Fact or Hype: Predictive Analytics is Transforming Human Capital


After years of hype about employee engagement with little progress to show for it, we understand why some might regard the noise about human capital analytics to be hype. In addition to that skepticism, lack of progress in people analytics is reinforcing the perception. In spite of years of urging by industry analysts and thought leaders, HR has made little progress, but promising signs are emerging.

Deloitte’s 2016 report is more optimistic, citing a doubling in the percentage of companies capable of developing predictive models, from 4 percent to 8 percent. Their new conclusion is that HR is “turning the corner.” CEOs are reporting better results from HR.


When people ask us about the future of predictive human capital analytics, we say it is the next logical progression in the way we do business. HR leaders have been making predictions based on past and current data for as long as we can remember, but the traditional methods of predictions have been benchmarking, “best practices,” gut instinct, and hope. That is no longer good enough. The competition for people is demanding better tools.

HR is on the cusp of massive change. Adoption of people analytics is gaining momentum, and it won’t be long before human capital analytics becomes a required fundamental competency.

What is holding HR back?

Four primary factors have been holding HR practitioners back.

Siloed thinking -- the flawed premise that HR does “people stuff” and everyone else does business. It manifests itself when HR leaders can speak of outcomes only in trendy or “feeling” terms. We have talked with many HR practitioners who describe themselves as “a people person, not a numbers person.” That concept is beginning to change.

Lack of capability. People analytics is a relatively young concept; expertise has been scarce, but the number of analysts and consultants is growing to meet the demand.

Technology. Robust analytical tools have historically been expensive and hard to use, but now human capital software vendors are packaging user-friendly tools with their platforms. However, the tools they provide are not unique to industries and are not designed for predictive modeling.

Misdirected focus. HR has been focused on proving its value with internal efficiency and effectiveness measures rather than contributing value by impacting the business. That will change as more and more CEOs demand to know how their investment in people pays off.

People Analytics: A Business Necessity

Modern CEOs realize that people are their only sustainable competitive advantage. The demand for people analytics will continue to grow, and HR will need to hustle to catch up.

By themselves, machines, materials, processes, and information do nothing. People create value when they interact with those things and with other people. If people are 70% of your cost and all of your value, why would it not be a fundamental business practice to apply the power of analytics to maximize their net contribution?

Why HR Needs Predictive Analytics Now


In the early years of our new century, we were working in talent management software companies, implementing integrated platforms for dozens of medium to large business. For the more forward-thinking companies, many of them global household names, the ultimate holy grail was a succession planning application.

Supported by an applicant tracking system, performance and goal management, employee development, and integrated compensation planning, the succession applications comprised a database and talent dashboard that brought together all of the history, performance, and learning information about employees into an interactive organization chart.

All based on bad data.

Follow the Data Trail

To understand why we call it bad data, let’s follow the journey of the data from beginning to end. Start with the recruiting process, where hiring managers made their decisions based on their best judgment of a candidate’s ability to do the job. The evidence a manager used was a finely crafted resume, a manual screening process, and a series of interviews.

Then, managers set performance objectives based on their perception of how the new employee’s job supported organizational goals. At the end of the year, the manager exercised “best judgment” to rate employees on their performance, and awarded merit raises and bonuses based on that judgment. Thousands of companies developed rating models intended to make evaluations more objective, but managers’ judgment decided the difference between evaluative adverbs like often, frequently, always, and sometimes.

The managers created development plans for employees based on their assessment of what skills and competencies the employee needed to develop, and then evaluated, again based on their best judgment, how well the employee had progressed.

Then, as the final stroke, the manager used their experience to estimate the employee’s potential for growth and advancement so the dashboard could display the employee’s position on the famous 9-box. Only then, after the intervening gut decisions, were the top contenders evaluated using professional, objective assessments. To be fair, we must say that the best companies used assessments early in the process, but most did not.

The problem is in our humanity.

How We Make Decisions

Humans do not make decisions with their rational minds. We make hundreds of decisions every day, originating in the emotional centers of our brains, then we use our rational minds to justify them. Even when we work through the process of gathering information, quantifying costs and benefits, using every possible data source we can find, our biases guide our decisions.[1]

Let’s look at the evidence. In Harvard Business Review, Marcus Buckingham cited three studies where the effect of rater bias was well over half of the rating. In the Applied Psychology study, 51% of the variance in ratings was determined by bosses’ idiosyncratic biases, almost twice the effect of actual performance.


The result is a growing movement to change the way we assess performance and a small but growing dependence on harnessing the enormous amounts of data we generate to make better decisions.

