There’s no doubt you have heard a vendor or analyst drop one of HR tech’s hottest buzzwords: analytics. The term has permeated our HR tech atmosphere as of late, so much so that I’ve noticed myself starting to glaze over. So whether you’re a newbie to HR tech or just plain confused, let’s break it down!
Big Data
Before we dive into analytics, let’s start with big data. I’m sure you’ve also heard this term thrown around in all its vagueness, but what does it actually mean?
“Big Data is not merely the accumulation of vast amounts of information, but a collection of interconnected and interrelated data points that, when analyzed carefully, helps business leaders make decisions that lead to increased profitability and job creation,” writes Marco Lübbecke, professor of operations research at RWTH Aachen University in Germany and vice president of the Institute for Operations Research and the Management Sciences (INFORMS), in a commentary for CNN.
Simply put, big data is an extremely large set of information that, when used correctly, can help improve organizational performance.
The availability of all of this data can be great for your organization … as long as you know what to do with it. Big data, in and of itself, is useless unless you have some sort of analytics to make sense of it all.
It’s also important to get the right data. Before committing to more data, ask yourself whether the data will drive actionable business changes. Gathering useful data – and then acting on it – isn’t an easy task.
How does big data relate to human resources? Many HR tech vendors are offering analytic tools to help you, as an employer, improve practices, including talent acquisition, development, retention and benefit costs, among others.
Metadata
Metadata is the collection of all the data that describes other data. For example, I have a card on my keychain I scan at the grocery store. It gives me discounts. It gives the grocery store something even better. Not only does it capture what I buy, it also captures information about who I am (remember all that information on the form to get that little key-ring card?), what day of the week I’m shopping, what time of day, what time of year and so on. Do I buy specific items at certain times of the day? Do I buy milk every Tuesday; therefore, should milk coupons be mailed two days before? This metadata gives the user a better picture of who I am. So in summary, your purchasing habits would be the data that describes your personal data used to obtain the grocery store savings card. Another simple example would be the author, date created date modified and file size information of a Word document.
Workforce Analytics
Ok, now on to analytics … Workforce analytics is a term used to describe the method of obtaining data from your HR tech systems for performance measurement and improvement, as it relates to personnel. Workforce analytics can help employers determine who will be successful in a specific job, identify factors that lead to job satisfaction, optimize organizational structure and even identify and cultivate future leaders. IBM has put together some great papers on workforce analytics if you’d like to read more.
Predictive Analytics
Predictive analytics is using your existing workforce data sets to determine patterns and predict trends or future outcomes. Steve Bates had a great article in HR Magazine in September that nicely explains predictive analytics. He also brought up Independence Blue Cross, a Philadelphia-based health insurance company, that collected claims data and other routine information from their customer base. The company then built and refined algorithms and used artificial intelligence to combine the information and use that data to forecast. The company identified who was at greater risk for illness and who fails to take their meds, which ultimately drives up costs. Independence Blue Cross was able to forecast outcomes with better than 70 percent accuracy – all from the data the company collected.
Because so much of analytics depends upon past behavior and data collected, it is imperative to approach with caution. Like my financial advisor tells me, “Past performance is no indicator of future results.” Inaccurate information cannot always predict high utilization or high cost behavior.
Many HR tech vendors promote data analytics as a key feature of their systems, which is great. But it’s equipping HR professionals to pull and figure out how to use the data that becomes the challenge.
A few tips:
- Make sure your data is good. In order to get good analytics from your system, you have to put good data in it. This means ensuring your employee, position and payroll data is accurate.
- Look at the right data. Just because your system compiled the data, doesn’t mean it is appropriate for you to use in your comparison.
- Set benchmarks. Data means nothing by itself. In order to make something of it, you have to compare it against something. Establish benchmarks based on company or industry standards.
What will be the downstream effects of the availability of all of this data? How are you using big data and analytics in your business? Share by commenting below.