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ICYMI: HR Developments Through the Decades

We recently read an interesting article, “HR’s Epic Journey,” in Human Resource Executive’s 30th anniversary issue, and we thought the insights offered by several CHROs were worth repeating. The trends they pointed to aligned with the ones we have spoken about before as well. You may read the full article here.

HR as a Strategic Business Partner

Executives want to believe in the value of their employees, but often struggle to understand how the HR function drives value through the organization’s people. HR’s value isn’t often seen or seen as an expense on a balance sheet. Strides are certainly being made by some but there is still work to be done. See what some CHROs are saying about it …

“The transformation of human resources from more or less an administrative, strictly employee-relations function to more of a strategic business partner” is the most dramatic change that Johnna Torsone, longtime executive vice president and chief human resources officer at Pitney Bowes, says she’s witnessed in her 27 years dealing with employment and HR issues at Pitney Bowes.

“Although it’s almost impossible to predict exactly how technology will alter the workplace over the next 30 years, the HR executives interviewed for this article mostly agree that the skill the CHROs of the future will need most is business acumen.”

“While it’s hard to imagine which gadgets and robots will revolutionize the workplace by 2047, the experts agree that the concept of the modern HR executive as a key business partner is here for good.”

Good news: SMD can get you a seat at the table! With the advanced analytical approach from SMD, you can clearly demonstrate your department’s value, and track improvement in business metrics eradicating the “What is the value?” question.

Solving Business Problems, Not Checking Boxes

Is HR impacting their organization’s bottom line? Most likely. Are HR leaders demonstrating this and illustrating their value to executives? Hardly. This can change. Shifting the focus from typical HR outcomes of interest such as employee engagement, or job satisfaction, to business outcomes such as sales, customer satisfaction, financial performance, and employee performance is the first step. HR leaders need to go beyond slicing and dicing HR data and start demonstrating direct connections to business metrics that matter most to executives.

“Mark Berry, vice president for human resources at CGB Enterprises, says the biggest boost for the HR profession has been the ability to use data to show the specific effects of various policies or programs on the bottom line.”

“Since arriving at CGB a couple of years ago, Berry has increased the frequency of employee surveys but targeted them to solve problems such as high turnover rate.” (Special note: Mr. Berry is an SMD client, utilizing the SMD consultants and patented technology to solve these business problems.)

“In a service-based economy, people and talent management has become more critical to business success than the other costs of goods.” – John Murabito, executive vice president for human resources and services at Cigna

“In the last 10 years, the focus has been on strategy … how do you take an organization that is not performing well and help it to be great.” – Darryl Robinson, executive vice president and chief human resource officer at Dignity Health

By utilizing our expertise in data integration, surveys, and the most advanced analytics, all delivered through our patented reporting and action planning platform, we’ve maintained a remarkable track record of improving business outcomes (e.g., voluntary turnover reduction) for our customers. So, we’re willing to put our money where our mouth is. We recently introduced results-based pricing. That means our price is based on the value/results we deliver. Essentially, this approach acts as a guarantee to deliver results. That is how confident we are that our approach truly yields results!

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Another SMD Prediction Validated: Bad Analytics Leads to Bad Business

At the end of last year, we developed a “Crystal Ball” prediction series about predictive analytics in HR, and how this approach will impact you, your organization and the HR field in the future. Our first prediction came true in February (see here). We’re happy to report yet another has been validated; the full prediction follows.

An article was written by Lisa Milam-Perez, J.D., in SHRM titled “The Promise and Peril of Big Data,” and it confirms our fear that bad analytics can lead to bad business.

“If HR professionals and hiring managers were to ignore these possibilities and take the data at face value, they would risk making unwise hiring decisions based on erroneous—and biased—assumptions,” states Milam-Perez. See our “Manager Mayhem” section below.

“Among the most pressing concerns inherent in relying on big data is that improperly used HR analytics can result in employment discrimination,” said Milam-Perez. See our “Affirmative Action Wins” paragraph below.

And lastly, Milam-Perez comments, “Because a poorly conceived algorithm can produce discriminatory outcomes, it’s important to make sure you validate all algorithms before acting on them. Consider whether data inputs fairly correspond to desired traits or whether the use of certain data sets skews the analysis.” See our “Buyer Beware” thoughts below. Keep in mind, we do encourage leveraging analytics in HR; you can add significant value to your organization when applied correctly. The key: you must be thoughtful in how you approach it.

