Future Proofing the Future of Work

A few weeks ago I gave a presentation at the Talent Strategy Institute’s Future of Work conference (now PAFOW) in San Francisco about how I see the long term relationship between data and HR Technology. Essentially, I was talking through my thought process and development that I could no longer ignore and had to go start a company to chase down it’s long term vision.

So here it is.  

My conviction is that we need to (and we will) look at the relationship between our data and our technology differently.  That essentially the two will be split. We will choose technology to manage our data and our workflows as we need it. We will replace that technology as often as our strategy and our business needs change. 

Those that know my team, know that we have a long history of working with HR data.  We started at Infohrm many years ago which was ultimately acquired by SuccessFactors and shortly after SAP.  Professionally this was fantastic, worlds opened up and we were talking to many more organizations and the challenges they were facing across their technology landscape.  

How to achieve data portability.

Over time I was thinking through the challenges our customers faced, a large one of which was how to help grease the wheels for the huge on-premise to cloud transition that was underway and subsequently the individual system migrations we were witnessing across the HR landscape.   

The pace of innovation in HR was not slowing down.

Over the years hundreds of new companies were appearing (and disappearing) in the HR Tech space.  It was clear that innovation was everywhere and many companies would love to be able to adopt or at least try out this innovation but couldn’t.  They were being hampered by political, budgetary, and other technology landscape changes that made any change a huge undertaking.

System migration was on the rise.

As companies adopted the larger technology suites, they realized that modules were not performing as they should, and there were still gaps in functionality that they had to fill elsewhere.  The promise of the suite was letting them down and continues to let them down to this day. This failure, combined with the pace of innovation meant the landscape was under continuous flux.

Fragmentation was stifling innovation and analytical maturity.

The big reason to move to a suite was to eliminate fragmentation, but even within the suites the modules themselves were fragmented and we as analytics practitioners without a method for managing this change only continued to add to this.  We could adopt new innovation but we couldn’t make full use of it across our landscape. Ultimately this slows down how fast we can adopt innovation and downstream how we improve our analytical maturity.

All HR Technology is temporary.

The realization I started to come to is that all of the technology we were implementing and spending millions of dollars on was ultimately temporary.  That we would continue to be in a cycle of change to facilitate our changing workflows and make use of new innovation to support our businesses.

This is important so let me state it again. All HR technology is temporary.    

We’re missing a true HR data strategy.

The mistake we were making is thinking about our technologies and our workflows as being our strategy for data management. This was the problem.  If we as organizations could put in place a strategy and a framework that allowed us to disconnect our data from our managing technology and planned for obsolescence then we could achieve data portability.   

We need to understand the data at its fundamental concepts.

If we know enough to understand the current technology and we know enough about the future technology then we can create a pathway between the two.  We can facilitate and grease the migration of systems. In order to do this effectively and at scale you had to develop an intermediate context of the data. This becomes the thoroughfare.  

This is too advanced a concept for organizations to wrap their minds around.

This is a powerful concept in essence and seems obvious, but trying to find customers for this was going to be near impossible.  We would have to find companies in the short window of evaluating a system change to convince them they needed to look at the problem differently.

Analytics is a natural extension.

With the intermediate thoroughfare and context of each of these systems you have a perfect structure for delivering analytics from the data and powering downstream use cases.  We could deliver data to vendors that needed it to supply a service to the organization. We could return data from these services and integrate into data strategy. We could write this data back to those core source systems.  We could extend the data outside of these systems from sources that an organization typically could not access and make use of on their own. Wrap all this up in the burgeoning advanced analytics and machine learning capabilities and you had a truly powerful platform.  

We regain choice in the technology we use.

In this vision, data is effectively separate from our technology and we regain the initiative back from our vendors in who and how we choose to manage our data. 

An insurance policy for technology.

With freedom to move and to adopt new innovation we effectively buy ourselves an insurance policy in how we purchase and make use of products.  We can test; we can prove; we can make the most of the best of breed and innovation that has been growing in our space. If we don’t like we can turn it off or migrate-- without losing any data history and minimizing switching costs.

This is a long term view of how our relationship to data and our vendors will change.

It is going to take time for this view to become mainstream, but it will. The efficiencies and pace that it provides to change the direction of our operations will deliver huge gains in how we work with our people and our supporting vendors.  There’s still challenges to making this happen. Vendors young and old need to provide open access to your data (after all it’s your data). The situation is improving but there’s still some laggards. The innovative customers at One Model bought us for our data and analytical capabilities today, but they know and recognize that we’re building them a platform for their future.  We’ve been working with system integrators and HR transformation groups to deliver on the above promise. The pieces are here, they’re being deployed, now we need to make the most of them.