Implications for HR Analytics

The digital transformation of the past fifteen years has wrought big changes. Now we have validated assessments during the recruiting process, performance analytics to support evaluation decisions, and, in the most progressive companies, profiles based on predictive analytics.

Well designed pre-employment assessments work.We have evidence that algorithms make better hiring decisions than people. We have seen the positive impact on personal development a well-designed assessment can bring to an individual, and that identifying high potential employees and developing them has a positive impact on organizational performance.

That doesn’t mean you need to bet the business on an analytics function. If you can afford to invest in a team of data scientists, a state-of-the-art analytics engine, and a massive data warehouse, do it. For most, there are less risky alternatives.

  • Many consulting firms specialize in people analytics. You can capitalize on the economy of scale offered by a company that serves many customers.
  • Skilled consulting analysts and statisticians can help you realize the full value of the technology you have.
  • Assessment vendors are reaping the same rewards of analytics that all of us are, and passing that value on to their customers.

Our recommendation is to start from where you are with the data and tools you have and work on the problem of the leadership pipeline. We will write more about how to get started over the next few weeks.

If you have questions about getting started or implementing analytics in your organization, call us at +1 855-978-6816 or use the comments section below. We will be glad to share our knowledge.


1.  Haidt, Jonathan. The Righteous Mind: Why Good People Are Divided by Politics and Religion. New York: Pantheon Books, 2012.

2.  Scullen, Steven E., Michael K. Mount, and Maynard Goff. "Understanding the Latent Structure of Job Performance Ratings." Journal of Applied Psychology 85, no. 6 (2000): 965. doi:10.1037/0021-9010.85.6.956.

Predictive Talent Analytics for the Bottom Line


We often read that companies have poured billions of dollars into employee engagement programs over the past few years with little to show for the effort. Every day we get an offer for another webinar, white paper, research study, or blog articles about employee engagement.

With human capital analytics now becoming mainstream, the clamor has increased. An entire industry sprang up around employee engagement, to the point that it seems impossible for a CHRO to know where to start. We have technology vendors, assessment providers, mood monitoring systems, work management systems, and a wealth of local, national, and global consultants. And let us not forget the new disciplines like Engagement Diagnostics Specialist.

We are not saying well-implemented engagement programs do not work, or that those of us in the business don’t provide valuable service. Dozens of case studies prove otherwise. Good programs succeed, but it takes strategy, discipline, and a road map.

Navigating the Market

Josh Bersin and his team have created an Employee Engagement Vendor Market Navigator to help CHROs navigate the provider maze. The model takes work to fathom, but it helps us understand the contributions each of the specialties and platforms make. It maps the resources and expertise needed to enhance productivity, monitor organizational health, and effect change.


All the resources Bersin recommends would be helpful, and might be doable if these factors are in your favor:

  • the organization has the resources,
  • your CEO is not a skeptic,
  • the CFO understands the value of the investment, and
  • you have a strategy and a well-rounded, experienced team.

For the rest of us, we recommend a more measured approach. We agree with Bersin that the place to start is with a strategy, but a CHRO can’t go down the analytics road alone.

First, stop trying to chase employee engagement. To many people, employee engagement is HR jargon. Engagement is not a business outcome, and a direct impact on the income statement is hard to determine. You will not get support for what business leaders perceive to be a nebulous idea. Follow the money trail to build credibility.

Form the Right Partnerships

Assuming you need to overcome internal resistance, the place to start is with the right alliances. Your three new best friends should be Marketing, Finance, and a business leader with a problem.

If anyone knows analytics, Marketing does. They have been studying consumer behavior for years, and can predict who will buy what and when with uncanny accuracy. The predictive analytics they use will be much like what you will use to predict employee behavior.

The CFO has been using analytics to predict the value of investments in the company for decades. This initiative is an opportunity to add value to the business on the CFO’s terms.

Most business leaders have financial objectives they must meet, usually expressed as key performance indicators, (KPIs). For example, if you can predict with accuracy which candidates will be better sales people, you can improve sales, reduce attrition, or both. A large medical device company cut sales attrition by 1 percent and saved $30 million in turnover costs.

Solve a Business Problem

If you can help a business leader solve a problem that results in better numbers on the balance sheet, you are well on our way to changing the way HR does business. In the best case, that business leader will command the resources necessary to get the work done.

Every situation is different, but we can recommend a general framework for your analytics project.

