• Mark Monfort

The first steps towards better data literacy

Data literacy in the workplace still surprises me. Whilst we have seen advances in technology capabilities and the evolution of the typical 9 to 5 job, we still haven’t come that far when it comes to having a strong level of data understanding across the general employee population (something I've seen first hand in many organisations). You might say that this is not the case at your workplace and congratulations for that being the case but when you compare the gap between those that are data literate and those that aren’t in the general working population, there is still a great divide.

What would count as data literacy you say? Regardless of whether you have any coding or data manipulation skills I think the first (and most paramount) point is to be able to conceptually understand where something can be improved with better processes and access to better data. I say that this first ‘thinking’ step counts because, whilst you might not have the skills to solve the problem at first, just being able to recognise that something can be improved is a major step towards becoming data literate.

Let’s say that in your role you are required to look up different databases to do a certain task. Despite the databases being separated you are able to pick up a customer ID in one of them that matches the customer ID in another. The first database has sales information and the other contains customer addresses. This manual work takes you time and this time could be better spent on other tasks but because the databases are not joined you are forced to manually look things up and then run another set of tasks on the results.

In the above, there might be a variety of reasons as to why the data sets are not already joined and perhaps that’s because you don’t have the data tools/software to do this. Problems such as this are all too common across a variety of companies big and small so don’t be embarrassed if this is all too familiar to you. The key is that recognising something like this is an important step towards solving the problem and making your workplace a more data literate one.

So, how would we begin to solve this problem? Depending on the tools that people are used to using there are a variety of options. Database engineers might look to solve the problem using script and coding with tools like SQL. This could take time depending on how busy their work schedules are and the end user (aka you) ends up waiting. A solution I find quite useful that requires less knowledge of complex code (but still exposes you to some) is the use of Business Intelligence tools. There are a variety of these but some of the best on the market include products from Qlik, Tableau and Microsoft. In a Qlik environment, for example, it is quite simple to pull in the data from both databases and create joins between them. The program also allows you to then create tables of the connected information and you can create queries to quickly look for the customers you had painstakingly been looking for when things were manual.

The steps above would look like this:

  1. Connect to database 1

  2. Connect to database 2

  3. Provide common name for customer identifier (so as to create a join)

  4. Load both datasets

  5. Create a table that shows customer sales information and addresses

  6. Create queries on the back of this to highlight certain requirements such as customers who have spent over a certain amount

Doing simplistic work like this might seem daunting at first but I’ll put some examples together in a future blog post. The point here is that just doing simple things like this would uncover many insights that you and your business can take advantage of. In my previous roles I have joined various customer data sets in order for our sales associates to have a clearer picture of clients before speaking with them. In some situations our sales team had to rely on other members of the team to obtain this information but in the new world I helped create, they had ready access to this on their own computers and could save time just by running their own queries. I also made this available on mobile devices so you can just imagine how much better each client conversation was when our team had a stronger understanding of the business. All of this just through a few simple joins and using simple yet powerful software. This is just scratching the surface yet it is often those first, formative steps that can be so powerful for organisational improvement. The future of data literacy looks bright so imagine what more can be done as technology and worker skills improve.

Over and out.

#data #datamanipulation #dataliteracy #Qlikview #QlikSense #Tableau #MicrosoftPowerBI #BusinessIntelligence

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