Whatever has happened in my quest for innovation has been part of my quest for immaculate reality.

—George Lucas

Big Data continues to be touted as the next wave of technology and analytics innovation. From our perspective, the next wave of innovation is less about Big Data and more about how companies leverage Big Data analytics to take action and optimize their business. Having data is not enough; it needs to be leveraged effectively to drive and optimize business action that is coordinated at all levels of the organization. As it relates to People Analytics, Big Data is critical to providing real-time insights to businesses regarding how to maximize the value of the talent for the organization, as well as maximize the organization’s value for the talent it intends to retain and develop. In this chapter, we revisit our Seven Pillars of People Analytics Success in the context of Big Data, providing examples for each pillar to help illustrate the concepts you’ve read about so far throughout this book.

WHAT IS BIG DATA?

Big Data is one of those buzzwords that can mean so many different things, and, as a result, it has the potential to be meaningless. However, to most people, the concept of Big Data is the notion that certain data comes at us so frequently, in so many different forms, and at such high volumes that it’s difficult for a single human to make sense of or analyze it efficiently and effectively. For example, a daily temperature reading over the course of one year is not, by most, considered Big Data. However, if you imagine temperature readings collected every minute across a thousand different types of thermometers placed around the globe—that would be considered Big Data. Most location data such as that provided by WiFi or GPS tracking is considered Big Data because there’s an ongoing stream of it over time.

Big Data is the hottest buzzword to hit the tech and Internet world since social. So, what does it mean? Big Data tends to be a broad and overused term that is varied and ill-defined when you actually ask people to explain what it means to them. Some people define it as web data, whereas others define it as a large data set that cannot be handled by traditional database software; still others define it as data that flows in real time. All definitions have their weaknesses, but we like the one first formulated by Gartner analyst Doug Laney that asserts that Big Data has volume, velocity, and variety.¹ Although we can argue with some aspects of this definition, it’s a helpful way to think about and understand how Big Data may differ from other data sources and how you need to think about it differently.

Volume is the attribute most people mention when they think about Big Data. The idea here is that you are dealing with large quantities of data, usually larger than a normal person can get one’s head around or larger than can be processed using traditional tools. The exact criteria for what is considered large volume is a moving target as the technology is improving so rapidly that yesterday’s large- volume data set is today’s typical-size data set. For instance, the online engagement marketing platform Constant Contact has always dealt in very high-volume data. Each year Constant Contact facilitates 65 billion marketing communications on behalf of customers. This type of activity creates unimaginable flows of data volume. The main appeal in using larger volumes of data is that doing so allows you to predict behavior using statistical models with much more accuracy that smaller volumes of data can provide.

However, volume presents unique challenges of its own to many conventional information technology tools and systems. Data volume calls for scalable storage and a distributed approach to querying. It’s more common for companies to have large amounts of archived data stores, but they often lack the capacity to process that stored data effectively. Companies that can process large volumes of data rely on more than conventional relational database infrastructures. They must rely on, for example, parallel data processing architectures such as Hadoop and Spark. Hadoop is a platform for distributing computing problems across a number of servers that was first developed and released as open source by Yahoo! It implements the MapReduce approach pioneered by Google in compiling its search indexes, and involves three steps. The first step is the “map” stage, where data is distributed among multiple servers and compiled. The partial results are then recombined in what is known as the “reduce” step in order to simplify the data. The last step is the “retrieval” step in which the data models are retrieved from the Hadoop file system and utilized. For example, Hadoop is one of the tools Facebook uses to be able to personalize its site experience for you.

The second element of the definition put forth by Laney is that Big Data has velocity. Velocity simply means that data has an ongoing flow and fast speed coming into your organization, sometimes referred to as “streaming.” Unfortunately, as volume of data has increased dramatically across the enterprise, so has velocity. This is primarily due to the growth of the Internet and mobile usage such that data is flowing 24/7 every day of the year. Therefore, if you are an Internet company, your data architecture and tools must accommodate the processing of high data velocity and volume all the time, nonstop. As a result, companies such as online retailers are able to compile large histories of customers’ every click and interaction, not just the final sales. Successful companies are able to utilize that information in real time by recommending additional products and services. For example, Walmart began using a 10-node Hadoop cluster as a way to analyze the online shopping experience and to make it more personal. It worked so well for the company that Walmart is moving to consolidate 10 different data processing platforms into one 250-node Hadoop cluster to deal with the increased streams of data it needs to process in order to create the strongest possible online customer experience.²

