‘Analyze data‘, says the middle manager while his blue eyes stare into yours… (And the whites of his eyes are not white, but red, because he has been staring at the same spreadsheet for several hours without making sense of it). But he means to have them analyzed by someone else because those two words are easy to say, though not so easy to do. Fortunately, with a little accessible language, data analysis ceases to be black magic and becomes a process that actually helps to make good decisions. Let’s see how.
What data analysis is and why you should know how to do it
Data analysis is a process that starts from the very basics (collecting the data to be analyzed later) and gets more complicated as the analyst goes through its different phases: cleaning the data, defining what we want to obtain from it, processing the raw data to find out if it hides valuable information (the answer to this is usually yes) and, finally, carrying out the analysis that will lead to better business decisions, especially economic ones.
The problem is that most companies are content to collect the data, but then don’t know what to do with it. And what is it that data analysis will allow you to do? Well, no more and no less than:
- Improve the user experience because you have obtained the data you are analyzing from your customers, so you can use it to understand them better.
- Make better decisions: because the can tell you where the strengths and weaknesses of your business lie.
- Understand your customers’ behavior. For example, you will be able to predict when they are going to buy or what drives them to do so.
- Better monitoring of the competition: this is one of the advantages of the data analysis that we like the most because it makes us feel like supervillains… Although in reality everyone is doing it. Your competition is already analyzing you. Now all you have to do is catch up.
Data analysis for dummies
Analyzing data is not easy, but here are some things you can do to make it easier for you to get some value out of your data.
- Clean the data: although this first step can be refined to infinity, you can start by eliminating duplicates that skew your results and deleting fields or columns that are of no use to you.
- Define what you are going to ask the data for: if you know what you want it for, the task of cleaning it will be much faster and easier. This decision gives you clear criteria to follow.
- Bear in mind the context: for example, data from most websites in 2020 is not comparable to other years. Why? The COVID-19 health crisis altered all our behavior.
- Get your data from different sources: to ensure that you control for bias.
- Analyze only the metrics that are most relevant to you… without losing sight of the general ones.
There are many data analysis techniques that will help you take your business to the next level. However, there is one thing you should not forget: analyzing does not mean bending. There is a saying that makes this clear: you can bend a number to say exactly what you want it to say, but that is not analyzing data, but torturing it… And torture never gives useful information. Do you know where you will find all the information you need to stay up to date on Big Data and AI? At the biggest Data Science and AI event in Europe! Join in https://datanatives.io/tickets/.