Vision for Embedded Analytics in HR
There is a great discussion going on about ‘embedded analytics’ in HR, kicked off by Mike West’s recent blog: Why Josh Bersin is Wrong About Embedded Analytics.
Wikipedia states that analytics “is the discovery and communication of meaningful patterns in data.” In the case of HR, systems today are used to bring out the trends and patterns, but in most cases, it is left to people to interpret the data that is presented to them in form of tables and charts and to arrive at a course of action.
Mike’s description of embedded analytics in his blog stops with the premise of incorporating charts and insights inside HR systems, and he gets into challenges with sourcing data from multiple systems. He argues that embedded analytics from HR vendors, similar to a standard navigation system in a car, are not sufficient to meet customers’ needs and suggests that “we should think about our HR Technology needs in families of interconnected products supported by interconnected workflows” and “Providers should seek out data from other systems that would be relevant to create or enhance the embedded analytics in their systems”.
Josh on the other hand takes this further, hinting at the vision for embedded analytics, “that if we don’t “embed” these analytics into work then they won’t be used”.
Mike’s point of view seems to ends at the data aggregation step, which is a real challenge for many, whereas Josh continues to how that data is acted upon.
Is the car analogy the new Moneyball?
Blogs and articles with references to the book Moneyball: The Art of Winning an Unfair Game by Michael Lewis and the movie that followed are plentiful. The authors draw parallels with HR departments’ search for talent to successfully drive their organizations’ objectives of growing and maintaining the business. This is becoming real. Every year we’re seeing increased investments in HR analytics and broader adoption across regions and industries with more great success stories – see latest Sierra-Cedar HR Systems Survey. So what’s next?
In his response in the comments section of the blog, Josh provides an example of ‘actionable’ analytics in the modern car and how “somehow it seems to recognize when someone jumps out in front of me or I drive over a white line – and it ‘nudges’ me thru a beep or shift in the wheel to be careful”.
I’ve also heard Holger Mueller, VP and Principal Analyst for Constellation Research, use the anti-lock braking system (ABS) in late-model cars as an example of ‘actionable’ analytics, where the car itself can sense that another car in the front is slowing down so it automatically applies the breaks to also slow down and avoid a collision.
What’s different about the examples from Josh and Holger is how the modern car is leveraging data collected from sensors to change the driving experience and help make drivers hands-off and more safe. This is not just about a better dashboard or a smarter navigation system built into the car; the modern car doesn’t flash a chart and rely on the driver to interpret the information and step on the breaks. The modern car senses the situation and slows down on its own. Access to accurate sensor information and ability to process data quickly and take action are what makes the modern car smart and autonomous.
The modern car is being transformed into an autonomous car, a car that detects “surroundings using radar, lidar, GPS, Odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage. Autonomous cars have control systems that are capable of analyzing sensory data to distinguish between different cars on the road, which is very useful in planning a path to the desired destination.”
Image Credit: ExtremeTech
How is this related to HR?
As Josh hints, many HR vendors are working towards a similar vision with their modern systems where the embedded analytics in the modern HR systems will behave like the advanced control systems in the cars. Embedded analytics will do more than the next generation of dashboards and BI technology embedded in HR systems: embedded analytics – or advanced control systems – will offer a different way of consuming analytics and driving actions.
When put in the context of people, the number of inputs and variables multiply, making it more challenging to predict the outcomes and drive interventions. Even though analytics and visualization tools are getting simpler, better and more agile, people still have a difficult time making sense of all the information in a timely manner to determine what’s important to consider, how to interpret the information, what the options are and what the best course of action is.
There’s been a lot of talk about big data, in-memory computing, predictive analytics and machine learning. These are all among the underlying technologies that are required to be in place to achieve this greater vision. They are critical to crunch through the data and variables and determine the greatest influences for every different situation. This requires the modern HR system to take into account data from many different ‘sensors’ beyond just HR functions, such as traffic and weather, sales and finance, operations and logistics, so the systems will have to evolve to expand their reach and consume other data, processed through multitudes of algorithms to determine the best to handle a specific situation and actually initiate the best, and most viable course of action.
(Recommend reading: Algorithms: The New Means of Production, discussing algorithms as a catalyst for injecting more data analysis into a business process.)
This also ties into the debate if we still need Human Resources? The notion of an inherent and advanced control systems in HR will definitely face a lot of debate and resistance (who wants to be controlled?). This is not about self-service anymore, it’s about direct and live interaction with managers, leaders and everyone else who does work to help them grow and be more effective and productive. How can the systems provide constructive feedback to a manager or an employee just in time to adjust or correct the behavior before it takes place? How can the systems suggest the right learning to have immediate impact on performance on the job or eliminate the risk of an accident on a task, or to pave the path into new roles and for career growth? It starts with insights made available in the context of a process or transaction, then a “nudge” to initiate action, and when smart and capable enough, the system will initiate action on its own where it makes sense.
We’re still some ways from this vision of analytics becoming a reality in a scalable fashion, but progress is being made in the right direction. At the same time, new capabilities introduced in this area will certainly help increase broad adoption (not by choice, but by the fact that it’s a part of everything), and ease the load on HR professionals and analytics specialists; however, they will not be a replacement for these roles and functions in an organization.