Early Adopters: Insurance and Finance

In Win with Advanced Business Analytics,¹ we described how the insurance and financial services industries have been pioneers in collecting, managing, and leveraging predictive analytics to create actionable insights from their data. Given the mandatory reporting environment of these fields, not to mention the direct correlation between accurate data and their revenue, these industries first began using analytics in the 1800s to price life insurance and underwrite marine insurance. They have since continued innovating, undergoing many waves of improvement, including the use of neural networks to optimize premiums; introduction of credit scores to evaluate a customer’s financial position; and application of various behavioral, third-party, and social media data sets to supplement their forecasts and future predictions.

Over the years, the success stories have been numerous, and financial institutions that are not using predictive analytics today in product pricing and underwriting would be seen as obsolete and doomed to failure.

Data Analytics Adoption in Human Resources

Even if People Analytics and predictive modeling in HR are fairly nascent, interest in HR metrics started before the current trend of HR and human capital analytics. As a result, 10 major events occurred:

In 1911, Frederick Taylor, a mechanical engineer who sought to measure the productivity of workers and improve industrial efficiency, pioneered the scientific management of time-and-motion studies. He developed a method for capturing and measuring the effectiveness of an organization’s employees’ work, and summed up these efficiency techniques and best practices in his book The Principles of Scientific Management.² Taylor believed in transferring control from worker to management and focused on the distinction between mental labor (planning work) and manual labor (executing work).

To demonstrate these principles in action, Taylor used “The Parable of the Pig Iron,” which applied his time-and-motion studies to illustrate the role management plays in determining the correct workload for each employee that keeps him or her the most efficient and happy.³ Despite a lot of criticism of his work and principles, Taylor spurred a whole movement of analyzing the data behind physical work, which was first implemented in industrial engineering.

Two years later, in 1913, Hugo Munsterberg, a psychologist and an admirer of Taylor, extended these ideas into applied psychology, including industrial and organizational, legal, medical, educational, and business settings. In his book Psychology and Industrial Efficiency,⁴ he addressed many topics of industrial psychology and claimed that it is not only the physical strength of a worker but also his or her psychology that defines business productivity. He introduced the industry to the notion of worker selection based on a scientific approach of worker testing and job analysis. This later became known as an assessment center.

Munsterberg also outlined a new science in his book—a blend of modern laboratory psychology and economics. He believed that the question of selecting the best possible person for a particular vocation comes down to making a process very scientific (for instance creating tests that limit subjectivity), and that it comes down to fitting the person with the correct skill set with the correct position to maximize productivity.

During World War I (1914–1918), the U.S. Army started large-scale testing called the Army Alpha and Beta Tests of World War I,⁵ which were the first mental tests designed for the masses. In developing these tests, psychologists proved that one could be quite intelligent even though illiterate or not proficient in the English language. This led to the creation of two subsequent tests: the Army Alpha for literate groups and the Army Beta for the illiterate, low literate, or non-English-speaking individuals.⁶ Both tests were based on the theoretical assumption that intelligence was an inherited trait, and the assumption was made that native intelligence was being assessed. These tests also demonstrated how quantitative analytics could be leveraged for resource selection.

In 1920, E. L. Thorndike published his psychometric view of social intelligence, dividing intelligence into three facets: abstract intelligence, or one’s ability to understand and manage ideas; mechanical intelligence, or one’s ability to manipulate concrete objects; and social intelligence. In his classic formulation, Thorndike stated, “By social intelligence is meant the ability to understand and manage men and women, boys and girls—to act wisely in human relations.” Similarly, Moss and Hunt (1927) defined social intelligence as the “ability to get along with others.”⁷

In 1921 and 1923 in English, Carl Gustave Jung, a renowned Swiss psychiatrist and one of the founding fathers of modern-day psychology, published his book Psychological Types,⁸ in which he proposed that people are innately different, both in terms of the way they see the world and take in information, and in terms of how they make decisions. In this tract, he expanded the concept of social intelligence into a science of work, and taught that it is not only our individual skills that create productivity, but also our personalities and how we get along with each other.