  1. Engage a data analyst. If you don’t have expertise in the organization, hire an experienced consulting company. (It may be time to upskill HR.)
  2. Isolate a business problem.
  3. Agree on the metrics that measure the outcome.
  4. Determine the analytical method you will use to determine correlations, causation, and predictions.
  5. Assemble the data. Much of the data about employee performance is in systems other than HR.
  6. Analyze the data. Be sure to include data quality assessments and validations
  7. Discuss the findings with your executive team.
  8. Implement the decisions of your executive team.
  9. Track, assess, and validate the results.
  10. Communicate the outcome, with emphasis on financial data.

A small success that affects financial results will lay the groundwork to help you improve and enlarge on your efforts. In time, predictive analytics will be the foundation for more informed decision making and a more productive workforce.

Impact of Predictive modelling on Human Capital


Human resources are vital constituents for every organization. In today’s age of competition and growth, it’s become crucial to view people as assets rather than “costs” to the organization. In a fast paced world , skill requirements are constantly changing with rapid advancement of technology and regulations. Human capital dynamics demand that we apply analytics to reconfigure our workforce scenarios and predict our next best move or else we lose competitive advantage and market share. Human Resources (HR) analytics refers to the integration of relevant HR statistics from different sources, analyzing this captured data, and ultimately establishing the predictive models based on business requirements and funnel the available data into the respective model and utilize it towards organization performance.

Significant Challenges faced by HR  

Every business function has to make some or other changes such as  reduce costs, increase revenue etc to stay profitable and grow. Some of the biggest challenges faced by HR teams and other key executives in maintaining growth rate and managing human capital across various segments include :

  1. Handling the Talent acquisition and resource management
  2. Training and appraisal
  3. Forecasting and Budgeting
  4. Fraud Risk management
  5. Key Employee Analysis

Although financial capital (cash) and economic capital are the lifeblood of a business, it is human capital that apply logical skills  and leverage intangible assets to drive  business performance. Companies are exploring new opportunities for utilizing the ever-increasing  volumes of data from various segments and affixing it with strategical approach which ensures that analytics and its outcomes are aligned with business objectives. Predictive analytics for HR is based on establishing an analytics-driven statistical relationship between the objectives of  HR function and fruitful resources available  within an organization.

Key segments where Predictive Analytics Can enhance Value  for HR domain include :

  1. Employee Segmentation and Profiling : Predictive analytics can be leveraged for effective talent management by accurately segmenting  employees which can help in understanding the employee base in a better way. A statistical relationship between profile variable (such as education and experience) and employee value enables HR to identify the most deserving profiles. This helps to increase quality and reduce recruitment cost extensively creating sustainable value for the organization.
  2. Training and Appraisal : Predictive Learning algorithms can help predict the impact of organizational requirements  and tailor the programs accordingly for improved outcomes. Predictive analytics helps identify employees with particular training needs as well as detect emerging trends in areas such as program diversity, enrollment, onboarding,employee management  etc.
  3. Forecasting of Human resources and Recruitment Needs : Predictive analytics helps to better forecast the organizational requirements by building targeted recruitment plans,optimizing HR partner initiatives etc. This enables organizations to maximize resource utilization and amplify appropriate growth and profit margins.
  4. Key Employee Analysis : HR professionals should capitalize on unstructured and structured data from multiple sources to create or redesign their initiatives targeted for Key employees at different levels. Key employee analysis is more effective than general employee surveys in getting fruitful and productive feedback.  Such information can promote understanding of how various  HR policies, initiatives, organizational changes are being perceived by the employees.
  5. Employee Fraud Risk Management : Predictive analytics helps improve the fraud risk management by enabling an organization to identify employees who are at increased risk of non-compliance with the organization's security policy. Organizations can formulate a fraud risk score by analyzing employee activity reports using statistical modeling techniques. This can help protect the company's reputation and possibly prevent financial impact.
  6. Intangibles : HR function reports costs of various activities such as  training, recruitment, appraisals, perks, incentives etc. Moving in line with strategic objectives, HR teams can increase its organizational presence by focusing on intangibles assets (such as  leadership, culture, commitment, loyalty) of organization. Knowledge based on Predictive models can guide towards changes in leadership capability, engagement, culture etc which can be used for better planning and predictions.

The Leveraging effect

The key to realize maximum benefits from the Human capital data lies in aligning tying the different data sources to strategic business objectives. Leveraging data from different sources along with application of  predictive models helps in projecting the right picture clarifying holistic analysis of the organization. Blending of statistical information from government and other sources along with organizational data provides a clear ground for effective planning and meeting both short term and long term goals of the organization.

To play a more strategic role in the organization, HR teams need to move ahead from operational analytics to predictive competence. Instead of generating reports for operational effectivity, it needs to embrace advanced predictive algorithms that support strategic organizational vision and objectives.

