The reality of today is that we live in a world awash with data. “The Internet of things” has brought about more data points to scrutinize that one can imagine. The abundance of data has created an environment where decision makers and constituents have greater leeway and freedom to believe that certain answers out of reach less than a decade ago are as easy to grasp as a book on the shelf. This is an outgrowth of the reality around us. From traffic patterns, to shopping habits, to sports performance; what once was a hidden force is now known with certainty.
For those in organizations and departments that are at the beckon call of their internal or external overlords, your cognitive ability must be adept enough to maneuver through pathways unknown and unplanned for. Saving grace cannot be fully dependent on AI, a spreadsheet, database or any other tool. It is your understanding of your organizations resources (data) and how to piece the disconnected informational items in order to arrive at a meaningful outcome. All the data tech tools in the world cannot help you, if you do not know where to look for the source data or what that the information actually means.
On the flip side of things is how to capture the right data and at the right time. I say this because not everything is a barcode or something that can be optically recorded on a live or on demand basis. Unfortunately, not every industry has the infrastructure or electronic tracking mechanisms to turn interactions into data points. For example, if you look at K-12 education, a number of services delivered to students are known, but not tracked at a level where metrics can be pulled without individuals employing time and effort to re-create the past in data points. Student data systems for attendance and enrollment are robust, yet when you get to things like tutoring, the full composition of what is delivered is less defined.
The issue of needs now comes into play. A lot of things can be tracked, whether it is through an automated data collection process or more manual system. The process that needs to be thought about is not so much of what current needs and requirements exist, but what might be needed in the future. To identify what might be needed in the future, the questions that should be asked are, “What items in our process result in value?” or “What items in our process facilitate an outcome?”
In the example of supplying tutoring for students, it is not an activity that generates income, such as average daily attendance (ADA) or, at times, enrollment. Though it is not tied to funding, tutoring, is tied to the outcomes of educational agencies. In addition, tutoring is a visible and known activity that any stakeholder could latch onto as an activity to be evaluated in terms of data. How many students, what type of students, what frequency did they receive service, what short-term outcomes were seen, what long-term outcomes were seen, what expense was associated on a per student (or other basis)? The questions are relevant, though the ability to answer them through data might be darn near impossible. How do you recreate a scene without a record of what the scene actually looked like?
In the world in which we live, an increasingly prevalent assumption exists that data points for all activities are prevalent and readily available to be extracted and analyzed. Trying to fit the bill of this assumption by pulling different data sources and information to paint a picture many constituents assume already exists, is a time intensive process and probably does not end with the clarity needed to bring forth accurate and meaningful decision making.
What is increasingly needed is a more focused discussion of what relevant data points are missing from the organization’s data set, how to go forth in actually capturing such data, and what decisions can be made from the data captured. You can either get ahead of the data ball or be running to escape the data ball rolling after you.