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Bad Analytics Leads to Bad Business

There are lots of vendors jumping on the analytics bandwagon. Some are just re-branding things they were already doing and calling it analytics. Most of these tools/approaches use very basic methods and don’t connect results directly to business results. Others are pushing new approaches based on machine learning and algorithms. These may be truly innovative, but be very careful in this area. Remember that one of HR’s primary roles is risk mitigation and some of these “new” approaches introduce very real risks. Let’s explore some of the potential risks:

  • Manager Mayhem: HR will go overboard with the data they have and lose great employees by giving managers individual turnover risk metrics. We are already seeing this happen in the market. So, what do you think the likely outcome will be if you give managers an individual level turnover risk metric? A manager might fire or not promote someone because they are a “turnover risk.” Can you say lawsuit?
  • Affirmative Action Wins: Affirmative action lawsuits will be won in court based on a “predictive metric” with adverse impact or discrimination. This is where the machine learning component becomes risky. In this approach, correlations are sought for any and all data points. So, if a font style is a predictor of success in the resume screening process, is it possible the algorithms might have adverse impact? Sure it is. We can’t say for sure when this will happen, but it definitely will.
  • Buyer Beware: Many “predictive” analytic technology companies will fail and go out of business by rushing to get unproven things to market that aren’t actually predictive tools. These companies will fail because (1) clients will discover that bad analytics don’t actually drive success and/or (2) lawsuits. So, don’t just assume that something new and innovative will work. Be an educated buyer for your organization. Don’t make the mistake of buying something that will likely fail or result in significant risk for your organization.
Reports

Groundhog Day: A Comment on Gallup and Other “Annual Employee Engagement Reports”

It’s that time of year again—when we get Gallup’s Annual Report on Employee Engagement—and many others too. At this point, you can pretty much predict what the key themes are going to be:

  • Engagement scores aren’t very good
  • Engagement is pretty much flat again this year
  • Engagement hasn’t moved in the past 16 years
  • Gallup tells us what the key drivers of engagement are
  • Companies with high engagement scores happen to have decent business outcomes

Let’s start with the last bullet first. It is getting harder to believe that engagement is a key metric for organizations to focus on and strategize about. Studies that say that “companies with high engagement scores have higher business outcomes” amounts to fake analytics. There, we said it. Did you know that companies with taller buildings also make more money? That’s another example of fake analytics. Real analytics (see SMD’s study that uses cause-effect, longitudinal data) shows that engagement is a key driver of business outcomes less than 30% of the time.

It has to be getting frustrating for HR leaders (and all leaders for that matter) to hear about all the time, money and effort that has been put into driving engagement, knowing that the only thing that has happened is stagnation for the past 16 years. When will a CEO ask HR, “We’ve spent lots of money on engagement surveys and engagement consulting—when is this going to pay off?”

A few rapid-fire questions:

  • If Gallup knows what the key drivers of engagement are—then why don’t the scores ever move up?
  • What exactly is a “good” engagement score? What number do we need to hit when money starts falling out of the sky?
  • Have we hit critical mass on engagement? Maybe this is as high as the scores can get?
  • Isn’t it time we started looking at which employee attitudes drive actual business outcomes instead of chasing this suboptimal metric?

What are your thoughts on the topic?

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The Basics of Big Data, HR Analytics & Predictive Analytics

There are lots of words and concepts floating around the HR world these days that are new or different from the traditional approach – ones like “big data,” “HR analytics,” and “predictive analytics.” We think it is worth defining and discussing each of these topics.

Big Data

Definition: A collection of data sets so large and complex that it becomes difficult to process using traditional data-processing applications.

HR Application: HR has tons of data in various systems (e.g., HRIS, ATS, LMS, surveys, 360 assessments, performance management ratings, compensation data) but we don’t always harvest the insights and intelligence from the data. Even though our data sets don’t always have the magnitude of data as traditional big data applications, the big data principles still apply!

Example: A good example of big data outside of HR is in consumer banking. Consider the number of individual customer transactions a bank processes every minute or hour. The big data concept started outside of HR, and most IT professionals would NOT consider the typical HR analytics project and data sets as “true” big data.  (Although, at SMD, we have analyzed many data sets with millions of data points.)

Connection to Analytics: Underlying all the big data talk is the concept of analytics. Analytics provide the methods to harvest intelligence from the data. So, the HR analytics concept is much more important for HR professionals.

HR Analytics

Definition: The analysis and application of a company’s people data to uncover people insights/intelligence that informs HR strategies, process changes, and investments – all with the goal of improving organizational performance (i.e., driving actual business outcomes).