Jung’s work in social psychology spurred a global evolution in personality testing. In 1943, driven by a desire to help people understand themselves and each other better in a postwar climate, Isabel Myers set about devising a questionnaire that would identify which psychological type a person was. To do this, she enlisted the help of more experienced psychometricians, and her work, which was first implemented in industrial engineering, was later endorsed by professors from the Universities of York, California, Michigan, and Florida. In 1943, she and Katharine Briggs published the first Myers–Briggs Type Indicator questionnaire, which is still in use today as an assessment tool.

While the roots of analytics are firmly planted in the late nineteenth and early twentieth centuries, most of the HR metrics in use today were developed following World War II. With the essential building blocks in place, much progress was made quickly in HR analytics.

In 1978, Dr. Jac Fitz-enz published the first HR metrics. And in 1984, he implemented the first benchmark metrics in HR and published his findings in the reference book How to Measure Human Resources Management.⁹ In it, he created 30 HR metrics that were developed through a joint effort between the Saratoga Institute and the American Society for Personnel Administration that later became the current Society for Human Resource Management (SHRM) in 1989. Some of the original 30 HR metrics identified where predictive analytics can be applied to derive actionable insights are: voluntary separation, involuntary separation, voluntary separation by length of service, time to fill jobs, time to start jobs, revenue per employee, expense per employee, hire as a percentage of total employees, cost of hire, absence rate, human capital return on investment, turnover rate and cost, and vacancy rate. Employee attrition, also called voluntary separation, happens when an employee decides to leave your organization.

One way to apply predictive analytics is to address questions such as: Who are employees at risk of leaving your organization? When will they leave? Why will they leave? To answer those questions People Analytics harnesses all the data available from internal source HRIS (human resource information systems) data to external talent and market data such as publicly available talent data, labor market data, and competition data to anticipate “talent at risk to attrite” and proactive actions to put in place to save them. There is also a body of People Analytics opportunities arising from a combination of 2, 3, or 4 of the 30 HR metrics to better understand some specific workforce challenges you may face.

In 1995, Rutgers University professor Dr. Mark A. Huselid’s work on high-performance systems demonstrated that the systematic management of HR was associated with a significant difference in organization effectiveness. This work provides evidence that the SHRM did, indeed, have a strategic potential. Shortly thereafter, in 1996, Drs. Robert Kaplan (Harvard Business School) and David Norton introduced the balanced scorecard. And, in 2001, in their book The HR Scorecard, Brian E. Becker, Mark A. Huselid, and Dave Ulrich highlighted how HR scorecards, which demonstrate the alignment of HR activities with corporate strategy and activity, improve organizational outcomes.

In 2005, the first talent management system (TMS) was created. TMSs are integrated platforms that automate and improve the key major processes of talent management, including recruitment, performance management, learning and development, and compensation management. They are tools that also store a wide variety of employee data, from recruitment data, learning and leadership data, performance and compensation management data, and resume profile data to job postings performance and work data. Today, TMS platforms can also store and manage social media data, as well as other digital footprint and talent behavior data.

It was only around 2010¹⁰ that predictive analytics began to appear in the HR departments of most leading companies. And, according to Josh Bersin,¹¹ only 4 percent of Fortune 1000 companies are using predictive analytics and, in doing so, this group’s stocks outperformed their peers on the Standard and Poor’s 500 by 30 percent.

First adopted and implemented by financial and insurance companies in the 1800s, and then within the HR industry throughout the twentieth century, data analytics has been gradually and meaningfully changing business practices and improving organizations’ decision-making abilities.

Facing Today’s Challenges

Over the past 10 years, we have had the opportunity to speak at conferences across the world and to meet with industry leaders in staffing, talent management, and HR from organizations of all sizes on a regular basis. The most common challenge we consistently hear in People Analytics is that industry leaders are inundated with data from a variety of sources and are hampered by disconnected tools and systems. They are seeking the best analytical practices in order to create innovative talent life cycle management processes and optimize their HR teams.

