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TTL LookBack V3: Why Market Analytics Offer Critical Context for Big Data

Welcome to Talent Tech Labs Lookback. We spend a lot of time exploring the Talent Acquisition Ecosystem and Marketplace and while every TTL Trends Report focuses on a different theme, we occasionally like to look back at some of our greatest hits.

Your HR department is sitting around a table, perplexed by a recent spike in people quitting. Is it a manager issue, a pay issue or possibly a new competitor in town? Maybe more team activities are proposed to solve the problem, or perhaps an expanded benefits package. Your data shows that the current numbers are worse than last year. Fixing the problem will cost money and your budget is tight. You definitely have a problem, but the problem is not your turnover, it is that you have no perspective outside of your own company data.

Your HR dept is perplexed by a recent spike in people quitting, turn to big data. Take a look back at our Trends Report V3 on Big Data Market Analytics!
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The conundrum of big data and context

So much of what we know as big data is internal, and internal data has very little perspective. This is especially true in total talent management, where knowledge of external economic trends is critical. Let’s dig into the importance of contextual analysis of big data through some examples.

Here is a question asked by companies of all sectors: What percentage of my worker population should be contingent? While the question is common, it is actually flawed. It shouldn’t be about how much of an organization’s worker population should be contingent. Instead, it should surround how much of a contingent workforce the organization is able to utilize. During the real estate boom phase, and likewise through the default/loss-mitigation phase, it became almost impossible for banks to find qualified loan processors and loan underwriters. Because employers knew that both phases were sure to come to an end, their desire was a largely contingent workforce. However, the extreme tightness in that labor pool meant that permanent options, and the benefits and job security that came with them, were plentiful to job candidates.

Thus, banks were forced to hire more permanent employees. In weak economies, low- and high-end workers have opposite desires for contingent work. Low-end workers see contingent work as a way of keeping food on the table while high-end workers seek stability. In strong economies, low-end workers seek stability and high-end workers seek the freedom and higher money of short-term assignments, such as those available to consultants and freelancers.

Knowing this, you should ask yourself these questions:

1) Is the industry and local economy I am in facing tight or loose economic conditions?

2) What skill sets would I want contingent and what do I need permanent?

3) Which skill sets fit the profile for contingent willingness?

Depending on the answers to these questions, anywhere from 0% to 80% could be the correct number for your organization.

Take a walk through a use case example of analyzing market analytics from our past Trends Report V3!
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Using market analytics and big data to solve big challenges

The following example offers a closer look at how market analytics can help you maximize big data. In this example, your organization needs to open a call center that will house 200 people — and it needs to be up and running in six months. Your organization has three markets from which to choose for the call center location. In many instances, an organization feels that the answer is, “…whichever one has the most open desk space or an unused floor of an existing building.” Now, if all things were equal, that may actually be the correct answer. But in fact, all things are most often not equal. For this example, let’s say that your three location choices are: San Francisco, Des Moines and Houston.

market analytics chart

These are obviously very different markets. What factors are most critical when planning to add a 200-person call center? And one that has to be up and running in six months?

While market analytics can offer insight into several key areas, we’ll examine the most critical here in this example. First, it’s important to examine the unemployment rate. All three markets are below the national average, which indicates better than normal health. Des Moines has the lowest rate and since that market has the smallest population, that low rate will certainly affect our decision. While San Francisco and Houston have higher unemployment rates than Des Moines, both have seen explosive job growth. Examining additional differences between markets, it is a relatively quick decision to eliminate San Francisco, as the seemingly minor difference in hourly pay rate there equates to increased base labor costs of well over $1 million per year.

All of the data used in this example is free to obtain. In fact, even more exists from the Bureau of Labor Statistics alone. By taking a relatively small amount of time to pull market data on a few key points, your organization could have saved millions of dollars while giving confidence to your business decisions. The economic realities of your markets will heavily affect your HR decisions.

Are you in a booming market? If so, start promoting your high achievers faster, giving them higher visibility assignments and pro-actively pursue their thoughts about your company. Or, are you in a stalling market? If the market has stalled, then cut low-performing workers and increase recruiting efforts to take advantage of labor slack. In the end, your company should not be spending money to fix issues until you fully understand the impact of key economic trends. Giving context to your internal data is essential.

About the Author: Ron L. Hetrick is the Director of Market Analytics at Allegis Group Services, where he works with organizations across different industries as a site selection and workforce planning consultant. He also provides economic analysis for the long-term strategic growth of the Allegis Group family of companies.

Download the full Trends Report V3 on Big Data to explore more key insights from industry experts.

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Published on Jan 26, 2018