A Quick Guide to Becoming an HR Tech and People Analytics Pro (Oops, Did I Think That Out Loud)

Ok, just like doing pull-ups at the gym, becoming an HR Tech professional doesn’t happen overnight. We all have to start with knee pushups.

While I cannot teach you how to go from knee pushups to full pull-ups, I can summarize the thinking patterns of some HR Tech and People Analytics pros out there so you can use them to become a pro yourself. First, I can guarantee you are doing some of these things already. Second, all knowledge takes practice to become expertise, so use this as a starting point to begin your practice. Third, this is meant to get you started in your HR tech journey; it is by no means a holistic guide.

1. Technical System Design

• Start by asking: how does this process work? (e.g., how do we hire someone?)
• If you are already asking that, then ask: what parts of the process are standardized, and where do deviations and exceptional scenarios occur?
• If you are already asking that, then ask: will it save us time/money/resources to automate parts of the process that are already standardized?
• If you are already asking that, then ask: what solutions are in the market today that can help us automate the standardized parts of the process?
• If you are already asking that, then ask: how will that solution play nicely into our existing technology ecosystem? (i.e., don’t try to do the HR equivalent of using MS Office on a Mac; some things are just not meant to be, even if they technically do work together)
• Remember: system design is like building Lego blocks. You can build them any way you like, and they will form some shape, but if you want a particular shape (or outcome in this case), then you have to build with that goal in mind.

2. Data Architecture

• Start by asking: what do I need to know when I do this activity? (e.g., what do I need to know when I onboard a new hire?)
• If you are already asking that, then ask: where and how do I document this information?
• If you are already asking that, then ask: where and how can I give other people access to this information?
• If you are already asking that, then ask: where and how can I consistently store this information?
• If you are already asking that, then ask: how can the information be captured and stored so that it can connect to other pieces of information in the ecosystem?
• Remember: data is just information segmented into smaller chunks for ease of system analysis and interpretation.

3. Integrations

• Start by asking: how would this system/technology work with the rest of your ecosystem?
• If you are already asking that, then ask: what information would it need from other systems? Or what information would it need to send to other systems?
• If you are already asking that, then ask: what format does the information need to be sent and received in, and who in your organization can get the information into that format?
• If you are already asking that, then ask: how often does the information need to be sent or received, and can the existing technical infrastructure accomodate that frequency?
• If you are already asking that, then ask: who will monitor and maintain the integrations and how they will do that.
• Remember: integration is just a fancier way of asking, “How is data passed back and forth between systems?” It’s kind of like running the pipes between your kitchen, the main water line, and the wastewater line—you need to make sure they’re all connected (and ideally without duct tape) so nothing leaks and wastewater doesn’t enter the main line.

4. Analytical Insights

• Start by asking: what is happening? (e.g., how many people left the organization?)
• If you are already asking that, then ask: why do we think this is happening?
• If you are already asking that, then ask: how can we find out if these reasons are true or false?
• If you are already asking that, then ask: what data do we already have access to, and what data do we need to collect?
• If you are already asking that, then ask: who is responsible for it and how will we monitor the data to determine if initiatives are working to influence our desired outcome?
• Remember: If you ever went shopping and calculated the price of a 50% off clearance item with a 20% additional discount that weekend and determined it was a great deal, you have done analytics. People Analytics, at its core, is about applying a statistical approach to understand whether all of your workforce-related spending is a good deal for the business or not.

5. Privacy & Compliance

• Start by asking: what minimum data is needed to make the technology function?
• If you are already asking that, then ask: who can I work with in my company to determine if it is safe to share this data? (Hint: likely your Information Security or Risk team)
• If you are already asking that, then ask: how far back in history should I go, and were there any historical activities or events that could have introduced bias into the dataset?
• If you are already asking that, then ask: where and how will this data be processed and stored once it is shared? How long will it be stored, and will I be able to request that it be destroyed?
• If you are already asking that, then ask: will this data be used for the system to make decisions, provide guidance towards decisions, or provide information for people to make decisions?
• Remember: 1) there is always a minimum dataset that is needed to make any technology function, and you don’t need to share more than that; 2) technology should always provide information or guidance for people to make decisions and not make decisions on its own; and 3) you own your workforces’ data and have a fiduciary duty to guard it with care.

To all the HR practitioners reading this, hopefully you feel more at ease about HR Technology and Analytics after reading this.

To all the HR pros in my network, I probably oversimplified a few things. I’d love to know what you would add to this.