In part one of this three-part blog I touched on the initial operating model that launched many People Analytics teams. In part two I will delve into the factors driving this shift in the operating model.

What’s driving the change — Scale

To talk through the drivers here, I see one main factor driving this shift in the operating model and two factors enabling it. I believe the driving force behind the emergence of these roles is the need to scale People Analytics.

Driving factor: Service models are expensive to scale

1. I’ve heard people say there’s a flywheel for People Analytics support.

2. People Analytics Flywheel

3. Teams in HR don’t know they need People Analytics,

4. They get a taste of insights from a PA team

5. They demand more PA functions get more investment

6. Flywheel spins (return to step 2)

This is how PA functions go from a handful of reporting analysts or survey PhDs, to mega-teams in a matter of just a few years. HR as a whole has been starved for data support and when they finally get it, they want more immediately.

This flywheel may start off by supporting a key stakeholder. This is sometimes the CHRO or sometimes the parent org that owns analytics (frequently Comp or Talent). The PA team may pair that stakeholder with an analytics business partner guiding the relationship and researchers digging in to the stakeholder’s questions to understand the levers driving their business.

As the stakeholder starts to see success, not only will they ask for more support, but also the PA function will be approached by other HR teams. HR bringing data to the table is a recipe for success and word of success travels fast. Those other functions that have seen the success of that first mover will be eager to get the same level of support and bring data-driven insights into decisions about their own organisations and to their business partners.

But research and custom analysis takes time and hard-to-find skills. Every HR function wants their unique last mile problem solved. Reports end up being run and rerun with small tweaks and analysis is being done and then cut for a line of business and then redone and recut for a new line of business. New clients, their direct reports, and their direct reports all look to the PA team for support. After a while all of this white-glove work starts to add up. Adding heads to this model is the only way to meet additional demand.

In adding headcount to meet the demands of the flywheel, the investment in the service model starts to get heavy as it mirrors HR or, more realistically, the investment stops. When that investment stops, the People Analytics team has to make a call to prioritise clients and stop providing analytical support beyond a certain level in the organisation or bubble-wrap their resources to be reserved for a special set of clients.

So what is it about these product jobs that could help with scaling? As it turns out, the scaling issue for service functions is not only occurring in HR. We’ve seen this across many disrupted service industries: a garage full of software engineers at Airbnb can collapse an industry of travel agents and a startup of software engineers at Uber can collapse an industry of taxi operators that used to take phone requests to tell cabs where to go.

As the service operating model of people analytics hits a certain scale in the organisation, it starts to make more sense to invest in software to scale insights. The good news is, there have been some external factors shifting in the background over the last decade that enable PA and HR to move into this next phase of software driven scale. While HR has lagged partners before when it comes to automation, there’s an opportunity now to jump to parity due to advances in the tech space.

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Enabling Factors

I’d be remiss to skip over the enabling factors that are allowing HR to make this jump into developing products, but at the same time I could write an article about each of these factors and this blog is already getting long. So here is my rapid-fire argument as to why the environment has shifted to allow HR to make this jump into product.

Summarised: Data science, data engineering, and machine learning talent as well as computing power used to be incredibly expensive, but in recent years the cost has plummeted allowing HR to enter this space now.

Factors leading to this decline in cost for scaling HR with software

1. The other functions started to hit economies of scale for data science and data engineering talent (you don’t need the 10th data scientist as much as you need the first one.)

2. That led the talent market for product talent to normalise and saturate. This meant that HR making a request for product talent is no longer outlandish and with the growth of education in this space the supply of product talent started to look to HR as a function as a way into the field

3. There has also been an explosion in open source data science, programming, and automation tools like packages in R / Python which dramatically brings down the cost barrier to entry for HR to start developing products.

4. Combining that open source movement with a massive growth in cheap cloud processing and storage through services like AWS, Google Cloud, and Azure means that a capable person can now set up the foundation for a product in a few hours.

5. We’ve seen a dramatic rise in the capability in HR tech, which makes sense too with the drop in previous barriers of computing and talent. Over the past ten years, the value of modern HR management systems like Workday and SAP has become widely accepted and we’ve seen the emergence of data-oriented HR tech firms like Visier, Swoop Talent, OrgVue, One Model, which step in to do much of the hard lifting to organise and democratise insights. Many of these companies not only provide data reporting, but they also standardise data entry, act as the data warehouse, provide cleaning, and provide their own analytics.

This drop in barriers to entry for computing and product talent has opened the door for HR to enter the product space.

In final part of this three-part blog series I will lay out a framework for the next phase of operation - the platform operating model.  

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Other blogs in this series…

ABOUT THE AUTHOR

Richard Rosenow has carved out a niche for himself in the People Analytics Space in helping teams and companies learn about and start their journey into People Analytics. He’s a former member of the Facebook People Analytics team and is now Uber’s Senior Manager of People Analytics Operations.