Watch this video below as PeopleInsight Co-Founder and CEO, John Pensom speaks at the People Analytics and Future of Work (PAFOW) conference as part of the Innovation Showcase. John talks about how HR can use their HR data to create meaningful people analytics and his 6 realizations on HR data.
Our job at PeopleInsight is to help HR become data-driven. It’s as simple as that - and we enable that with a predictable and accelerated business model and platform.
Over my 25 plus years in HR data and HR systems, I’ve made a number of observations and realizations that have formed my business.
I’m gonna step out of HR for a second and make an observation and use an analogy in a world we all know and have experience with - online flight bookings.
Observation: Digital Transformation and Future of Work is more about being data-first, versus transactional-first
The realization is that data has transformed the way we have engaged from a flight booking perspective. When we go and interact with hundreds of thousands of flights per day or an online inventory, the first thing we do is search, filter, and discover based on our specific criteria. The search and discovery is a very sophisticated piece of these platforms. That’s a data warehouse that’s actually processing and running that. It’s incredibly complex behind the scenes but it gets us to the actual product that we’re gonna buy.
After you’ve “searched” - you select and lock in that inventory, and process that transaction, and do the financial side and assign it to ourselves - let’s call that the “transaction” piece.
Again, the first thing that we do is “search and discover”. Second thing that we do is “transact”. The first piece of data discovery in the online world is really really complex. The second piece is sophisticated but it’s quite predictable and less complex.
Now if we flip that back to human resources and the world of human resources that we live in, what we’ve done over the last 25 system driven years is essentially just transact.
But in the new transformative business model (of Uber, Airbnb and flight bookings) it’s search and discovery first, then transact.
Yet in HR it’s always been flipped, we transact first and then we search and discover.
Not only that, we’re also using basic tools for that discovery piece in the world of HR. This is problematic and as we think about the future of work I think we have to flip that model and use our data to drive the actual transactions that we’re doing in HR.
Now each of those systems have sophisticated transactional data processing - but they aren't data warehouses.They don’t have the ability to have multi system and multi dimensional discovery capabilities which is what you're going to have to move to if you really want people analytics.
With that, here are my realizations that HR faces:
6 Realizations on HR data
1 . Don’t expect your transactional HR technology to deliver people analytics.
You have to move towards something more sophisticated to work with your transactional technology to enable that data to come together.
2. Your HR and people data is everywhere
We’re buying new products, new transactional products, we’re generating these additional separate sources which are all built with different data models, different schema and it all has to come together for people analytics to actually be enabled. The way a lot of organizations make progress here is you put a little team together and you start to move out of Excel, then move into a desktop business analysis tool, buy some stuff, play around but the capabilities of these basic DIY tools taps out really quick.
3. Do-it-yourself projects can sometimes go wrong
Do-it-yourself projects sometimes meet the need but they often don't scale. Every now and again you might actually be getting yourself to a prototype phase that cannot scale and you need to look for something different to enable the broader solution. DIY projects can sometimes go wrong.
4. HR Data management is people analytics kryptonite
Data management for HR data is incredibly complex. There’s a lot of talk about the end solutions and the end use cases, but the real science is down in the data management side of things. Don’t underestimate the complexity of HR data management.
People may say “we’ve been told with our systems we can get APIs and we can have these new integrations that can move data around”. True but those APIs were really built around transactional processing, moving small packets of data from one system to another to enable that primary view which is a “transaction”. They’re not built and designed to enable atomic level detail and movement. Atomic data management is what is necessary for rich robust multi-dimensional people analytics.
Next piece of that is the HR data itself is nested. It’s “effective dated”, it’s diverse, and even without processing any transactions in the systems - we’re constantly experiencing time-based progression in that file as every day passes.
So in order to piece it all together at all points in time from all these different data models which are structured around different timelines and different time periods, that’s complex but it’s necessary for rich people analytics.
Now, a principle of GDPR is privacy by design, so as you move from the DIY world of one person hacking and building you don’t necessarily always have the scalability to move it beyond that one person. You have to make sure that data is accessible and used and processed by the right people in your organization.
So building a one time here and now people analytics use case is really hard and achievable - but if you want to take this to production, and you want to make sure it’s securely deployed out to the organization - then that becomes incredibly more difficult.
5. There are many layers of complexity to deliver “production” people analytics.
There’s many layers to a people analytics solution - from business intelligence, hardware, security, firewall, data model design and then optimizing the data not only for processing purposes - but also for delivering the data to the user at speed of thought.
There are many pieces that are involved.
This is why our business has focused on this because that’s all tough to do. Our platform is focused around data management and solving this universal problem, which is HR data all over the place. We do this so you can make smarter hiring decisions, understand who creates value in the organization, and drive ROI.
So, from RedThread’s landscape research, we’re categorized as a multi-source analysis platform that’s built around raw untransformed data coming into our platform, it’s built around that data being mashed together at all points in time to be optimized for HR data warehousing, and it’s built around secure cloud based delivery of the right information to the right people based on privacy and security requirements.
6. Our business model and multi-source people analytics platform works remarkably well for companies <5,000 employees
The last bit I’d like to mention, is that we find that our business model and platform doesn't work for everybody.
But companies who are running with 500- 5,000 or so employees, it may be a perfect fit for many reasons, one is you have tons of data, tons of value locked in that data and the last thing is you may have limitations internally on how much you can build and how much you can scale yourself.
With these conditions, we can be a great people analytics partner for you.
Thanks to the PAFOW community and Insight 222, it’s a great initiative and thanks for propagating this conversation.