More and more companies are starting to realize the need to plunge into their employee’s data to uncover actionable insights and use that to potentially influence business strategy and decisions. Here, an employee’s data can be related to their demographic traits like gender, age, nationality, etc., or employment defined like attrition, tenure, compensation, job grade, roles, the organization they report into, etc., or education defined like highest qualification, university, and so on. Making sense of these data could help an organization better understand their employee’s DNA, like, how diverse they are, help drive metric goals in recruiting or retainment, etc., to achieve corporate milestones.



A lot to take in? This was really just a comprehensive framework around the word People Analytics. Also, this field, in my opinion, is still a niche field, not as abundantly embedded within organizational HR/People teams yet, which (I think) is good because that gives opportunities for mouldable scopes to whatever brings maximum business impact. But, for someone interested to venture into this field just for exploration, or more seriously, make a career out of it, what is the scope generally like? What tools/skills are needed? Where to start? I’ll try to elaborate exactly on these points in this article, and hopefully, that can give some head start.


How I hopped on board this journey



For me, it hasn’t always been about analyzing people data. Graduating with a Master’s degree in Engineering, followed by being an Engineering Data Analyst, could have potentially laid the foundational brain wiring to naturally “think analytically” and be skeptical, questionable, curious when it came to numbers. However, that alone hasn’t been enough to survive and then thrive in the people analytics field. It has, and most certainly will continue to be, a continuous learning process and upskilling and needs passion plus grit to go on.


Clearing the air on the cliché


A popular cliché around people analytics (like perhaps data analytics too) is that analytics is just the process to uncover insights, in simpler terms, to convert data into actions. But in my experience, it has been that and much more, and it really depended on how much more I could deepen my knowledge by venturing into new areas.


In the content to follow, I’m taking the context of data analytics here because, in my eyes, data is data; the difference lies in what type of data we use for analysis. And another disclaimer is that whatever is shared below doesn’t mean someone aspiring to do people analytics must know it ALL because this is really broad and meant to outline the most common use cases of what can be/is done with people data. The company you join might have dedicated team members to concentrate focus on certain areas only. For situations where this is totally new to the company, they may be looking to hear how you can value add. It’s then worth understanding all options and venture according to stakeholder pain points resolution.



Online research shows data analytics pillars (broadly speaking) have included focus in the following areas. I’ve additionally listed some tools and skills applicable to each of them. They are certainly not an exhaustive list, but most of them are what I’ve used in actual work to get stuff done.


Data analytics pillars — taking a stab at the “whats”


I can’t go on without calling out before anything that foundational knowledge of Excel is so useful before getting into anything fancy. Excel so beautifully puts data structurally into rows and columns, and its friendly UI features allow you to get quick answers. The most common features I’ve used extensively are pivoting and vlookup.


  • Data governance — Efficient end-to-end data pipeline management. This is where the data is molded and made available, involving heavy lifting in standardization, automation, and maintenance by understanding stakeholder needs. Some specific examples could be when a company is moving to a new HRIS (Human resources information system) and thus embarks on understanding current and future data needs, collates requirements, accesses the new HRIS’ capabilities, and moves forward with the change or following similar efforts towards creating a database or data warehouse.
  1. Tools - SQL (for the database), navigating around the HRIS opted by the company
  2. Skills - Perhaps program (& vendor, if applicable) management


  • Analytics — Data science meets storytelling to address business problems. What distinguishes analytics from reporting for me is the element of data science knowledge needed for the former. If the task at hand is just visualization (converting numbers to convey a story), then using a dash-boarding tool would be more than enough. But, complex analysis having to go deeper into the AI realm falls into data science space.
  1. Tools - Programming language like Python (this is what I use) or R
  2. Skills - Problem-solving, statistics & mathematics concepts understanding, storytelling, visualization, the art of storytelling


  • Reporting (or dash-boarding) — Takes care of pulse checks on data that needs to be continuously monitored. Super useful to monitor key metrics for an organization (like a scorecard) and quick data visualization.
  1. Tools - Tableau, Power BI
  2. Skills - Visualization, the art of storytelling


  • (Bonus) Business process automation — Simply put, this is the automation of repeated tasks. This, to me, is a bonus to the conventional three above. And has become a popular trend based on simplicity pitching, especially to non-technical crowds like HR (as is still the widely believed truth in the market). One specific example is using RPA (robotic process automation) to create automation workflows targeting repeated logic-based tasks.
  1. Tools - UI Path is perhaps the most popular software out there for RPA and adapted by companies
  2. Skills - Programming fundamentals to understand commonly built activities on Ui Path involving variables assignment, looping, etc


Starting from scratch? Fret not; you’re not alone!



I’ve actually done a combination of these, researched on the most widely used tools in the market, and did self-paced training to practically use them at work, as well as researching on the best tool/skill for the task AFTER having a business problem on hand, then picking that tool/skill and delivering. A combination example of this was when I started training on Python out of interest to automate tasks, specifically for data wrangling, but then eventually had the opportunity to work on predictive analytics, thus requiring me to train on Machine learning. I’ve picked up all the tools I’ve ever used in this field through foundational training reviewed on the Udemy platform.


The ultimate bottom line



To wrap up, I would say for someone who feels fuzzy about numbers, stats, maths, and most importantly, unable to pick up new skills constantly, then this will definitely be a far shot. I can guarantee from personal experience that not being able to code will NOT be a stopper to get into this field because, coding like every other thing we get good at eventually, comes with a lot of practice. An educational background in computer science or analytics could definitely provide a foundation, but I strongly vouch that this is in no way is mandatory.


Work can get done with rigorous practice and thanks to the abundant online resources available. So, if you’re someone that’s looking to take this winding continuous learning journey, becoming a Sherlock Holmes investigating data and reporting insights, have the OCD to bring structure to data and relay insights, to be wowed by how the advancements in technology can transform human resources, then, by all means, hop on for the adventure of a lifetime! ☺

* Disclaimer - The article reflects the perspective, views and opinion of the author only, and not of 'thebulletinbox'. For more information, please visit our terms & conditions.

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