuss the value of data governance, many immediately think of data quality, and rightfully so. Estimates suggest that poor data quality can cost organisations anywhere from 15% to 30% of their revenue, with IBM estimating the cost of data quality issues in the U.S. at approximately $3 trillion. It's a promising area for investment, but measuring its full impact can be tricky.
If we broaden our focus beyond data quality, we can explore another often-overlooked aspect: "time to value" and how to improve the change process.
Data plays a pivotal role in countless change initiatives. Almost every change, except infrastructure or networking-related ones, involves data in some capacity. Unfortunately, when we reach the data-related phase of a change, we often encounter roadblocks that slow us down. These delays aren't usually caused by technical implementation issues but rather by data governance-related concerns, such as data location, access, interpretation, and documentation. Resolving these issues can add weeks to a change initiative, and once we overcome them, we tend to forget the lessons learned and rely solely on organisational memory.
This issue becomes even more critical when dealing with legacy systems, numerous point-to-point integrations, and a lack of knowledge retention, all of which hinder an organisation’s ability to adapt quickly. Quantifying the true costs associated with these challenges can be difficult.
To address this issue, I've developed a formula that combines various factors to quantify the impact of slow change due to data governance challenges. This involves evaluating the average time lost to investigating data-related problems and determining the associated resource costs.
The formula looks like this:
Value of Change =
Number of Changes per Year x Average Annual Value of Change x 1.5
Data Delay Impact =
Average FTE lost to Data Issues / 260 (working days in the year)
Resource Cost =
Changes per Year x Average FTE lost to Data Issues
Average Day Rate x 0.5
The value of change multiplier accounts for the importance of change within your organisation. If your business undergoes frequent changes, these changes are likely to be vital to your operations, and their value will extend beyond the immediate financial impact.
The average day rate multiplier accounts for the fact that individuals may not be solely focused on data governance issues.
Imagine you have a year with approximately 250 working days. You might experience delays equivalent to four weeks on a change initiative due to poor data governance. If you calculate the cost of these delays by considering the average day rate, you can estimate the annual impact of these data-related challenges.
Here is an example of what those numbers could look like:
Value of Change =
50 x £20k x 1.5 = £1.5m
Data Delay Impact =
28 / 260 = 0.11
Resource Cost =
50 x 28 x £200 x 0.5 = £280k
Total loss/not gained: £442k (29%)
Using these figures, you can estimate that on an annual value of £1.5 million, your organisation might lose (or not gain) £442k due to poor data governance. While these numbers are just an example, what matters most is the percentage—nearly one-third of the change's value could be lost due to delays caused by inadequate data governance.
These figures demonstrate the significance of addressing data governance issues and making a strong case for investment. Now, more than ever, organisations must leverage data governance to their advantage. Instead of solely focusing on risk, emphasising the value it brings will be crucial for securing investment and gaining buy-in from across the organisation. Articulating the value of speed in change, in addition to data quality improvements, will be key to achieving success.
Please get in touch if you'd like to discuss data governance and how to deal with it in your organisation.