Senior Learning and Development Advisor Jewish Care
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Why learning analytics may be challenging

21st Nov 2019
Senior Learning and Development Advisor Jewish Care
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Analytics challenge

Let's be clear, learning and development analytics is just data analytics applied to learning and development data. It really isn't that different to using data analytics in other fields. So as a reminder data analytics is the process of answering questions, solving problems and making better decisions with data. Therefore learning and development analytics is about using data to make better learning and development decisions and make the delivery of learning and development much more efficient and effective. Learning and development analytics involves a number of processes, which I have summarised into four steps captured with the acronym DAAR. It stands for:

  1. Define the learning and development question to answer or problem you want to solve with data clearly.
  2. Acquire and Prepare. Get the data you need to answer the question/solve the problem and if necessary prepare it for analysis.
  3. Analyse the data to answer the question or solve the problem. Analysis of data exists at four levels which are descriptive (what happened?), diagnostic (why did it happened?), predictive (what can happen?) and prescriptive (what should happen?). Probably all L&D analytics can be done at the first two levels.
  4. Report and conclude: Draw up your conclusions from the analysis and report to the relevant stakeholders. And of course take any actions required by the conclusions.

So why do we find learning and development analytics challenging if this (the above) is what we need to do? There are many reasons but I think one of the biggest challenges are the software we use for managing learning and development work. Most of the technology we use such as learning management systems do not have good or easy to use data analytics functions. The functions on most of them either don't produce reports easily, don't produce good reports or are just complex to use. I belelieve there are more recent systems that are better and tied to the XAPI framework (Learning Record Stores and all that), but most L&D teams, especially those in small, medium and larger organisations with smaller L&D teams and budgets are not going to invest in them. For the time being until those technologies become more uniquitos, teams will still depend on their Learning Management Systems.

What can we do about this challenge? Learn an external analytics tool that you can pull data into and do your analysis. Most of our stakeholders are not concerned about how we do our analytics, they just want us to do it and give them the answers they need. In that case there are three tools you can start using almost immediately. Two of them you may need to spend a bit of time learning.

  • Excel: The first tool is the one and only trusted Excel. This one most likely is on your desktop already. If your organisation doesn't use Excel, there are free and open source alternatives such as the spreadsheet that comes with Office Libre and Google Sheets. I would recommend that every L&D professional learn to use Excel. It has some key functions that can help you to analyse data quickly such as PivotTables, conditional formatting, conditional functions and the PowerPivot functionality. It is my top choice for starting analytics immediately.
  • Power BI Desktop: My second choice is Power BI Desktop. Power BI is a business analysis tool from Microsoft which can work with diverse types of data, be used to clean, analyse and visualise data. The desktop version is free to download from Micorosoft, but the web-based version you will have to pay for. PowerPivot in Excel is also included in Power BI desktop and you can do a lot of great data analysis with this free tool. So download it and start learning how to use it with videos on YouTube.
  • R: My last tool which most people are unlikely to take up is the R programming language. This is my favourite analytic tool. R is a statistical programming language common in the statistics community and used for data science. After Python, it is the second most common language used for data science and machine learning. What makes R great is that, it's a functional programming language with a lot of one line functions that you can use to do most of the analysis needed for learning and development analytics. In a previous role I used it to do all of my reporting for manadatory training. To use R you will need to download it from the R Project website. You can get a free IDE (Integrated Development Environment) called RStudio from the company with the same name, RStudio, to write R code for analysing data.

Don't wait for your L&D platform to do all the analytics you need. Get your data into another system like Excel and start analysing it. It can make a big difference in how you use the data you already have.

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