While some are aware that HR will undergo a seismic change with Big Data analytics, those of us who work in the field believe the odds are high that the analytics revolution, which invaded the marketing industry in the late 1990s (with the beginning of the World Wide Web), is coming in HR. So how do we ready our teams for the future?

Having worked in business analytics for many years, enriched with interviews and research with those leaders, and sharing with them business analytics success stories in other functions of the organization, we have found that one of the best ways to tackle the migration from business analytics to People Analytics is by using the lessons we learned when the marketing industry adopted analytics. Why? This is because HR and marketing have significant common denominators, and they both used to be non-data-driven functions and cost centers for most organizations.

By reviewing how marketing enhanced its role and practice over time, we can then derive best practices for the HR field.

From Business Analytics to People Analytics Following the Marketing Analytic Path

Before 1990 (the beginning of analytics use in marketing), marketing was basically a discipline of creativity, art, “gut feel” judgment calls, and experience. In search of becoming a strategic business partner and more accountable in a growing competitive marketplace, the industry underwent a massive change by integrating analytics into its business practices. Searching to strategically attract, segment, acquire, grow, retain, and reward customers, marketing opened up to analytics in order to fully understand its customer base, target demographics, the 360 degrees of its different customers, and the market, and to optimize the entire customer life cycle management and customer relationship management. Thus, marketing analytics was born.

Marketing analytics ushered in a new era. The old days of “spray and pray” and shooting blindly to acquire and retain customers quickly became obsolete. Top-performing companies gradually began to sunset those old gut-driven practices and started using analytics. For the majority of successful companies, marketing coexists with marketing analytics, and constantly leverages the power of data intelligence, which enables them to become a balance between a disciplined art (experience, judgment, and instinct) and science (the intelligence of data).

Talent management and HR teams can learn a lot from their marketing colleagues, adding or building a People Analytics program in order to proactively embrace the analytics revolution. Similar to traditional marketers, HR professionals are generally not known as “data geeks.” The field of HR has traditionally leveraged intuition, judgment calls, instincts, and experience. Although some managers may still rely on isolated metrics from HR dashboards to make major decisions, the field is not historically known for using predictive analytics as a systematic part of the decision-making equation.

Dr. Jac Fitz-enz¹² rightly points out that “HR managers have done a poor job teaching the C-level executive how to achieve a high rate of return on employee investment. On top of that, there is a serious perception issue that the HR function has to fix as well.”

In today’s globally connected and competitive talent marketplace, HR is facing challenges from multiple fronts, and some HR leaders we have spoken with often say their departments are perceived as:

• Cost centers
• Order takers
• Useless
• Disconnected with the business reality
• Tactical, not strategic
• Useful primarily for regulatory and legal reporting

Let’s take a closer look at some of these challenges, so that we can begin to address and rectify them.

• Perception challenge: In some organizations, HR is perceived as a cost center, useless, an order taker, and disconnected with the business reality. But why? As Dr. Fitz-enz points out, “We should ask ourselves this major question: Why don’t CEOs recognize an investment in people as they do in other initiatives functions?” The answer is twofold:
  1. Often C-level executives can make an investment in a nonhuman arena, such as sales production and technologies, and feel confident that a reasonable return on investment will ensue. This is not the case with people. Talent management and hiring have high levels of variability, which can make such investments challenging to evaluate without the proper data and analysis.

  2. HR managers have done a poor job of teaching C-level executives how to achieve a high rate of return on employee investment. Analytics can be used to demonstrate the value of these investments and illustrate the important implications for the organization’s bottom line—for instance, how a 2 percent turnover would impact sales and profitability.

     There was even an article in Forbes claiming that companies should fire their HR departments.¹³ In it, a group of economics researchers conducted a study on 2,500 resumes either with or without photos of the applicant and found that being good-looking (in the case of women) did not help an applicant find a job. According to the article, “attractive” women faced an uphill struggle to get a chance at a job, because 93 percent of the HR staff deciding whether to call someone for an interview were female.

