predictions analytics

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.

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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.

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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.

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Predictive Analytics in HR: Our Crystal Ball Says …

Part I

If you’d heard it once, you’ve heard it a million times. Predictive analytics in HR. We can all agree that predictive analytics is not going away (a good thing because leveraging the approach properly will behoove you and your organization) so we began pondering how this approach will impact you, your organization and the HR field in the future. We have been in this space for close to 10 years living and breathing predictive analytics so we’re basing our predictions on our expertise and practical application. We hope you join us for this five-part blog series that unveils visions from the SMD crystal ball. We are interested to hear if you agree.

#1 The Data Balancing Act

We believe that organizations will increasingly understand the value of “Big Data” and will work to connect data sources across their organization in order to more effectively conduct analytics and strategically answer important business questions. Applause for analytics done correctly!

However, we envision HR still struggling with the balancing act. What we mean by that is, there is a balance between just having more data and having the right data. Think about all the data HR already owns: HRIS, employee surveys, 360 feedback, candidate data from an ATS, performance management ratings, etc. The reality is that lots of data already exists and many practitioners will continue to have an insatiable–and unnecessary in our humble opinion–need for more data.

Our advice to you (if this prediction comes true in your organization): remember that the need is to harvest the power of data to identify drivers of outcomes. HR functions are better off leveraging the intelligence from the data they have instead of racing to gather more data.

Stay tuned for next week’s prediction #2.

Business Vision concept Businessman looking to the view with telescope. Looking to the future

Don’t be Fooled: Predictive or Not?

How do you know if you are truly conducting predictive analytics? If you’re not sure, you’re not alone. Few organizations fully harness the potential of predictive analytics due to a number of misconceptions about predictive analysis methods. It is not uncommon for an organization to invest in a predictive analytics program that is actually not predictive.

Walk yourself through the questions below. If you answer “yes” to any of them, we have bad news for you: your approach isn’t actually predictive in nature. The good news: we can steer you in the right direction. If you vehemently say “Yes; I’m doing it correctly,” we’d still like to challenge you to this test because you may be surprised.

Are you using trend lines to project the future?

Anyone that uses score-based trends to claim they’re providing predictive analytics is wrong (sorry if this is you!). Examining a trend line calculated from tracking changes in scores over time tells you nothing about why any ups and downs have occurred or where that line could go in the future. Unfortunately, HR has gravitated toward data visualization tools. Although these tools create pretty trending pictures, they are not predictive and do little to actually move the needle on the HR data being tracked (or “visualized”)—ignoring the other fact that these HR data haven’t been connected or shown impact on an actual business outcome. In short, trend lines simply track progress; these are not predictive analyses.

Are you simply using correlations?

Although a correlation is a statistical measure of a relationship, it is not predictive. Just because a relationship exists between two variables does not mean that one causes the other. It’s the classic example of ice cream sales and shark attacks being correlated. No one would argue that shark attacks cause ice cream sales to go up nor that ice cream sales cause more shark attacks—they both just happen to increase during the summertime. Similarly, having high engagement and high business outcomes doesn’t mean that engagement causes the other. Because these methodologies do not tell if a true relationship actually exists, they are not considered strong enough for prediction. If investments are going to be made based on analysis, then correlation is certainly not a strong enough basis for driving and securing a significant financial investment.  Correlations should only be used as a starting point to determine where to continue additional analyses. They are not predictive.

Do you believe that regression is the way to go?

You may be amongst the many who believe regression equals prediction. While regression does identify variables that predict (i.e., are antecedents to an outcome), they are not necessarily causal.  For example, the winner of the Super Bowl is a strong predictor of stock market performance (an NFC champion predicts a bull market) – however this is obviously not causal in nature. As a result, there are limitations to the usefulness and interpretation of simple regression analyses. We agree that multiple regression is closer to modeling real-world relationships because multiple factors can be tested as predictors of an outcome. This approach allows for the examination of each factor’s unique effects on the outcome. Although a step in the right direction, this method is still not the strongest method to determine cause and effect relationships because measurement error cannot be modeled; only one dependent variable can be included, and, most importantly, causation cannot be determined with much, if any, certainty. If you’re here, you’re getting close, but you still have not reached true predictive analytics.

Predictive Analytics Done Right

If you said “yes” to any of the questions above, don’t be discouraged just don’t stay your course. The method you need to employ for making predictions is an advanced statistical modeling method called Structural Equations Modeling (SEM). SEM has four large advantages over the other analytic methods:

  • Multiple inputs or “causes” can be tested along with multiple outcomes concurrently.
  • It provides the ability to correct for measurement error.
  • An accurate assessment of ROI can be calculated.
  • Causation can be determined.

You can get a quick glimpse of SEM’s advantages versus other methods below.

Comparison of Analytic Methods for HR Prediction

Simply put, SEM is the best approach to predictive analytics for HR. The caveat to utilizing SEM is that it requires specialized statistical software and a highly trained statistician to be correctly implemented. However, this should not be a deterrent for any HR practitioner hoping to leverage this type of analytics. Universities often have professors or graduate students with the skills to conduct this type of analysis, or the analysis can be outsourced to a consulting firm with expertise in predictive analytics.

Whatever the organizational goal (e.g., reduce turnover, increase hiring success, increase ROI of your HR initiatives), true predictive analytics, when done correctly, will not only allow for the identification of key drivers, but will also allow for their prioritization. The approach will make planning and resource allocation straightforward and give your company a massive competitive advantage.

To learn more, read our white paper.

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Big Data Blunders: Not All Analytics are Created Equal

You’re hard pressed to go through a day and not see or hear a mention of big data. Let’s start with the definition. According to an Inc. magazine article, “Simply put, big data refers to a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.” The article also states that “Although estimates vary widely, research conducted by CSC estimates a 4,300 percent increase in annual data generation by 2020.” There certainly isn’t a lack of data, or a question of its utility. The question is – are companies leveraging it properly?

A top big data trend for 2015, according to an article in VentureBeat, is “faking it with big data will no longer cut it.” We couldn’t agree more. Companies have confused the marketplace by purporting to show results and demonstrate ROI with their tools and technologies. Problem is, not all analytics are created equal. Not all technologies can measure the specific ROI of your HR investments—or make the results actionable for front-line leaders.  Our goal is to help you move the business needle so we’ve pulled together some thoughts for you to consider before enlisting an assessment/analytics vendor or conducting analysis in-house.

Real Analytics for HR:

  • Analytics CANNOT be limited to slicing-and-dicing HR data (data visualization tools add little value)
  • Analytics must be true cause-effect and predictive of real business outcomes (not Engagement!)
  • Analytics must be reported and actionable to all front-line leaders (not just for pretty corporate PowerPoint presentations)
  • Actual business impact must be shown

Guiding principles for business-focused metrics:

  • There are no magic metrics that work for everyone
  • Every element on the scorecard must be directly linked to business outcomes
  • HR Efficiency Metrics are fine for Internal HR tracking but not for senior business leaders
  • HR Metrics must be predictive
  • For every metric you should be able to answer yes to these questions:
    • Can I articulate why this really matters to the business?
    • Do I know what a good number should be?
    • Can I articulate the business value of moving this number up or down?
    • Why would senior and front-line leaders
      care about this metric?

We’ll be speaking and exhibiting at the Predictive Analytics World Workforce Conference March 31-April 1. If you’d like to meet with us to discuss more on this topic while at the conference, or to schedule a demo of SMD link, please email us at info@smdhr.com.