The third important concept to consider in Big Data is variety. In today’s complex world of multiple points of customer interaction and data streams, data comes in a standardized form and ready for processing. Given this, a common theme in Big Data systems is that the data sources and formats are widely diverse and don’t fall into consistent structures that can be easily utilized by a company for processing or analysis. Examples of the variety of Big Data flows with high volume and velocity might include customer comments on a social media website, search terms on a website, click-stream data from an online shopping experience, location data from GPS or WiFi tracking, and image or video uploads, among others. The critical advancement in the data management world related to the principle of variety in Big Data is that traditional structured data is now able to be joined with semistructured and unstructured data. In Big Data, variety is just as important as volume and velocity. Put all three of those concepts together (volume, velocity, and variety), and you start to understand the challenges and opportunities of Big Data.

There is another V that we believe is paramount when addressing Big Data. That fourth V stands for value: the business value that you can create from your data. Big Data without business value is simply noise. Your goal is to find out signals from your deluge of data. That’s why the value is extremely important for Big Data to be relevant to your business.

Big Data efforts require the right people, processes, and systems to execute at a highly skilled level. As companies struggle to get the most business value from their Big Data initiatives, ABI Research forecasts that global spending on Big Data hardware, software, and services will continue to grow at a compound annual growth rate (CAGR) of 30 percent through 2018, reaching a total market size of $114 billion.³ Additionally, IDG Enterprise research indicates that companies are intensifying their efforts to derive value through Big Data initiatives, with nearly half (49 percent) of respondents already implementing Big Data projects or in the process of doing so in the future, with the average enterprise organization expecting to spend $8 million on Big Data–related initiatives in 2015.⁴

As a reflection of how rapidly the Big Data landscape is evolving, IDC issued the following 10 predictions for the future of Big Data. This list is taken from the IDC FutureScape for Big Data and analytics report.⁵

1. Visual data discovery tools will be growing 2.5 times faster than the rest of the business intelligence market. By 2018, investing in this enabler of end-user self-service will become a requirement for all enterprises.
2. Over the next five years spending on cloud-based Big Data and analytics (BDA) solutions will grow three times faster than spending for on-premise solutions. Hybrid on/off-premise deployments will become a requirement.
3. Shortage of skilled staff will persist. In the United States alone there will be 181,000 deep analytics roles in 2018 and five times that many positions requiring related skills in data management and interpretation.
4. By 2017 unified data platform architecture will become the foundation of BDA strategy. The unification will occur across information management, analysis, and search technology.
5. Growth in applications incorporating advanced and predictive analytics, including machine learning, will accelerate in 2015. These apps will grow 65 percent faster than apps without predictive functionality.
6. Seventy percent of large organizations already purchase external data, and 100 percent will do so by 2019. In parallel, more organizations will begin to monetize their data by selling it or providing value-added content.
7. Adoption of technology to continuously analyze streams of events will accelerate in 2015 as it is applied to Internet of Things analytics, which is expected to grow at a five-year CAGR of 30 percent.
8. Decision management platforms will expand at a CAGR of 60 percent through 2019 in response to the need for greater consistency in decision making and decision-making process knowledge retention.
9. Rich media (video, audio, image) analytics will at least triple in 2015 and emerge as the key driver for BDA technology investment.
10. By 2018 half of all consumers will interact on a regular basis with services based on cognitive computing.

BIG DATA AND PEOPLE ANALYTICS

Throughout this book, we have provided multiple examples of companies undertaking People Analytics initiatives using Big Data analytics across the scope of talent management challenges that companies are facing. Now that we have covered a lot of ground throughout this book, we thought it would be helpful to revisit our Seven Pillars of People Analytics Success, and provide a real-life example of Big Data analytics in action for each one. We believe this will help solidify understanding of the pillars as well as how Big Data analytics can be applied to each in order to address key business challenges associated with each pillar.

The rise of Big Data and analytics is changing the way the world does business, and this applies to talent management as well. When you combine the way technology has changed the speed at which people communicate with the vast insight available on human behavior, you get Big Data that can be applied to the workforce. Big Data around workforce behavior and attitudes can help us predict behavior, identify valuable talent like never before, match capabilities to market needs, retain the best people, and act on proven insight to drive business outcomes.

As the complexity of workforce challenges continues to rise, so too does the demand for more quantitative approaches to address the increasingly difficult people-related questions central to organizational success.

