2. Has the Role of HR Technology Changed to Meet the Demands of the Quantified Organization?


Stacey Harris: Getting Up Close and Personal Right Now — The New HR Technology

Some of the fastest-growing areas of technology in general have also had an impact on getting HR the kind of data to help it make better decisions faster — and help employees do the same.

Embedded analytics

In a nutshell, embedded analytics is the integration of analytic content and capabilities within business process applications. When it’s bolted onto HR systems, embedded analytics give HR access to a whole world of data that was previously hidden away in other parts of the organization.

Embedded Analytics Defined

“Embedded analytics is the use of reporting and analytic capabilities in transactional business applications. These capabilities can reside outside the application, reusing the analytic infrastructure built by many enterprises, but must be easily accessible from inside the application, without forcing users to switch between systems. The integration of a business intelligence (BI) platform with the application architecture will enable users to choose where in the business process the analytics should be embedded.”


In-memory databases or constantly available data

Real-time data is what’s needed if HR hopes to become more proactive and forward-looking. In-memory and constantly available data allows HR to perform the kinds of analyses to understand the root causes of events ranging from drops in sales to tardiness. It puts your data into context.

An in-memory database (IMDB, also known as a main memory database or MMDB) is a database whose data is stored in main memory to facilitate faster response times. Source data is loaded into system memory in a compressed, non-relational format. In-memory databases streamline the work involved in processing queries.

In-memory Databases Defined (IMDB)

“An IMDB is an analytic database that is a read-only system that stores historical data on metrics for business intelligence/business analytics (BI/BA) applications, typically as part of a data warehouse or data mart. These systems allow users to run queries and reports on the information contained, which is regularly updated to incorporate recent transaction data from an organization’s operational systems.”


Ever-more granular data

Data can be overwhelming. It can feel like we have too much data and too few answers. But it also allows you to connect the dots of data that could matter most. Data as granular as how often someone walks into a room, the sales that are occurring in real time for one discrete location, or actions by workers on the front line of the work process.

Steve Boese: Refining the Old, Making the New More Adaptable

Many of the most common HR technologies that are starting to deliver richer data and better analytics have been around in one form or another for years. Others are newer and are trying to solve HR problems by applying predictability and repeatability.

Assessment tools, for example, have been around for decades, but only in the last five or six years have they been able to apply a scientific method to assessments to help HR make better hiring predictions and project more accurate performance outcomes.

Maybe it’s not being done explicitly, but HR technology can start to answer hypotheses that can help you be more forward-thinking. For example, “If we knew the answer to this or that specific question, what would we do? What would we change? How would we roll that knowledge out to the organization?”

Especially in high-volume hiring with repeatable job functions — retail and call centers are two good examples — it’s easy with today’s assessment tools to see who your best people are and what sets them apart. That kind of information can help HR improve retention and performance, reduce costs, and enhance processes, all based on profiles of employees who do good work, and then trying to match those characteristics and attributes in the recruiting process.

HR technology is also becoming more adaptable. Solutions are able to “learn” over time about an organization’s processes or workflow. They can scale and move with an organization’s changing needs (if HR needs more complex data or needs to target new data) or to meet changing business goals and strategies. Many HR vendors are working on predictive technology, especially to improve retention as the demand for high performers continues to increase.

Addressing the Demand for Predictable Metrics

For example, using data that uncovers common denominators among former employees, you might be able to predict when someone is likely to leave. It might be that as a group, they didn’t get any pay raises in two years because of low performance, or maybe a well-liked manager left.

If you can analyze enough data looking back, you can predict a model going forward. You can be predictive and then recommend actions to prevent the same scenario from happening again.

That said, it’s also important to be able to tweak your model going forward, which isn’t as easy as it may sound. Why? Because people are simply not that predictable.

Robin Schooling: Moving From the One to the Many

In a data-enabled, quantified organization, HR is now able to move beyond the individual toward a view of the whole enterprise.

In the past, for example, we may have taken a view of performance-management improvement on a case-by-case basis, which was the premise of performance appraisals. Yet we know that to create something that has a positive impact on the entire organization, we must move beyond that micro or singular-employee view.