• Strategic challenge: HR has to become a more strategic business partner, proactively providing recommendations that are directly tied to addressing core business challenges and objectives.
• Performance challenge: The challenge is to leverage data analytics in order to master talent life cycle management, bridge the global skills gap, and better attract and retain top talents, as well as to capture the attention of the millennial market in order to ensure succession planning as the baby boomers move toward retirement.

Leaders want to leverage the power of advanced analytics and data intelligence to make more informed decisions. They are seeking to harness the power of Big Data analytics to improve performance in their business practices, and address core business challenges and core talent management questions such as:

• How to attract the best people?
• How to select the best people?
• How to acquire the best people?
• How to engage and develop the right people?
• How to reward the best people?
• How to retain the right people?

These questions are similar to the ones that the marketing industry was able to address by implementing business analytics and predictive modeling into their practices.

At this point, you are probably wondering how HR can piggyback on marketing to undergo its own analytics revolution. To manage human capital for tomorrow, you need HR metrics that are inherently predictive and that will have a high business impact. To move from business analytics to People Analytics, we can simply replace the word customer from any marketing analytics strategy with the word talent or employee. This will provide us with a basic migration of marketing analytics to People Analytics (talent analytics or workforce analytics), as seen in Table 2.1. This is possible because the common denominator between marketing and human capital management is human behavior.

Over the past decades, HR has heavily invested in tools and technologies; however, it is time to move beyond simple dashboards, scorecards, and isolated talent metrics and embrace advanced business analytics to derive true value for organizations.

Advanced Business Analytics Becomes Mainstream

Advanced business analytics starts with a business goal or question, integrates disparate data sources, creates predictions for the future, and leads to strategic actions with measurable results. It is powered by predictive modeling, which has helped marketing teams attract, acquire, engage, grow, retain, and reward their most valuable customers—goals that are very similar to those of HR departments looking to maximize their talent management cycles and that will require HR to think like a marketer.

Depending on their level of analytical maturity and their most pressing business objectives, companies have embraced analytics to create business value at different stages; predictive analytics is becoming a pervasive and competitive differentiator across different industries and business functions:

• Amazon uses analytics to recommend what book to buy, and 30 percent of its sales are generated from these recommendations.
• Netflix leverages analytics to recommend what movie you are most likely to watch and like. And more than 70 percent of Netflix movie choices arise from its online recommendations.
• Companies are using sentiment analysis of Facebook and Twitter posts to determine and predict sales volume and brand equity.
• Target predicts when a pregnant woman is due based on products she purchases by simply combining her loyalty card data with social media information, hence detecting and leveraging on changing buying patterns. This allows the company to target pregnant women with promotions for baby-related products. The company also increased revenue 15 to 20 percent by targeting direct mail with product choice models.
• Google was able to predict the 2009 flu epidemic two weeks ahead of the Centers for Disease Control, simply by leveraging online search trends (e.g., related to symptoms).
• Google’s self-driving car is analyzing a gigantic amount of data from sensors and cameras in real time to stay safe on the road.
• The GPS information on our phones is analyzing how fast the device is moving, which is used to provide live traffic updates.
• During the 2012 presidential election, the Obama campaign team used analytics to micro-target voters in swing states.
• Also during the 2012 presidential elections, famed statistician Nate Silver used analytics to correctly predict Obama’s victory.
• Politicians are using social media analytics to determine where they have to campaign the hardest to win the next election.
• Pediatric hospitals are applying data analytics to live streams of a baby’s heartbeats to identify patterns, and, based on the analysis, the system can now detect infections 24 hours before the baby would normally begin show any symptoms, which allows early intervention and treatment.
• The FBI is combining data from social media, CCTV cameras, phone calls, and texts to track down criminals and predict the next terrorist attack.
• Video analytics and sensor data of baseball and football games is used to improve performance of players and teams. You can now buy a baseball with more than 200 sensors in it that will give you detailed feedback on how to improve your game.
• Artists such as Lady Gaga are using data about our listening preferences and sequences to determine the most popular playlists for her live performances.

It’s clear that analytics is being used in nearly every aspect of our lives and across myriad industries. So, how do we apply it in HR to help solve current challenges?

