The rate at which things change can sometimes be so gradual that we barely notice it. Like that one day when we casually grab for and put on someone else’s glasses, only to realize how our vision has changed and how nearsighted we’ve become. Or, you head to the closet to find last year’s golf shorts have mysteriously “shrunk” around the waistline (still can’t figure that one out). The situations are numerous where the slow progression of change goes essentially unnoticed. It’s only when an event causes us to compare the current situation to one of a significant time before that we can see how far off path we have drifted.
Sadly, many organizations are in this situation with regard to the quality of their data intelligence. Data which is slowly becoming unusable for decision making and will eventually kill their operations. First it’s just a few minutes of hands-on ‘tweaking’ to make it look good, but over time it becomes a whole team which ‘tweaks’ the data to make it presentable and useable. Before they realize it, the team is expending the bulk of its time manually cleansing and manipulating data rather than doing their actual jobs. The result in the end is the same as our vision and waistline. Our inability to recognize the slow progression of change in our environment has caused an unsustainable situation that now requires a major correction to get back into line.
Successful organizations grow, and so to must the way they generate, aggregate and maintain their operational data. Early on, spreadsheets are perfectly acceptable and satisfy the need, but as the volume and complexity of what they‘re tracking grows, the practicality of using spreadsheets fades. The recognition that the practicality has faded, however, lags well behind the reality. Everyone has seen those spreadsheets with external links, pivot tables, macros, VB scripts and whatever else can be packed into spreadsheet these days. It works today, so nobody wants to change it and it never seems like that one little, extra feature is asking too much. The problem is that continually doing so over the years is where this approach meets its ill-fated end.
We have all seen, heard of or actually been in situations where organizations are floundering and struggling to make decisions. They talk about how the data needed to support decisions is not available or takes too much effort to acquire and manipulate. Then, a new employee comes into the team and is shocked by the situation. They quickly point out how so many sources are bloated with features and enhancements to address oddities in the data that have occurred over the years. After further analysis, they expose even more little tweaks and adjustments that have been put in place at what appears to be exorbitant cost. Worse is that the source is still not providing the data quality desired.
Our vision was blurring and we never recognized it. The unintended use of someone else’s glasses was the wake-up call that was needed, and allowed us to immediately realize our eyesight had drifted so far off course from our childhood 20/20 vision. In this case, it was the new employee who was able to immediately recognize that the data quality wasn’t sufficient to continue running operations and was already undergoing immense manipulation at considerable costs. Neither of which was recognized by the existing employees or management.
There comes a time when you need to take a step back and take a fresh look at what you’re doing. Maybe it takes bringing someone in from outside to look at your data or a new tool to analyze it differently. The bottom line is that the pattern needs to be broken and it may take new eyes or tools to provide that fresh look at your data. Having grown and been involved with the current environment makes it that much more difficult for you to recognize how far it may have drifted from where you started and need it to be. The old saying “if it ain’t broke, don’t fix it” is dogging a lot of enterprises today. It is, in fact, “broke”, but people are so close to the problem they don’t realize it. The better maxim might be “If it ain’t broke, break it, and make a better one.”