How to Use Analytics to Find and Solve the Toughest Organizational Questions
05/28/2019 by Craig Willis Modernization - Analytics
The term analytics means different things to different people depending on the role and position in your business. If you are a CFO, it will mean something different than it does to a data scientist. If you are a financial analyst, it will mean something different than it does to a biostatistician.
I can remember in the late 1990s while implementing a financial consolidation and reporting solution in Vancouver, British Columbia. I was talking to the financial analysts of a major gas pipeline company. They talked about processing actual values, budgeted values, and a forecast that was updated monthly. Even though I have a financial background, because I was working for an analytic software vendor, I thought they were talking about analytical forecasting. What they meant was re-forecasting the rest of the year’s financial results based on the run rate of the most recent financial results.
Techopedia defines analytics as “the scientific process of discovering and communicating the meaningful patterns which can be found in data. It is concerned with turning raw data into insight for making better decisions. It is especially useful in areas which record a lot of data or information.”
While the definition gives an overview of how analytics is defined in the technology industry, it does not convey the same meaning to everyone who reads it.
All of these exercises are involved in performing analytics at varying levels. Which technique you use will depend on what business decisions you are trying to make as well as the results needed to best support those decisions.
You can look at what analytics means across different industries and to different roles in those industries. Let’s review an example in the higher education industry to demonstrate what I mean by this and establish a baseline understanding.
Suppose you want to analyze the Calculus 101 class within the math department to help make staffing and course time offering decisions. You decide to examine the last five years of class data. The following descriptive statistics instantly come to mind:
After you look at the results, you have a pretty good idea about the demographics at your institution and which days, times and professors are the most popular.
From those results, you can make some conclusions about the Calculus 101 class from the data:
For those of you who look at this as analytics, don’t let anyone tell you that it isn’t. This is part of the analytic process. However, it is not the total picture. There is so much more to look at on the topic of analytics – like predictive analytics!
How can we gain further insight into the patterns and trends in our Calculus 101 class?
Can you answer these types of questions from the data and analysis listed above? The answer is “maybe” in some cases. However, to truly answer these questions, you would need to dive deeper into analytics to do so. This is where more complex techniques and data science usually come into the conversation. A word of advice – these more complex results and techniques are not readily available in the average spreadsheet package.
Analytics means different things to different people. It will take on varying levels of complexity depending on your role in the company, the industry in which you work, and the problems that you are trying to solve. The level of insight available to you will depend on your investigative curiosity and the inventory of analytic software and skillsets available in your organization.