Predictive analytics helps organizations manage monetary formalities  while developing a high performing workforce. Predictive analytics might be uncharted realm for HR domain, therefore to fully capitalize on analytics expertise, HR personnel need to collaborate with other business domains  to understand how can they leverage existing data to create sustainability for brand and value. By doing so, HR departments can  boost superlative employee experiences which can aid to achieve projected long-term goals with desired optimum efficiency.

Pixentia is an enthusiastic team of individuals, fervent to make lives simpler through effective use of technology. Our mission is to implement solutions that drive business results. Know more insights from our thoughts and experience.

Step Up to Predictive Human Capital Analytics


If you read our recent article on HR big data, you know you can analyze your data without advanced analytical tools to discover connections, relationships, correlations, and causations. You may have already started on a test project to affect a business metric by applying your conclusions.

We need analytics to give us insight into the probability of future events and trends. In Predictive Analytics for Human Resources, Jac Fitz-Enz and John Mattox describe three levels of human capital analytics beyond simple reporting.

  1. Descriptive analytics examines historical data to evaluate connections, relationships, correlations, and causations.
  2. Predictive analytics uses past patterns to predict future patterns.
  3. Prescriptive analytics takes prediction to the next level by using complex data to predict alternative outcomes to optimize the workforce. 


In a previous article, we described how to use descriptive analytics to develop a test case in which we studied trends and relationships to see how we might affect a business outcome. The time you spent in exploring relationships will guide your thinking in the next step in your path.

The next level in analytics uses predictive analytics to forecast results. The primary tool of predictive analytics is regression analysis.

Regression Analysis

Regression analysis has been with us for two hundred years. In its simplest form, linear regression, it compares two known variables to determine their relationship. In more complex forms, it can compare many variables and even functions with infinite dimensions.

Regression analysis gives us correlations -- how variables are related, and probability -- the likelihood that patterns will repeat. In marketing, we use the techniques to tell us who will be most likely to buy a product (and when, where, and why) so we can target marketing to those consumers.

Many organizations are using regression techniques to determine which candidates for employment will succeed. We find that statistical models are better at hiring decisionsing decisions than manager judgment, and we can create predictive models to tell us which employees are most likely to leave.

Many possibilities come to mind for using analysis of historical trends to predict outcomes. For example:

  • Will the impact of role-playing in leadership training justify the cost?
  • How will a 10% increase in employee health care costs affect employee retention?
  • What will the impact on productivity be if we create a non-managerial promotion path for individual contributors?

Assumptions in Data Analysis

What makes predictive analytics work is the underlying assumptions. A false assumption can render a regression model invalid, and the assumptions that were true last year may not be valid today. Assumptions can also be invalid if we don’t include the right variables.

Good data analysts know how to test for the validity of your assumptions, and analytical software has tools for testing them. What is important for business leaders to understand is that invalid assumptions will lead you astray.

Get Started

We recommend a few guidelines to follow to make sure your early efforts are a success.

  • Clean your data, but don’t waste resources making it perfect. No one has perfect data. You only need to make  good enough to make valid predictions. People data is always messy. Many hands create it, and people make their own judgments about how important accuracy is.
  • Make Marketing your new best friend. Marketing has been using predictive analytics for a long time, and they are superb at predicting human behavior. Let them share their experience with you. You might also share assets and expertise.
  • Engage an experienced human capital data analyst. You may not need to hire a data analyst. You may be able save money and get better results using an analytics consultant.
  • Challenge assumptions. Always be looking for weak assumptions that will skew your results. Question your analysts on their data sources, variables, and assumptions. Ask for explanations of how they deal with outliers in the data.
  • Be transparent. Tell your people what information you are collecting and how you are using it. Show them how you don’t measure people – you measure what they do. Explain how you keep their information safe and anonymous. If you don’t have a privacy policy, create one.
  • Speak the language of business. Be wary of using “soft” measures like employee engagement. Engagement is not an outcome. Outcomes are profit and productivity. Businesses have spent billions on employee engagement programs over the past few years with little to show. Focus on financial results if you want to be heard.

The Payoff

Using predictive analytics will help you make better decisions and boost your chances of funding your projects. More important, your success will build your credibility with business leaders.

Recommended Reading:

Fitz-Enz, Jac, and John R. Mattox, II. Predictive Analytics for Human Resources. New Jersey: Wiley Publishing, 2014.

Davenport, Tom. "A Predictive Analytics Primer." Harvard Business Review. September 02, 2014.

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

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