What Does a People Analytics Operating Model Look Like? - Platform Operating Model Pt 3
In part one and two of this three-part blog I touched on the initial operating model that launched many People Analytics teams as well as the factors driving this shift in the operating model. In this final part of this mini blog series I will outline what a Platform People Analytics team could look like.
Platform Operating Model
If you were part of an enterprise tech company, you could expect to find the following functions working within the business to deliver the technology to clients
Enterprise Tech model
Product — People who build the tech product and enhance the product to meet customer needs (R&D, SWE, Data Engineers)
Solutions — Solutions teams work with buyers to ensure that they are getting maximum value out of the product.
Training — Training teams consist of specialists who help customers learn how to use the product. Usually scaled support (videos, lectures) and some custom support (on-site setup).
Client acquisition — Client acquisition (sometimes referred to as marketing & sales) works to increase market share, onboard new clients, and track portfolios of current client’s usage to ensure renewal agreements.
Specialty Research — No product serves 100% of client needs, so some shops have a custom research or custom tooling group to go the last mile
Operations — HR tech firms need an operations team to run the business of the firm. Project management, program management, business analysts, and internal operations.
Over the next decade, I believe People Analytics is shifting to a similar operating model especially when it comes to the focus on the products. By supporting a product you can make incremental improvements to a core piece of software and then sell that software 10,000 times over to scale your support instead of relying on an increase in human capital to scale the team.
I’ve put together a loose draft of what a Platform People Analytics team could look like below. Remember when I mentioned before to take these arguments with a grain of salt? This is a good time to pick up your salt. I’d love to hear your thoughts on the model below and I especially look forward to hearing if you’ve seen this in practice or if you think this is a model that your team could operate under.
Platform
The platform team is the engine of the People Analytics machine. The People Analytics platform team in this model is a collection of products, built internal and external, that are delivering analytical services to the business as a whole and the people who support them. This involves tools like Visier and One Model to scale reporting or custom built dashboards all the way to the internal analytics built into HR tech products. In this model you would even manage some 100% human processes as “Products”, but that’s an article for another day.
Each of those products within the platform would have their own product manager and for the internal products there would be teams with developers, data engineers, research scientists, and analysts. User experience skill sets would work across the team as a whole as a pooled resource supporting product teams to better understand the clients in the business. This sub-function would build, maintain, support, and understand the tools that scale people analytics into the business.
I’ve included a graphic with examples of what types of tools could fall into the People Analytics Platform area of work.
It would be unlikely that a PA team would own all of the tools listed above due to redundancy, but I wanted to show examples to put ideas out there about what would be owned by a platform team. Some People Analytics teams in the future may decide to own and build a single full stack product, ingesting data, warehousing data, and delivering data and insights back to users, but with the large upfront cost required and advancements in external HR tech, it seems more likely teams will own a blended ecosystem of internally built and externally purchased HR technology.
Types of talent required to support this part of the operating model: data engineers, product managers, data scientists, developers, visualisation experts, former HRIS, user experience,
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Solutions
Solutions is the team focused on increasing the value that HRBPs, HR line, Finance, and the business get out of the platform. It differs from partnership teams in the service model because it is a pooled resource instead of a client-allocated team. This team is brought in to maximise the value of the user experience between the enterprise clients and the Platform team.
Work on this team could include direct education on the ecosystem (where to go for what), helping HR team members start projects that need data or insights from the ecosystem (with a mindset to get the team to self-service), or making technical tweaks to the platform as needed (in conjunction with the platform group) to make the platform work for the end users.
Solutions teams help scale PA by creating a pool resource instead of a 1:1 partnership for direct client support, but they also help scale PA by allowing the platforms team to focus on the 80–90% need and filling in the gap for the 10–20% need of clients. Without a solutions team, the platform team would get overrun with direct requests from clients for custom changes to core products, which leads to technical debt, forked systems, and increased overhead on the products.
