Exploratory data analysis and its role in improving business operations
Let me tell you about an interesting meeting I had.
It was with a potential client, a prominent healthcare company, and we were discussing the prospect of doing business together. As the meeting progressed, we kept going back to one issue we had over and over again: Turning their mountain of data into useful action.
“Just can’t make sense of all the data,” One executive said. “Analytics helps to some degree, but it’s difficult to turn all those stats and graphs into actual business results!” I had an idea of why the company was struggling but decided to ask for more clarification, instead of jumping to conclusions. “What do you mean?” I asked.
“I mean… there is a lot of data, but it’s difficult to make sense of it. I hope this is making sense?”
It was definitely making sense, and I knew where he was wrong. This private healthcare company was missing one crucial step in their process: Exploratory data analysis, and I told him as such.
If you are like the executive and unfamiliar with the term, your next question would be “What is exploratory data analysis?” followed by “How does it help me use data to improve my bottom line?”.
That’s what I am going to address, right now.
What is exploratory data analysis?
Exploratory data analysis (EDA) is the first step in the data analysis process. The idea is to make sense of the data, figure out the questions you need to ask and establish the best way to manipulate datasets to get the answers you need. This is done by taking a look at the trends, patterns and unexpected data results.
EDA is necessary for the next stage of data research. If there was an analogy to exploratory data analysis, it would be that of a painter examining their tools and available time, before deciding on what best to paint.
Why is EDA so important?
EDA is important for business processes because you are essentially prepping datasets for a deep, thorough analysis that will address your business problems. Some of the tasks accomplished with exploratory data analysis are finding errors, discovering data, mapping out data structures, listing out anomalies and setting parameters.
As you can see, exploratory data analysis is a crucial step to making sure you have the perfect dataset because it sets the stage for more advanced analysis, like machine learning and data modelling. EDA is crucial because it provides the necessary context you need to answer your business questions.
Yet, I see so many businesses neglect EDA, not because they think its unimportant, but because they assume that machine learning algorithms and other advanced analytics systems are enough to get the job done. However, while these tools are incredibly powerful, they are only as good as the data they are working on.
If the data structure is flawed, then the results will be flawed. After all, an artist might have a brilliant mind and a sharp hand, but if the pencil is dull, then the portrait wouldn’t be up to par.
The importance of EDA to business operations
Now, we get to the second question I want to address, “Why is EDA important to business operations?” Exploratory data analysis is crucial for operations because it closes the gap between the technical and business sides of the organisation.
When data analysis is done properly, several things fall into place. For example, data scientists will know if they have produced results within the required business context. Stakeholders can confirm if they are asking the right questions and even discover interesting trends that they did not know existed.
Put simply, exploratory data analysis ensures that business executives (like the frustrated one at the meeting) are getting the results they are looking for from data analytics.
Furthermore, EDA can be easily incorporated into BI software, which is crucial when you consider that more and more businesses are adopting BI. According to a study published by Forbes, over 54% of businesses see cloud BI as critical to their current and future strategies. So exploratory data analysis is crucial for making the most out of your analytics software.
Getting the most out of analytics
Predictive analytics and machine learning are fantastic tools for breaking down and analysing data for business objectives. However, it is just as crucial to understand that the data sets must be in the right format and exploratory data analysis is crucial for making that happen.
A team of data experts, like the Selerity team, can ensure that your exploratory data analysis is done correctly so that subsequent analytics efforts are properly aligned with business objectives. That’s what the business executive from the healthcare company learnt when we started working with them 🙂