What Is People Analytics?

People Analytics starts with a talent management business question or goal, and then integrates disparate data sources together to create predictions for the future, which can then be used to outline businesses’ actions with measurable results. Let’s take a closer look at the major components of People Analytics.

Create Actionable and Measurable Business Predictions

Creating business predictions that lead to strategic actions and measurable goals for the future requires you to use predictive models to anticipate what will happen with your employee base and to enable you to put proactive plans in place, addressing:

• Whom to attract?
• Whom to hire?
• Whom to develop and promote?
• And whom to retain?

While these questions are critical for any top-performing organization, you need to translate them into measurable actions that can be presented as a business case to your executive team. This means you need to connect this talent data with business data. For instance, consider:

• What impact will proactive retention of top performers have on customer value?
• Who are the best employees to acquire or promote in order to drive customer satisfaction and loyalty?
• How does employee well-being impact productivity, customer lifetime value, and customer up-sell and retention?

The entire process of creating actionable insights with People Analytics is summarized in Figure 2.1, People Analytics Virtuous Process, where you start with your talent management business question, and then integrate data and use analytics to gain actionable insights.

Business Analytics

Earlier, we discussed that the migration from business analytics to talent analytics happens simply by switching the word customer with the word talent. With that in mind, Tables 2.2, 2.3, and 2.4 will help us map preliminary reporting analytics to predictive analytics. This begins by translating your business question into a talent analytics question. For instance, a marketing question about proactive customer retention could be translated into proactive talent retention, and can leverage similar analytics techniques.

People: Creating the Dream Team

The overall data scientist labor shortage prevalent in other industries is even more acute in the talent management and HR field. In a recent Wall Street Journal article, Jeanne Harris and her colleagues rightly assert that “It takes a team to solve the data scientist shortage”; one of this book’s authors, JP Isson, provided insights to Wall Street Journal for his team solution approach at Monster.¹⁴ Similarly, it takes a team to build a People Analytics center of excellence to solve that shortage.

So, how do we do that? In addition to utilizing existing analytics resources, tools, and technologies to minimize your cost, building a multidisciplinary team of people with a variety of backgrounds will enable you to view your data from different perspectives and derive even greater insights from it. Such a skills-diverse team might include:

• Technical specialists who will work closely with IT teams to secure the collaboration and support needed to gather data from multiple sources and integrate, standardize, and govern it, so that it can be used to help you understand your talent landscape. This oftentimes can be achieved by using certain reporting features and dashboards.
• Statisticians, data scientists, and business intelligence specialists who can help you elevate your talent life cycle knowledge and gain a better understanding of what is happening and why. These professionals offer a different view on the challenges and potential ways to address them, and are hands-on with numerical analyses. Additionally, they can leverage predictive models to anticipate workforce planning needs, such as identifying high-performing employees who are at risk of leaving, candidates who are more likely to be successful once hired, or, say, the number of injuries that might occur in mining and construction industries. These individuals are fact-finding and should help to create actionable insights from your data, and, if you have sufficient funding, you could also include demographers and econometricians for added insights.
• Business analysts and navigators who possess storytelling backgrounds and high data visualization acumen. This group will serve as the liaison between the People Analytics team and the rest of the business. After all, it is about using hard data points to paint a picture of how streamlined and cost-effective your organization could be that will help win over your C-suite.

Process: Creating a High People Analytics Impact with the IMPACT Cycle

The main goal of every organization, and arguably the reason you are most likely reading this book, is to create a high business impact. In order to do this, you need to ensure you have the right structures in place to properly pull your HR analytics.

Thus, in 2012, Josh Bersin (Bersin by Deloitte), who is one of the leading thinkers in HR analytics, created the HR Analytical Maturity Model to explain the different levels of HR analytics adoption. He defines these stages based on four maturity levels:

• Level 1 is labeled Reactive-Operational Reporting and is reflective of companies where HR analytics principally focuses on ad hoc operational reporting. This level is reactive to business demands, is characterized by data isolation, and is difficult to analyze.
• Level 2 is labeled Proactive Advanced Reporting and reflects companies where HR analytics focuses on operational reporting for benchmarking multidimensional decision making.
• Level 3 is labeled Strategic Analytics and is reflective of companies where HR analytics focuses on statistical analysis development of people models and analytics of dimensions to understand cause and delivery of actionable insights.
• Level 4 is labeled Predictive Analytics and is reflective of the development of predictive model scenarios for planning, risk analysis and mitigation, and integration with strategic planning.

