Previously I wrote an article about the challenges with learning analytics (you can read it here). With this article I want to continue along that line but I also want to start a new series which focuses more on the technical side of learning analytics. I believe the reason why most of us may not be doing learning analytics as much as we should is because we don't believe we have the skills. I describe learning analytics as data analytics applied to learning and development. While data analytics is using data to draw conclusions that leads to better decision making. Therefore learning analytics can be seen as using data related to learning and development to arrive at conclusions which enable us to make more effective decisions.

There are four broad skills and/or knowledge areas needed for learning analytics:

1. Firstly and probably the most important is domain knowledge. To carry out data analysis in the area of learning and development you must understand the domain of learning and development. That understanding is what will enable you decide why you need to analyse data, what data you want to analyse and how you will analyse the data. I'm sure this area will be a  given for anyone reading since you will most likely be a learning and development professional.
2. The second skill and knowledge is the most technical aspect, which is having knowledge of analytic techniques, basically statistics and mathematics. Don't let that scare you. Most if not all analytics needed for learning and development can be done with basic statistical and mathematical techniques called descriptive analytics. This is the first level of analytics and will include techniques such as visualising data, calculating averages, percentages and sum. The goal here is to describe the data and draw conclusions from the description.
3. The third skill is also a technical one and it focuses on the tools used for analytics. There are myriads of tools ranging from click-and-use software tools to programming languages. We don't need to get too advanced or technical here. Having good knowledge of how to use a spreadsheet application like Micorosft Excel will suffice.
4. The last are soft skills. It is not enough to be able to analyse data, we must be able to communicate and tell stories using our conclusions to engage the right stakeholders. This is a skill I believe again learning and development people should not struggle with, since for the most part we are communicators.

Going forward the main areas I am going to concentrate on are 2 and 3 which are analytic techniques coupled with the use of software, in this case Micorsoft Excel. 

In my next article I will delve deeper into what descriptive analytics is and why it's an important skill for learning and development practitioners to have.