Deep Dive: HR analytics come in many forms – from basic descriptive analysis and tracking trends to the more sophisticated predictive analyses like multiple regression and structural equations modeling.  This does get rather technical but understanding the basics will help all HR professionals be better consumers of analytics. The model below outlines four levels of HR analytics. Organizations should be utilizing and operating at all four levels. However, it should be noted that the most impactful analytics fall into the highest level – predictive analytics.

4 levels of HR Analytics

HR Application: HR has become much more sophisticated regarding metrics and data collection. However, most of our metrics are focused on HR efficiency (e.g., time to fill) and comparisons to benchmarks (e.g., engagement scores). The next evolution for HR is to directly connect people data to business performance via HR analytics.

Example: For example, we can identify the root causes of turnover in the first six months. The analytics can be used to understand potential impact and actual ROI over time. Ultimately, the analytics provide the business case, path to reduction, and the “story” of impact and value HR brings to the organization. We used turnover as an easy example, but we can do this with any business metric (e.g., sales, revenue, safety incidents, customer satisfaction). Bottom line – we must harvest the power of analytics to demonstrate the value of HR by driving organizational performance.

Predictive Analytics

Definition: The application of analytics to existing data sets to predict future outcomes and trends.

Deeper Dive: Predictive analytics don’t need to be applied across the board in HR. This type of analytics requires a higher level of resource and investment. However, predictive analytics are the best approach for solving complex business problems. The analytics method does matter in predictive analytics. Neither data visualization nor correlational analysis are predictive.

Buyer Beware: If a vendor tells you they use predictive analytics based on correlations, they are wrong!  Correlation means two variables are related but makes no inference regarding the order things occur in (a basic requirement for predictions) nor the causal implications of the relationship. Predictive analytics must be based on some form of regression analysis or structural equations modeling (SEM).  In fact, one can infer cause and effect from an appropriately-run SEM. Okay; enough of the nerdy stuff ….

HR Application: In HR, we are generally looking to use predictive analytics to understand the people causes or drivers of meaningful organizational outcomes.

Example: For example, what employee experiences drive voluntary turnover or what competencies drive quota attainment for sales professionals? Based on the analytics, we can uncover the people levers to reduce turnover or drive quota attainment.

Hopefully, these basic definitions and concepts will help you become a better consumer of HR analytics, which is important because a lot of VC money is being funneled into the HR analytics space and some of the new products are being oversold or even worse, are garbage. Ask the hard questions and remember it is good to be skeptical in a time of great change. We have some thoughts on what will happen in the future. Join us for our next webinar, “The Future of HR Analytics & How to Embrace It,” on April 5th at 1:00pm EST to learn more. To register, click here.

need for analytics

SMD’s Prediction Comes True: More Colleges & Universities Offering Advanced Degrees in HR Analytics

At the end of last year, we developed a “Crystal Ball” prediction series, including the blog detailed below. Turns out, it’s coming true per a recent Human Resource Executive’s article. Read here.

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Demand for Analytics Skills Will Continue to Grow

We are already seeing an increasing demand for HR analytics skills. Actually, there is already a shortage of these skills in the job market. Our advice: make sure you are growing your skillset and being proactive about bringing these skills to your organization. Our crystal ball sees the following happening:

Statistics Surge in Schools: We predict that HR educational programs will emphasize statistics and data analysis. Sometimes academia can be slow to adapt, but many programs are already starting to expand existing curriculum to include more statistical methods courses.

Skill Set Search: Organizations hiring for HR roles will increasingly seek out candidates with these skills. Again, we are already starting to see this in the job market. HR leaders don’t have to be statisticians, but they must understand the application of analytics and be good consumers.

Are YOU I/O?: Demand for I/O psychologists in HR functions will sky-rocket (and in fact is already occurring). I/O psychology is basically the application of psychology to the workplace founded on the scientific method and requires the application of statistical methods. I/O psychologists are experts in behavior, knowledge transfer, attitudes, aptitude, and statistical methods. Essentially, they possess the perfect combination of skills for HR analytics. Predicting human behavior and performance is very difficult and complicated. I/O psychologists know how to do this – hence the increased demand for their skillsets.

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The Employee Survey: A Strategic Tool vs. Engagement Barometer

Are you simply using your employee survey to measure engagement? How do you demonstrate the ROI from that approach? What if you instead use your survey to connect the employee perspective to business outcome data, such as financial performance, turnover rates, productivity numbers, and any other metric that the C-suite is using to evaluate company performance? If your current employee survey is not predicting critical outcomes to your company, your organization is potentially wasting valuable time and money on initiatives that will not impact business outcomes.

We’ve created a how-to guide to help you do just that. Yes. It’s possible to strategically use the employee survey to show ROI and improve business outcomes at your organization!