With Bersin’s HR Analytical Maturity Model in mind, let’s explore the IMPACT Cycle that will help you quickly navigate across the aforementioned four stages to create high business impact.

Focusing on the IMPACT

In speaking with industry leaders, experts, and business partners, we realized that the data challenges they were facing could be addressed by leveraging advanced business analytics, and the same approach and methodology used in traditional business could be applied to talent life cycle management. In fact, there are critical success factors that organizations leveraging talent data analytics are using to drive better business outcomes. Companies such as Accenture, Deloitte, CGB Enterprises Inc., ConAgra Foods, Microsoft, Google, Goldcorp, FedEx, Xerox, Hewlett-Packard, SAS Institute, Bloomberg, Bullhorn, Sysco, Shell, CISCO, General Electric, Johnson & Johnson, Dow Chemical, and Harrah’s have invested in People Analytics to optimize their human capital and improve business performance, and some have even gained global recognition for being a Best Place to Work.

So, how do these companies do it?

The short answer is that they are focusing on the IMPACT. Coined in our previous book, Win with Advanced Business Analytics, IMPACT is a framework for creating actionable insights at every stage of the talent management cycle. While data is a necessary component of every business, it alone is not sufficient to unlock value for your organization—for this, actionable insight is required.

We speak with business leaders on a regular basis about their data assets and challenges, and it never fails—a sentiment we hear consistently is that their organizations are drowning in data but are lacking in understanding and actionable takeaways from that data. Based on our experience leading and building analytics teams from the ground up, as well as taking into account input we received from researching successful analytical organizations, we developed the IMPACT Cycle to help guide analysts through the process of ensuring they are insightful business partners, rather than just purveyors of data. (See Figure 2.3.)

It’s not always an easy task to get your analysts to pull their heads up from the data and focus on the business; in fact, it is a bit of both an art and a science. The IMPACT framework is made up of the following steps:

• Identify the questions: In a nonintrusive way, help your business partner identify the critical business question(s) he or she needs help in answering. Then set a clear expectation of the time and the work involved to get answers.
• Master the data: This is the analyst’s sweet spot—assembling, analyzing, and synthesizing all available information that will help in answering the critical business question. Create simple and clear visual presentations (charts, graphs, tables, interactive data environments, etc.) of that data that are easy to comprehend.
• Provide the meaning: Articulate clear and concise interpretations of the data and visuals in the context of the critical business questions that were identified.
• Act on the findings and recommendations: Provide thoughtful business recommendations based on your interpretation of the data. Even if they are off base, it’s easier to react to a suggestion than to generate one. Where possible, tie a rough dollar figure to any revenue improvements or cost savings associated with your recommendations.
• Communicate insights: Focus on a multipronged communication strategy that will get your insights into the organization as far and as wide as possible. Maybe it’s in the form of an interactive tool that others can use, a recorded WebEx of your insights, a lunch and learn, or even just a thoughtful executive memo that can be passed around.
• Track outcomes: Set up a way to track the impact of your insights. Make sure there is future follow-up with your business partners on the outcomes of any actions. What was done, what was the impact, and what are the new critical questions that need your help as a result?

The IMPACT Cycle can help you guide discussions on some key talent management decisions, including:

• What type of talent to attract and who to select?
• Which employees to engage and develop?
• Which employees to reward and promote, and whom to terminate?
• How to reduce the cost of a bad hire?
• How to best manage people?
• What drives performance and retention?
• How to assist employees with performance improvement?
• How to increase employee engagement, satisfaction, loyalty, and lifetime value?
• How to improve succession planning, leadership, and overall talent life cycle management?

