The mean stepmother of data analytics

In our today's blog post we want to talk about what data analytics is, what use can we get out of data analytics and why many companies still neglect this.

The first step, when we don't know exactly how to describe a term, is to google it. Here you find a wikipedia post very quickly. Wikipedia describes data analytics simple as follows "Analytics is the discovery, interpretation, and communication of meaningful patterns in data." And that short and simple sentence describes data analytics very good. When we analyze data, we discover the data, we interpret the data and we're looking for patterns in the data. You use the information you get out of your data to gain an additional value out of it and to make certain decisions.

Every company in the world owns data. This can differ in type and volume and could be: personnel data, customer data, financial data, performance data of equipment and machines, behavioral data, data of products and so on. And there are many more categories of different data.

Lets get to the problem, why companies still neglect this topic. All companies own and produce data, some of them (not all!) save the data "someplace" and are done then. Most of the time the data is then just used to finish financial commitments or to create a small, very rudimentary report. And that's it. There are many understandable reasons for that. And that's the point where we get to the mean stepmother. In this case that are the constraints against data analytics:

• Some think, data analytics is not worth it (especially for smaller companies)
• Lack of employees - hard to find qualified employees for data analytics
• The core business is not data analytics and the employees don't have time to engage in data analytics
  

To visualize this problem very quick. You all remember that the first human landed on the moon in the year 1969. That didn't only result in us being happy to have stepped onto another planets surface (we don't want to discuss the status of the moon being a planet or not at this point), but this also resulted in further space flights and scientific discoveries. Would the humankind not have used the potential at that time, we wouldn't be at the point we are right now. The humankind could have said "well, we know how to fly, but that's ok and we don't want more". But instead we used the whole technical and scientific potential we had to achieve progress.

This comparison may seem a little bit dramitc, but only dramatic comparisons show certain situations in high detail. That means for our topic of data analytics, that it could be "ok" if someone doesn't care care of his data very much, but you are only able to achieve progress, when you use the full potential of your data. And the good thing is: You don't need to be a rocket scientist for that!

The final questions is, what additional value can we get out of data analytics for our companies? Short answer: An enormous value! And it doesn't matter how big your company is. Every data has high potential which has to be used. The main difference is the volume of the data, but that's it.

By professional data analytics you can use the whole potential of your data. And to be honest: You didn't generate data over months and years for nothing, didn't you? Why shouldn't you use the information which is in your data, if you already have the data? You will be surprised how man information is in your data and you will see correlations you've never thought of before.

Of course there will be initial costs to do professional data analytics but you will see very quickly, that it's worth it and you will save a lot of money. By professional data analytics you will see the strenghts and weaknesses of your company very quickly as well as possible savings and possible optimizations.