HOW TO: Think Strategically About Survey Content & Administration

Work Backwards: Go into a survey initiative knowing the business questions you want to answer and what the ultimate outcomes or goals of the survey should be. Think through the following:

  • What questions are being asked across the organization?
  • What are leaders hoping to understand?
  • What organizational issues are concerning to leadership?

Easy Peasy Access: Surveys should be accessible online, through a computer, tablet or mobile device at home or in the office, to ensure broader reach and greater participation rates. By doing so, you’ll achieve high participation and ultimately a fair representation of all employees. And, the turnaround time for analytics is greatly reduced, meaning your organization can move from survey to action in a much shorter period.

HOW TO: Connect Employee Survey Data to Business Metrics
(to prioritize organizational follow-up & solve common issues)

Show Me the Money: Advanced analytics that link employee scores to real business outcomes allow for the prioritization of time and resources in response to the employee survey results. This can also allow for a dollar amount to be placed on employee attitudes by demonstrating the connection between the employee experience and the organization’s performance.

CASE STUDY #1: Link to Bottom-Line Metrics

SMD helped a client link its employee survey to the following business outcomes: employee commitment (precursor to turnover), actual turnover rates, ROI metrics, and customer satisfaction scores. This linkage allowed the organization to prioritize around key topic areas, provide managers with specific scores on these key drivers, and direct follow-up and action planning. This approach is outlined in a graphical plot called a HeatMap® (below), which allows leaders to quickly see the attitudes that are key drivers of results and prioritize improvement efforts in these areas.

Happy businesswoman with colleagues in the background


CASE STUDY #2: Understanding Turnover

This is a pain point for everyone, right? SMD helped a client understand key employee experiences that were leading to voluntary turnover among their staff. By identifying 1) what employee perceptions led to turnover and 2) where in the organization employees scored low on these key drivers, this organization created targeted follow-up directed at the most at-risk work units. In a year’s time, they reduced turnover by 24-28 percent across the organization, saving an estimated $8 Million. Learn more about this case study in our recorded webinar; click here.

And, if you’re still not sure if your employee survey is up to snuff, take this quiz and see where you land.

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Predictive Analytics in HR: Our Crystal Ball Says …

Part V

This is what you’ve all been waiting for: our last prediction! We saved this one for last because it’s very personal to you and your development in the future.

#5 Demand for Analytics Skills will Continue to Grow

We are already seeing an increasing demand for HR analytics skills. Actually, there is already a shortage of these skills in the job market. Our advice: make sure you are growing your skillset and being proactive about bringing these skills to your organization. Our crystal ball sees the following happening:

  • Statistics Surge in Schools: We predict that HR educational programs will emphasize statistics and data analysis. Sometimes academia can be slow to adapt, but many programs are already starting to expand existing curriculum to include more statistical methods courses.
  • Skill Set Search: Organizations hiring for HR roles will increasingly seek out candidates with these skills. Again, we are already starting to see this in the job market. HR leaders don’t have to be statisticians, but they must understand the application of analytics and be good consumers.
  • Are YOU I/O?: Demand for I/O psychologists in HR functions will sky-rocket (and in fact is already occurring). I/O psychology is basically the application of psychology to the workplace founded on the scientific method and requires the application of statistical methods. I/O psychologists are experts in behavior, knowledge transfer, attitudes, aptitude, and statistical methods. Essentially, they possess the perfect combination of skills for HR analytics. Predicting human behavior and performance is very difficult and complicated. I/O psychologists know how to do this – hence the increased demand for their skillsets.

Thank you for indulging us in our game of predictions. If you missed any of the prior predictions, you may read all here in our white paper. Do you agree with us? Let us know by commenting below.

Print

Predictive Analytics in HR: Our Crystal Ball Says …

Part IV

We are continuing to take a look into our crystal blog with our five-part predictions series. This fourth prediction is probably the most important for you to consider as you develop analytical capabilities in your organization. By the way, if you missed our previous predictions, you may read them here.