Technology: Talent Management Tools

Over the past four decades, HR has shifted from mainframe computers and manual payroll systems to cloud-based technology, software as a service (SaaS), and client–server solutions. In fact, the emergence of TMSs has enabled companies to capture, store, and manage a variety of employee and HR data, including social media data and talent digital footprints. As we see the amount of data increase, we see the number of HR tech firms who create these solutions also increase. In fact, this industry represents roughly $4.5 billion in business.¹⁵

But, because this is a journey, it is always best to start small and first master the human capital information at your disposal. As we mentioned, analytics is fairly new in HR, so leveraging experts in the field of traditional analytics technology can help you map out the best approach for your organization.

According to the 2014 HR trends survey conducted by the Information Services Group,¹⁶ there are three major benefits that companies expect to realize from their HR investments:

1. An improved user and candidate experience.
2. Access to ongoing innovation and best practices to support the business.
3. Increased implementation speed to enhance the value of technology to the organization.

When investing in talent management tools and selecting service providers, there are some basic considerations to keep in mind:

• Does it ensure data security and data privacy?
• Does it integrate with existing systems, both HR and your general information technology (IT)?
• Is it customizable (can it align with internal systems and processes)?
• Does it enable reporting and analytics regarding the employee life cycle?
• Is it a cloud-based SaaS solution?
• Is it flexible, with mobile and social platforms that have the ability to handle Big Data?
• Is there a social collaboration tool?
• Does it have an intuitive and user-friendly interface?
• Is it a scalable system?

Before getting started, there are also a baker’s dozen best practices to keep in mind for building a successful People Analytics journey.

1. Get buy-in from your senior leadership team.
2. Tie your People Analytics goals, strategy, and activities to the business goals of your organization.
3. Start small, but dream big.
4. Follow a baby-steps approach when implementing People Analytics. It is an approach that needs to be delivered in stages.
5. Don’t let the “good” be the enemy of the “best” with your data. Oftentimes, talent data can be very messy and not 100 percent accurate at first. It is challenging to aggregate and integrate because of the quality of the data. Start with what you have and improve as you learn.
6. Have some quick measurable wins to build the momentum and create awareness.
7. Leverage existing tools and resources as far as you can before requesting heavy investment in new tools and software.
8. Start by addressing small questions and pieces of information.
9. Use what you learn, and focus on developing and building capabilities.
10. Don’t get lost in the sea of data; let the business question lead your data requirements.
11. Take a collaborative approach with IT, finance, and other departments within your organization. People Analytics should be everyone’s business.
12. Proactively prepare by readying for change management, training curriculum, and end-user experience testing to ensure good implementation.
13. Create a People Analytics exchange hub where professionals from various departments in your organization can share best practices and experiences of leveraging analytics. These individuals could become your advocates and help promote adoption by offering fact-based decisions and data-driven approaches across the organization.

REFERENCES

1. Brian E. Becker, Mark A. Huselid, and Dave Ulrich, The HR Scorecard (Cambridge, MA: Harvard Business Review Press, 2001).
2. Josh Bersin, John Houston, and Boy Kester, “Talent Analytics in Practice,” Deloitte University Press, March 2014, http://dupress.com/articles/hc-trends-2014-talent-analytics/.
3. John W. Boudreau and Ravin Jesutham, Transformative HR (San Francisco, Josey Bass, 2011).
4. The Hot HR Technology Trends in 2014: www.forbes.com/sites/meghanbiro/ 2014/07/20/the-hot-hr-technology-trends-of-2014/.
5. Information Services Group Study: HR Delivery Trends: www.isg-one.com/web/expertise/hr-technology/hr-servicedeliverytrends.pdf.
6. Carl Jung: www.ajna.com/great-thinkers/carl-jung/.
7. Moneyball in HR, Harvard Business Review: http://blogs.hbr.org/2010/03/moneyball-geeks-and-the-new-er/.
8. The Parable of the Pig Iron using Taylor’s Principles of Scientific Management: www.na-businesspress.com/JHETP/GovekarPL_Web12_2_.pdf.