Types of talent: technical consulting, project managers, portfolio leaders, HR analytics partners, enterprise technology solutions partners.
Training
The training team onboards and trains new clients or the company at large on how to interact with the products in the Platform. While the solutions team still requires human capital to interact with the business (though less than a partner model), the training team can operate with a lean team to create scaled resources like video training, interactive demos, or FAQs that can be accessed through self-service tools.
Training helps scale People Analytics by working to get ahead of requests to the solutions team. Requests to the solutions team are a great source of data about what a training team should develop, but the training team can also measure success by how much they reduce ticket volume to the Solutions group for commonly asked questions. This helps elevate the value driven by the Solutions team, which in turn further protects the Platforms team.
PA getting a dedicated L&D / training function may not be a reality in most companies, but in those cases where they can not, a direct partnership with L&D is critical.
Types of talent: L&D professionals, consultants, trainers, designers, production staff
Client Acquisition
It may seem odd to think about building an internal marketing and sales group, but the skill-set required to deliver a People Analytics platform is different from the work of selling People Analytics. Many times I’ve seen People Analytics teams ask a PhD from a STEM background to pitch an idea for a project or the impact of a potential project to an executive in the business and while there are some rare people that can do it all, more often than not the sales skills are not present on the data science team.
Ian O’Keefe said the following during a recent talk at PAFOW west
A question I get a lot is what do you hire for. What kind of skills do you see in this space? Skills and the combination of skills is one of the first things I looked at when I joined and that we continue to look at over time.
Typically we see people come to us from four different areas and they’re deep in one and dangerous enough to be good enough in another one. I’ve yet to meet someone who has an advanced degree in mathematics, spent time managing a tech stack, has also spent time at a top tier strategy consultancy, and has also gone deep on IO psych research or team leadership. If you are one of those people, come see me after the talk.
When we let people play to their strengths we build stronger and more diverse teams. So to scale the platform to the business at large, having a dedicated team to focus on how to sell the products within the People Analytics platform to 100% of HR or the business is a critical skill-set.
It’s a team that works both ways too. They would pitch the platform to new potential clients and sell those clients on the training and solutions support structure, but they would also act as eyes and ears of the platform, doing competitive analysis and understanding customer pain points. Marketing and sales are on the front lines in enterprise tech; talking to clients every day and getting a pipeline of that information back to the people working on the product is critical.
It’s also worth keeping in mind that the flywheel of People Analytics adoption can also mean that the functions that got access early are the ones getting the most access today. When PA teams hit their limits for investment they start to prioritise and shut down new clients as they scale, which might not be maximising strategic value to the business. Building a client acquisition team can ensure that PA is delivering service to a strategic portfolio of clients.
Types of talent: UX, Marketing, sales teams, content creation, portfolio management
Specialty Research
Even with a full and functioning platform of products, the one-off and custom projects will not go away, but if the Platform, Training, and Solutions teams are doing their jobs the remaining needs from the business will be intensely complicated. This is the final 3% of projects which can sometimes consume an entire PA team’s capacity. There’s always going to be a need for someone to tackle strategic or high profile projects that don’t quite fit the platform (yet or ever) and the specialty research team acts as a SWAT team to tackle those needs.
The existence of a specialty research team also allows the platform scientists to work on scaled solutions instead of getting caught in a cycle of one-off high priority requests. By separating these teams into platform data science and specialty research, the platform team can fix root causes and the specialty research can focus on the high-profile fires. Likewise, establishing a training and solutions team ensures that this specialty organisation does not get bogged down in regular reporting or education requests.
In the long run, one additional goal of specialty research should also be to act as an external R&D function to the platform. If they find themselves doing the same project 3 times over, there should be a connection point to the platform to either find an HR tech solution or build a scalable product which can support that repeating question moving forward.