#4 Bad Analytics Leads to Bad Business

There are lots of vendors jumping on the analytics bandwagon. Some are just re-branding things they were already doing and calling it analytics. Most of these tools/approaches use very basic methods and don’t connect results directly to business results. Others are pushing new approaches based on machine learning and algorithms. These may be truly innovative, but be very careful in this area. Remember that one of HR’s primary roles is risk mitigation and some of these “new” approaches introduce very real risks. Let’s explore some of the potential risks:

  • Manager Mayhem: HR will go overboard with the data they have and lose great employees by giving managers individual turnover risk metrics. We are already seeing this happen in the market. So what do you think the likely outcome will be if you give managers an individual level turnover risk metric? A manager might fire or not promote someone because they are a “turnover risk.” Can you say lawsuit?
  • Affirmative Action Wins: Affirmative action lawsuits will be won in court based on a “predictive metric” with adverse impact or discrimination. This is where the machine learning component becomes risky. In this approach, correlations are sought for any and all data points. So, if a font style is a predictor of success in the resume screening process, is it possible the algorithms might have adverse impact? Sure it is. We can’t say for sure when this will happen, but it definitely will.
  • Buyer Beware: Many “predictive” analytic technology companies will fail and go out of business by rushing to get unproven things to market that aren’t actually predictive tools. These companies will fail because (1) clients will discover that bad analytics don’t actually drive success and/or (2) lawsuits. So, don’t just assume that something new and innovative will work. Be an educated buyer for your organization. Don’t make the mistake of buying something that will likely fail or result in significant risk for your organization.

Stay tuned next week for our last prediction.

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Predictive Analytics in HR: Our Crystal Ball Says …

Part III

We foresee our next prediction because it will be a direct result of our last prediction, which was “Adoption and Change Will be Slow.” HR organizations that do analytics the right way will have a significant competitive advantage because few will do so quickly.

#3 A Competitive Advantage with ROI … for Some

Analytics help organizations solve real business problems — when done correctly.  Essentially, the application of analytics will help HR begin making decisions and investments based on facts and data (not guesses).  Furthermore, analytics will help them uncover and prioritize the real drivers of results (not squishy concepts like employee engagement). Data-backed decisions, in general, are needed in our field, so the organizations that successfully apply analytics will gain a significant advantage.

Below is a list of reasons that will drive, and almost force, HR to achieve this competitive advantage:

  • Quizzical CEOs: One day soon, CEOs will start asking CHROs “What exactly is the ROI of all these surveys and assessments?” HR leaders will have to start answering the “so what” of the people investments and initiatives instead of relying on the assumption that it matters and provides value. The only way to identify the ROI is through the application of analytics.
  • KPI Confidence: HR will stay in its comfort zone for awhile focusing on KPIs like turnover. (That’s because turnover is a key metric with real costs that HR feels comfortable owning.)  Eventually, most organizations will start to show direct business impact on other KPIs, such as sales, revenue, productivity, operations, and customer satisfaction.

The good news is that you still have time to gain this advantage – recall the “Learning Curve Sprint” from our last blog? Stay tuned next week for prediction #4.

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Predictive Analytics in HR: Our Crystal Ball Says …

Part II

We are continuing on with our predictions for predictive analytics in HR blog series. Now on to #2.

#2 Adoption and Change will be Slow

Adoption of predictive analytics in the HR world will be relatively slow. This may not seem like a bold prediction, but it’s certainly worth discussing.  Let’s review what we know about HR. Many HR professionals went into HR because they love working with and helping people. Not as many chose HR because they love numbers and statistics, which also means that the current analytical skill level of HR is limited.  So, many HR professionals understand that analytics can greatly help HR contribute more to the bottom line, but may not have the proper skills to effectively apply analytics. Below are our thoughts on what we think will happen due to HR professionals’ lack of deep subject matter expertise (albeit eagerness to learn!):

  • Shiny Object Syndrome: HR will get caught up in the new shiny object of data visualization and miss out on something that is greatly needed – actual, rigorous HR analytics. They may focus on things that look cool and seam sophisticated, but lack real rigor and have minimal utility in driving improved outcomes.
  • Trends=Credibility Killers: Companies will apply bad analytics that will result in bad decisions—investing based on data trends (what happened in the past), correlations, and vendor fads. This will kill HR’s credibility, as well as the credibility of companies like SMD. Unfortunately for us, some of these sub-par tools that look pretty but provide questionable analytics could actually impact legitimate analytical companies like ours.
  • Rage against the Machines: The race to apply automatic analytics (i.e., machine learning) will result in more bad predictions than good ones. Predicting human behavior is complex and difficult and even when done well it can only account for a fraction of variance in outcomes. So when a company starts telling you who to hire based on the font in the candidate’s resume, you should be VERY skeptical. Unfortunately, some of these companies will find customers to buy these very flawed tools. These looming failures will make organizations slow to adopt all analytics solutions – even the ones that will add significant value.
  • Learning Curve Sprint: On a positive note, HR will quickly increase its understanding of analytics and its ability to evaluate potential applications. Basically, HR will be forced up the learning curve quickly because they will be bombarded with so many different tools and applications that they will have to learn.

Stay tuned next week for prediction #3.