The Truth Shall Make You Miserable
When companies begin making their data more accessible via Self-Serve Power BI, they soon reach a critical break point in those efforts. The Project dashboards tell them something that isn’t pleasant or doesn’t match the narrative been publicized.
The Reality in Your Project Dashboards
Performance indicators go red. The data shows the stellar progress that was planned isn’t happening. Operational demands for time are much higher in reality than assumed in planning. In short, it shows the harsh reality, as captured in the data.
This is a moment of truth for organizations. Are we going to embrace the transparency or will we attempt to control the narrative?
Data Quality Challenges
The first question is normally, is this data accurate? This is quite reasonable to ask, especially at the beginning the data stream may not be as clean as it should be.
The approach to this answer can decide your success going forward. For some, questioning the data is a prelude to dismissing the use of the data. For others, it’s a starting point for improvement.
The data deniers will provide many reasons why “we can’t use the data.” They will complain that the data is inaccurate or incomplete. Therefore, they can’t trust their data to integrate its use into their daily work or to use it to make decisions.
These data deniers may have other hidden reasons for their position, such as political or power base protection reasons. Moving to data-centric culture is a big change for many organizations, as you have to be open about your failures. No company is always above average in every endeavor.
Data deniers also fear how business intelligence might impact their careers. If the corporate culture is such where punishment is meted out when the numbers and updates aren’t desirable, likely data transparency won’t be welcome.
Change the Focus of How Data is Used to Succeed
The key to overcoming the data fear is to change the intent for its use, moving the focus from punishment to improvement.
For the successful companies using data, they embrace two simple facts. One, the data is never perfect and that it doesn’t have to be to effect a positive change. Two, they’ve defined the level of granularity needed in the data to be used successfully.
How Imprecise Data is Changing the World
We see this approach in our personal lives. For example, the Fitbit device is not 100% accurate or precise. Yet, millions are changing their behavior of being more active because of the feedback that it provides. based on relatively decent data. You may also be carrying a smart phone, which also tracks your steps. Between the two, you would have a generally good idea of how many steps you took today.
From a granularity approach, we aren’t generally worried about whether I took 4103 steps or 4107 steps today. We took 4100 steps. Hundreds is our minimum granularity. It could easily be at the thousands level, as long as that granularity meets your information needs.
Cost Benefit of a Minimum Level of Granularity
One area we see this type of data accuracy dispute in the corporate world is with cost data. It’s been engrained in our psyche that we have to balance to the penny. Our default data granularity is set to the cent.
While that may improve accuracy and precision, it doesn’t make a material difference in the impact. For example, if your average project budget is $2M, then worrying about a 5 cent variance is a percentage variance of 0.0000025%. I’ve seen organizations who get wrapped up in balancing to the penny and waste an inordinate amount of time each week getting there.
Instead, let’s define a minimum granularity in the data such that a 1% variance is visible. For a $2M average, you would round up at the $10,000 point. Doing so then reduces work attempting to make the data perfect. Any variances of that size are significant enough to warrant attention and are more likely to stand out.
Implementing Self-Server BI using products like Microsoft Power BI and Marquee™ Project Dashboards will enable your organization to gain great improvements as long as they are willing to accept the assumptions above. The truth may make you miserable in the short term as you address underlying data and process challenges. In the long run, you and your company will be better served.
Please share your experiences in the comments below.
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