To meet demands for improved performance, higher productivity, greater engagement, and more, HR technology has evolved to enable HR to:

  1. Increasingly gather data from disparate sources
  2. Monitor the effectiveness of processes and programs
  3. Tweak or innovate processes and programs in real time

What Can HR Technology Do Today? Take a Look.

  • Assist us in assessing capabilities and the readiness of the organization for change.
  • Provide us with the opportunity to visualize our data to more effectively analyze and take action; we no longer need to parse Excel spreadsheets.
  • Help us adopt Agile business practices and move at the same pace as our business. We can review, adjust, and shift on a monthly basis as opposed to biannually or annually.
  • Meet the needs, desires, and work styles of employees who already operate in the “quantified self” model; these individuals are incorporating technology to collect bits of personal data (e.g., food consumed, hours slept, steps walked, etc.), track that data over time, and use that knowledge for self-improvement.

As C-suite leaders realize what HR technology can do, it will lead to requests and demands for HR leaders to incorporate data in a more business-aligned manner.

An Example

An organization with a geographically driven field sales staff, such as a B2B route sales company (think Sysco, or a beverage distributor) could pull together sales and revenue data from existing accounts, add in data about prospective customers within a specific route or geographic region (because the sales department certainly has this data), and align that with current workforce deployment and staffing models from HR.

The result: the ability to determine if sales territories are set up appropriately, which areas require additional sales staff, and where it may be appropriate to reduce or re-assign sales staff.

Paul Hebert: Shouldn’t HR Technology Be Pointing to the Road Ahead?

What HR should be demanding from technology today is the ability to get “directional clues” — data that will tell HR where to explore to get to the root causes of events and workforce trends, rather than simply giving HR more decision points.

For example, let’s say 38 percent of your people are routinely late in one area of the business. That’s great data. What do we do with it? We make it a managerial problem. We say it’s the fault of that area manager and we fire him. Then we have the data to prove he was a bad manager (because 38 percent of his people were routinely late).

But what if the roads were all torn up on those days when people were late? What if there were other mitigating circumstances that made it impossible for any manager to have their people show up on time over a certain period?

Sometimes, the way we use data is like the old saying about the way a drunk uses a lamppost: for support, not illumination.

HR technology needs to help HR get to why the data says what it says, not just set HR up to react to the data. The real work is what you do after you get the data. HR needs to understand data. Managing it comes first — dashboards and reports. That work’s been done. But then you need to get back to the core of the situation.

Until you show somebody why it’s important to understand the meaning behind the data they’re seeing, they’ll default to the statistics at face value. Because that’s logical. And humans like things to be logical.

Lance Haun: Does HR Have the Tools It Needs? That’s Not the Right Question.

Do we have the tools now to help quantified organizations meet their own internal and external challenges with data? The answer, with absolute, 100 percent certainty, is yes. From the biggest ERP providers, to the vendors of specialized business intelligence tools, to HR-specific analytics technology, the tools have evolved for the organizations that are willing to take them and give them proper measure in their organization.

The real question is: How many of those tools are working to their full potential for HR?

How many data-gathering software solutions are truly helping HR leaders to make better decisions or draw crucial conclusions they wouldn’t otherwise be able to draw. How many of these tools are helping HR otherwise fulfill the promise of their — often-audacious — price tags?

Not many. Why? Because we’re at the very beginning of a product life cycle for many of these tools for HR.

In the Beginning, HR Wasn’t Into Data

Remember, quantifying the organization had been the exclusive domain of incredibly large, expensive ERP providers. Their programs lived on mainframes that generated heat and collected dust in the basements of expansive corporate headquarters.

HR initially wanted no part of that mess, and as a result, people were categorized by where they worked, whose budget they counted against, and what their full financial load was to the organization.

HR technology today is in the early stages of an exciting change with some exciting pieces already in market. Organizations are starting to understand that the location of a person might be less important than their level of potential or their skills and competencies.

We’re seeing that if HR is going to quantify something about our people, it should be the important parts of what they bring to the organization — not just their financial burden.

That’s what we should be looking for over the next five years from every major HR technology provider.