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BI blog | How do you analyse your data? What data?

Data is everywhere around us – this notion is something we encounter each and every day at public conferences, on the internet, at work, on the street, in the elevator – literally everywhere. However, what exactly does it imply, and have our lives actually changed in the past few years, in terms of how we manage our data?

I would say – sadly, no! If you look at the two charts below, you can see that the average number of data-related buzzwords you can find on the internet is growing similarly to how the amount of data itself is growing: advanced analytics, machine-generated data, data discovery, mobile bi, self-service, data science, real-time analytics, smart data and many more are so popular on IT conferences.

If you think about those for a while, you will see that all of them, no matter how customer-oriented, are still in essence technical projects. A complex technical solution is behind each and every of those buzzwords. In addition, if you walk into any average company in the region, you will see they are all still far removed from the complex topics mentioned above, and are still struggling with manual data preparation, copy-paste, VLOOKUP, and other not even particularly advanced functionalities. The question is – why? We have cool advanced technology, so what is missing?

In Wikipedia under buzzwords, you will find more interesting ones - and not in the section Science and Technology - but rather under Education, as Data Literacy. If you dig further still, you will find a single definition offered by MIT which states: “Data literacy includes the ability to read, work with, analyze and argue with data”. Now I dare you to think about this for a while. We can all read data and we all definitely work with data (at least VLOOKUP it). Some of us might even analyze data, but we all have to admit – we are not particularly skilled in arguing with data. Why is that? Why have we forgotten all about data literacy?

Because – we were expected to be good at it. Our employers expect us to be good with data and to be literate. If you go back and think back on the math problems you had to solve in elementary school, you will see that you were good at it (more or less). A couple of equations with a couple of variables, and we could solve pretty much anything. However, those problems included little data when compared to today’s business problems, which is what we’re trying to solve.

The timeline depicted above shows where we stand in today’s world of data (person icon), in relation to the tools we use to solve data problems (compass icon). These same tools, which are supposed to help us be more data literate, are a little outdated, mostly because we are not ready to invest in education and learning how to work with more advanced tools, which would in turn help us be more data literate.

If we move one step further and put this story into the context of a single enterprise, it becomes even more complex. You have business users with their requirements on one side, and IT on the other side, with requirements of their own. However, one of those buzzwords from the beginning of this story can help us solve this problem – self-service. The general idea behind self-service is to make decision-making easier and faster, and to bring that decision-making to all levels of the company. These modern analytical solutions should minimize time needed for repetitive activities like data preparation, cleaning, etc., and should leave more time for users to actually read, work with, analyze, and argue with data, or in other words – become more data literate.

And yes, this means that the roles you have become familiar with will now change. A brand manager will not have to consolidate multiple excels just to get numeric distribution on the market, but will have enough time to organize and motivate their sales team to work on promotion that is actually profitable. At the same time, the IT department will not have to add new measures to the OLAP cube daily (without knowing why), but will have enough time to control data quality and to make sure that come Monday morning, everyone has the same understanding of what ‘promotion efficiency’ means.

If you are going to take away anything from this article, let it be three things: stop dreaming about data you do not have and start using data you currently do have, analyze data (don’t just report it), and do not be afraid to change the description of your current job. Or to put it into more straightforward terms – embrace the simplicity of self-service analytics, which will enable you to become more data literate. Do not be afraid that someone will lose their job – you will just become more useful.

 

Milan Listeš, Head of BI Department, Adacta Zagreb

 

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