Types of talent: full stack swat team, IO psych, data science, project managers, consultants, data engineers
Operations
Lastly, the People Analytics function needs operations to keep all of the gears moving. In the service model today, we’re just starting to see operational roles appearing and I think the slow appearance can be traced back to the service model roots. Operations roles can sometimes feel like optional “self-care” for a team and it’s hard to make the choice to invest in what appears to be operational overhead when your clients are demanding more direct service support.
However, operations roles are leverage roles to increase the overall efficiency and effectiveness of the operation of People Analytics. A strong operational team to track projects and priorities across groups can help balance the workload across the team. While each sub-teams can get focused on their own silos, an operational team should be analysing across silos to keep the team balanced and working with cross-functional partners.
Platform Operating Model Visualised
Platform Operating Model Example
To illustrate, I’ve put together an example of a small unit of the platform operating model in the example below. It’s easy to picture this operating model working on a platform built around a Visier instance.
Team members:
1x Data Engineer (Platform)
1x Data Analyst (Platform)
1x Client Acquisition / Training (Client Acquisition / Training)
1x Solutions partner / Training (Solutions / Training)
1x Researcher (Specialty research)
1x Team Lead (Operations)
Software:
Workday
Recruiting System (ATS)
Visier
Tableau
In this example, the platform would consist of Workday and an ATS feeding directly into Visier. The data pipelines and data quality / integrity would be managed by a data engineer on the team. The data analyst would work with HR teams and their client acquisition coworker to develop scaled reports for the HR function based on customer need. Since the team is lean, the data engineer would also work with the data analyst to pull data from Workday and other HR systems directly to generate additional scaled reports in Tableau.
And in the spirit of keeping a lean team, the training for the enterprise on how to use Visier and Tableau reports would be split between the solutions employee and client acquisition employee. A stand alone training role might not make sense while this team is lean, but if/when complexity to the platform increases it could be added to the team.
The client acquisition employee would be charged with tracking, monitoring, and scaling Visier rollout across the company and the solutions employee would work with new teams that come on board to teach them how to build custom reports and manage a ticket-based queue of questions and requests regarding the team’s scaled Visier and Tableau dashboards.
The specialty researcher would pull data directly from all systems and Visier to generate custom analysis. They would work on projects ranging from compensation studies to diversity analysis. Their work would be at the direction of the CHRO, supporting decisions across the enterprise.
The team lead would manage the team, manage contracts for systems, work with the data analyst and data engineer to set strategic direction for scaled reporting, ensure that workload is balanced across the team, and measure KPIs for success of the team as a whole.
As the team grows, you could see the data analyst shifting into a product manager role for Visier with a few data analysts or visualisation reports working for them. Additional products like an ONA tool or a labor market tool could also be added to the Platform, increasing the need for additional product teams and client acquisition groups.
As complexity of the products grows, the need for a stand alone Data Warehouse may rise, bringing developers, data engineers, and automation experts into the picture to streamline the ingestion, cleaning, and delivery of data to the products. As the flywheel for PA projects spins, a centralised solutions team may be stood up with a ticketing system to better serve the organisation and a stand alone training function may emerge.
When you play this model forward though, the pooled client model and focus on scaled services through software allow this operating model to scale to support the full enterprise.
The shift
This isn’t a theoretical shift. I believe I’m just shedding light on this model as practitioners are starting to put it into place already. Within some teams there is an overhaul moving them this direction, but in other teams it’s a subtle shift with an increased focus on data solutions, automation, and HRIS. I’m seeing teams move this direction today and that pace is going to pick up over the next few years.
To summarise, what got us here might not get us where we’re going if we want to scale People Analytics across all enterprise clients. When they are ready to hit the next level of scale, PA teams may start to shift away from operating models that support white-glove custom reporting and analysis and invest more effort into operating models that support a scaled platform of software and helping HR engage with the software.
I’m excited to see that future unfold and to help that future unfold over the next decade and I hope you are too.
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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.