This post is going to be all about my experiences getting to grips with learning about this new-fangled thing known as “Data Analytics”. If you’re a Payroll, HR, or related professional reading this I’m sure you’ll be familiar with the requirements of continuous learning. There seems to be barely a week that goes by without news of a new legislative requirement, organisational concept, or technological innovation to get up to speed on. I mean, I’m writing this on a Microsoft Word program that now has AI capabilities to help me write better (which I’m going to blame on for any poorly constructed passages!). In short, a constantly evolving environment is something we’re all used to.
In recent years and months something else has appeared over the horizon and is now vying for our attention more and more as something we need to be learning about, and that is “Data Analytics”. It seems one can barely look at the news, browse social media or open a professional publication without seeing phrases like “Big Data”, “Machine Learning” or “Data Science”. Pretty soon all these terms can become quite overwhelming. Furthermore, whilst some industries or professions like finance, manufacturing, marketing, or sales have a longer history in using these tools and techniques, HR and payroll have arguably less experience in applying data driven analytical techniques to our fields. This has a certain irony given the volumes of data we hold and process! But I think it’s partly explained by a lack of a real understanding of what Data Analytics is and exactly how it can help in HR and related fields.
It’s this very thing that first tripped me up when beginning to explore how I can improve my Data Analytics skillset – it was an awareness that analytical skills are becoming more of a must have but coupled with an uncertainty about where to begin. What follows below will be a reflection on things that have helped me acquire a foundation on how to approach my learning in the hope that it will help others.
The first thing that helped me was coming to an appreciation that before jumping into trying to learn what Data Analytics is, what techniques to learn (e.g., statistics, Data Mining, Machine Learning) or tools (SQL, Tableaux, Python) I needed to have a thorough understanding in my own mind of what I was learning this for. What exactly would I be doing with it? If we look at good old trusty Wikipedia for a definition of Data Analytics, we get the following:
“Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making”. (My emphasis).
I think the important parts to focus on here are the ones I’ve highlighted. They are about understanding any analysis that you do has to, first and foremost, support the business, in a useful way, to solve real business problems and meet real business needs. This means, before beginning any kind of analysis learning or work (the first part of the definition above), you should aim to understand how your business could or would use the data at your disposal to answer real business questions.
Click here for one of the best articles I read that really helped crystallise this for me, I would highly recommend it.
When attempting to do Data Analysis in a bid to raise the profile of HR and Payroll functions it can be really tempting to begin with the data and worry about the purpose it can serve later. You can end up looking at the data you might have on, say, employee absence rates, overtime per period or Business Unit, or leavers in the last year; then start producing reports and lots of nice-looking summaries, cross-tabs and charts, and then sending them out to the business, without thinking about whether they are actually of any real value to your organisation. Are you sure that you are helping to solve a real business question or do you just hope you do? If you aren’t answering any questions, you might get a very muted response at best, which can have a detrimental effect on your motivation and how the company views your function – the exact opposite of what you want.
So – reach out to your organisation; managers and other stakeholders first and ask them what problems they face or questions they want answered and then investigate the data you hold to see how you can help them – this has certainly benefited me, and it’s shaped how I’ve approached my learning.
Keeping the first consideration in mind, you need to develop a bit of a filtering mechanism, firstly about how to decide what data to select, but then to judge what tools and techniques to learn. I’ve found if you search online about how to approach this, you’ll quickly become overwhelmed with opinions. For example, there is a long running debate in the Data Analysis community over the superiority of two of the primary Data Analysis tools – R and Python. The debate seems to be never ending and when I first became researching, I didn’t have a handle on how to decide. Then I came across some advice that made a lot of sense. It was about focusing on the foundation of what you know and building from there. It sounds simple and obvious but it helped me cut through the confusion.
So, I’ve started out by focusing on what I’m most familiar with. For me, in terms of tools, I’ve focused on trusty old Microsoft Excel, the tool I’ve used a lot over the years in Payroll. After that I began researching learning resources that focused on using this for Data Analysis, and quickly found that there are a huge number of really great tutorials out there, written by real Data Analytics professionals, that show some really useful things, that will add value to a business and allow you to focus on answering questions for your organization using Excel (or something else you know). The advantage I’ve found for this is it helps you get to grips with fundamentals without being distracted by having to get to grips with how functionality works in an unfamiliar application. Using a familiar tool certainly needn’t be the end point of the journey, but I’ve found it can be a great first step.
Furthermore, taking this approach has allowed me to re-think the idea of myself not as someone who wants to become a “Data Analyst” or “Data Scientist” necessarily, and getting overwhelmed with the long list of skills to learn – statistics, Machine Learning, etc., (which is easy to do), but as a Professional who wants to begin acquiring some Data Analysis skills to further enhance their impact in their existing role, and is interested in the quickest way to get there.
One of the best examples I’ve learnt recently is to construct Process Behaviour Charts in Excel, with some simple formulas and Chart tools. These are easy to make and can be useful everywhere to model and monitor Business Processes. I highly recommend anyone to research them. Another example is focusing on the kinds of data modelling you might do everyday in HR or Payroll without you really thinking about, that could then be used as a starting point for further analysis e.g., segmenting Data according to a Hierarchical Organisation Structure and then using Pivot Tables in Excel to analyse differences by some pre-defined business metric. Learning things like this I’ve found to be a great way to build confidence and allow you to concentrate on business questions.
My final tip in this post is about how to select learning resources to help you further your Data Analysis skills. Again, the main problem I found was the sheer volume of tutorials, learning platforms, boot camps, the list seems endless. After having sampled a few, I found the best approach for myself is firstly, given what I’ve said above, finding ones that use tools and techniques you know a bit about as a starting point. They’ll hopefully be familiar enough not to be too intimidating but will usually contain enough new knowledge to keep you interested. What you’re looking for is something that will push you out of you comfort zone just enough.
Having said that it can be difficult to achieve that on the first go, so perhaps look for things that have free offerings or allow you to sample them for free to get a taste of what they’re about. I’ve found some free resources are first rate, others less so, so be prepared to shop around. I would recommend starting with trusty old YouTube to begin with. Many learning providers offer free samples of their material there.
Secondly think about how you learn – do you like to follow video lectures, or do you prefer written material? I personally have found I prefer video lectures delivered by someone with an engaging and amusing style of delivery – but then that’s just me. Also think about what kind of testing regime you want – short sharp quizzes or longer assignments? Are you looking for certificates to showcase to your employer? If so, then perhaps research which ones might have more cache with employers in your field.
Given the huge formulas of data now being created around the world Data Analysis skills are likely to become as crucial for employability as literacy and numeracy, so getting a good start on your learning, I think is vital. However, this needn’t be daunting or insurmountable. Keeping your studies targeted and with achievable goals should really build your confidence. I’ve found if you learn something useful, no matter how small it may seem, it can go a long way.
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