I decided to sit with the file and go through it line by line with them. I am sharing my train of thought during parts of last night’s conversation in hopes that it will help someone else think about how to design their system to better facilitate data collection and analytical insights in the future.

With HR being a creature of habit, we often do things just because we feel like we must and because everyone else around us is doing it. It’s like putting on a performance and acting in the HR Theatre; we don’t quite know what our actions mean, but we are working from the script that’s been handed to us, and we are making our organizations go through the dance with us.

So, we have some great news and some quirky news in People Analytics. The great news is that more and more organizations are adopting People Analytics and hiring dedicated resources. The quirky news is that if you ask ten organizations where People Analytics belongs on the org chart, you will probably get ten different answers.

Humans are wired to detect patterns. This exact pattern-detecting ability can also be applied professionally—especially in HR—because this profession relies not only on textbook knowledge but also on experience and contextual knowledge.

Please don’t make your users equate your great People Analytics solution to mean that they have to do extra work to use it.

We have always said AI is going to revolutionize the way we work. This sentence should no longer be in the future tense for HR teams globally. AI has changed and continues to change the world where we work and manage human resources. I realized over the past few months that this time, it’s different because AI is not a function-specific trend that’s unique to HR; it is changing how everyone works and accesses information.

There are many ways to categorize HR teams and HR leaders. In my (possibly overly) simplified view of the world, I usually like to categorize based on the Span of Stuff. The Span of Stuff, in my perspective, is defined by the amount of periphery topics and projects HR chooses to participate in that do not present as HR problems in the beginning but can have significant impacts on the organization.

Okay, let’s start by acknowledging that there may be a few “sales gurus” on Instagram / TikTok / LinkedIn who probably provide you with sales tips that don’t work with real customers. Otherwise, I fail to come up with an explanation as to why most of my poor experiences are so consistently scripted with incredibly similar points of cringe.

I usually have a few ideas that don't quite make the cut for each article I publish. Since we are already ten articles into the series, I thought creating an "honorable mentions" list for the thoughts that didn't quite make it to the article stages would be a good idea.

I have worked in People Analytics across multiple organizations for over seven years. Here are ten things I learned the hard way.

This article is not a comprehensive guide to AI in HR. My goal is to simplify and condense my personal learning experience of artificial intelligence in HR into an article. I hope this article will help other HR practitioners interested in learning about AI expedite their journey.

There is such a thing as Leadership Privilege. This article is written with the hopes that leaders—especially HR leaders—will read it and hopefully become more aware of the unconscious biases and other factors that could be influencing their decisions. Even if one person makes a small change in their decision-making process because of this article, it will be well worth the effort.

Ask anyone who’s been in the HR Tech industry for 3+ years and they will tell you that the market's complexity and pace of growth is greater than ever before. The speed at which new vendors are coming to market, and well-established vendors are launching new functionality is astonishing.

As someone who has been either on the Tech side of HR or the HR side of Tech for most of my career, I have observed both sides extensively and understand both perspectives. Most of the time, both sides are reasonable. Sometimes one side just doesn’t get it! This article focuses on the perspective of the Tech side (I promise I will write one for HR soon).

When I started my career in People Analytics, never did I imagine that one day I would become an expert resource on the topic, let alone be able to advise those who are starting their journey in this field. Having had numerous conversations with folks who are starting out in this field and reflecting on my own journey to date, here are the seven lessons I have learned that I would share with my younger self in no particular order

When it comes to relationships, partnerships, conflict resolution, etc. I borrow my views from a Chinese proverb that roughly translates to ‘you can’t clap with one hand’ (trust me, it sounds better in Mandarin!). In practice, this means that in any sales conversation / partnership, it takes both parties to create a pleasant (or poor) experience.

This is a Hard-Lessons-I-Have-Learned-So-You-Don’t-Have-To and Nobody-Will-Say-This-Out-Loud-In-The-Workplace kind of article.

Here is the highlight reel of my thoughts on some comments I have heard in a professional setting, from someone in what would typically be considered an HR leadership role, and where more than one person was in the audience.

This one is for all the Account Executives and Sales Professionals out there: your customer is just a human looking to connect with another human.