A great example is what happened with the vacuum cleaner. How can anyone reinvent a product that has been around for 115 years? Well, James Dyson did. He thought vacuum cleaner bags diminished suction power and built a “bagless” vacuum. At first, consumers assumed “a vacuum is a vacuum” and competitors like Oreck ran ad campaigns saying that bagless vacuums were unsanitary. Today, Oreck is bankrupt and almost every vacuum cleaner on the shelf is bagless.

A paradigm shift is “a fundamental change in approach or underlying assumptions.” And the world has experienced many of them. However, consumers and companies often don’t realize there is a shift happening until they feel the ground shifting beneath their feet.

In video games, for years, traditional game controllers used joysticks and buttons. Nintendo introduced a paradigm shift with embedded motion sensors, enabling the controller to be used as a bat, sword, wand, fishing rod, gun, tennis racket and more.

The list goes on:

  • Taxi hailing vs. Uber
  • CDs vs. iTunes
  • Blockbuster vs. Netflix

So what does this have to do with contingent workforce analytics?

At IQNavigator, we would rather create a paradigm shift than be impacted by one. So we are focused on reinventing contingent workforce analytics.

I’ll describe the difference between simple workforce reporting and advanced analytics, but like all paradigm shifts, the only way to truly understand the difference is to experience it. I didn’t know the value of the Dyson vacuum, motion-sensor controllers, Uber, iTunes or Netflix until I started using them.

The Business Intelligence Ceiling

I’ll talk about the limitations of business intelligence (BI) and reporting before I discuss advanced workforce analytics, but note that BI and reporting have tremendous value; we continually invest in them as an offering. However, the largest limitation is that they are manual—a human is required to interpret and communicate reports. Companies hire analysts to create operational reports, interactive reports and event alerting. This is valuable, but there is a limit to this value. I define the ‘BI ceiling’ as:

The point at which deriving additional value from business intelligence becomes cost prohibitive due to additional resources needed—either multiple data analysts, data scientists or both.

BI value is dependent on the analysts you hire. At best, you have talented analysts who interpret value from the data. At worst, you have a junior analyst new to the job or industry, who unintentionally makes wrong assumptions about the data they are viewing. You can be left with nonactionable or inaccurate reporting.

Advanced Workforce Analytics

Advanced workforce analytics is a new domain—it is inherently more accurate, insightful and actionable than BI and reporting. It requires “Big Data” data science focused on statistics, data mining and predictive analytics that automatically extract insights from structured or unstructured data.

The results of advanced workforce analytics are more powerful. They move from traditional “rear-view-mirror” reporting to descriptive analytics, predictions, automated suggestions and program optimization.

Think about how key performance indicators (KPIs) or metrics are managed today. In BI reporting, a person makes up a KPI—it’s completely subjective. BI reports on performance against that subjective KPI.

By contrast, advanced workforce analytics can automatically recommend KPIs based on data science and desired company outcome. As data and business goals change, so do the recommended KPIs.

At a previous company, we launched a telesales program for a laptop manufacturer. Our customer required a KPI of 60 outbound sales calls per day per sales rep. Why? Because that’s the metric they had used for the past 10 years. We deployed revenue analytics and discovered sales associates making 40 calls a day sold more laptops. So we went to our program manager and asked, “Do you want to make 60 calls a day or sell more laptops?”

Instead of optimizing our program to generate more revenue, we were optimized to make more calls in a day. While our BI reporting bar graphs looked great and showed “progress,” we were actually leaving revenue on the table!

Welcome IQN ATOM

Our VP of Data Science, Anne Zelenka, and her talented team of data scientists have delivered an industry first in workforce analytics: ATOM (Automated Talent Ontology Machine). It’s a cognitive-learning machine that understands the talent domain—think of IBM Watson for talent.

Initially, ATOM will help our clients with real-time recommendations on rates, time-to-fill, and labor-demand recommendations and predictions. ATOM will also assist hiring managers in determining the right talent and skillsets needed for their strategic projects—no expensive analysts needed. And ATOM will be capable of so much more. We’ll talk more about ATOM in future blogs and news releases.

I’m excited about what advanced workforce analytics will do for our customers, partners and this industry. I recommend that everyone start researching this paradigm shift in analytics within our industry, or you may get caught on the wrong side of a paradigm shift…

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