The power of People Analytics is in its ability to challenge conventional wisdom, influence behavior, enable human resources (HR) and business leaders to make and execute smarter and more strategic workforce decisions, and ultimately impact business outcomes. To realize value from investments in People Analytics, organizations need to understand:

• The relationship between their workforce strategies and their business challenges.
• The approaches at their disposal.
• The capabilities required to translate raw HR data into defensible action.

Many organizations have built the capability to produce basic HR reports and metrics, and some have begun to use analytics to reveal and understand historical trends and patterns. However, a 2014 IBM study of 342 chief human resources officers reveals that less than 16 percent of companies report having the ability to use data to make predictions and take action on future workforce issues.⁶

LEVERAGING PEOPLE ANALYTICS

The ultimate goal of our Seven Pillars of People Analytics Success framework is to focus your attention on those areas that are keys to talent management success and will lead to the greatest return on investment. People Analytics is at a relatively early stage, and this framework should be used as a starting point for your organization. There is no specific order to use to follow the pillars because the challenges each company faces will differ. For instance, one organization may have a great talent acquisition strategy but be really weak in employee retention. However, you will find there are natural interrelationships between the pillars within the framework, as well as the underlying analytics. Take the Acquisition/Hiring Analytics pillar, as an example. A bad candidate selection could also lead to an increase in turnover rates or high retention costs, which is represented by the Employee Churn and Retention pillar. Depending on the maturity of your company’s People Analytics, each pillar could possibly be addressed separately without following a specific order.

The framework could also be adjusted based on your organization’s most pressing needs. It should help to master the talent management life cycle propelling it with the power of analytics that could be:

• Descriptive: What happened in the past?
• Diagnostic: What is happening now and why?
• Predictive: What will happen and why?
• Prescriptive: What should you do, knowing what will happen?

In the next sections, we revisit each of the seven pillars introduced in Chapter 3 and provide a real-world example of where a company has used Big Data analytics to tackle each.

As discussed in Chapter 3, our 7 Pillars of People Analytics Success include:

1. Workforce Planning Analytics pillar
2. Souring Analytics pillar
3. Acquisition/Hiring Analytics pillar
4. Onboarding, Culture Fit, and Engagement pillar
5. Performance Assessment and Development and Employee Lifetime Value pillar
6. Employee Churn and Retention pillar
7. Employee Wellness, Health, and Safety pillar

WORKFORCE PLANNING ANALYTICS PILLAR

Generally speaking, workforce planning refers to the process that helps identify what talent your organization will require to achieve its business goals and business objectives—from current needs to future needs and succession planning.

Planning should start with a clear definition and understanding of your company mission, and most pressing business goals and objectives. As with any large undertaking, it is important to be transparent and include all internal stakeholders and executives in the process to ensure full organization-wide support. They should understand from the get-go what their role is, how finding the right talent at the right cost will impact the company’s objectives, how HR functions and activities relate to business challenges, and how the business return on investment (ROI) of the initiative is demonstrated.

The Workforce Planning Analytics pillar is about leveraging analytics to proactively plan for the right number of employees with the right skill sets, at the right place, at the right time, and at the optimal cost. It is one of the most important pillars of talent management because it is highly connected to the other pillars. For instance, turnover, resume triaging, and retention insights will feed the Workforce Planning Analytics pillar. It is influenced by the quality and accuracy of the model used to predict churn or employee turnover, both voluntary and involuntary, as well as talent acquisition and promotion models.

Workforce planning analytics helps organizations to create economic value from their human capital planning processes. When properly executed, this approach enables your organization to reduce labor costs (through acquisition, onboarding, retention, and cost per hire), increase productivity, and drive business performance by providing organizations with the right knowledge and tools to be proactive in managing their most valuable asset: their employees.

A great example of a company applying Big Data analytics to workforce planning comes from FedEx. FedEx Corporation is synonymous with overnight delivery, an industry the company developed during the 1970s and one it continues to dominate. FedEx is now composed of five major operating companies: FedEx Express, FedEx Ground, FedEx Freight, FedEx Custom Critical, and FedEx Trade Networks. Operating in 211 countries and employing over 375,000 associates, FedEx is the world’s largest express shipping company. Every business day FedEx makes almost five million physical shipments and processes more than 100 million electronic transactions. FedEx thus uses its planes, ground vehicles, and electronic technologies to speed up transportation so that companies and individuals can transfer time-sensitive material across vast distances in virtually seamless fashion.

As you can imagine, it’s critical that FedEx’s talent management strategy be grounded in Big Data analytics, and workforce planning is no exception. Workforce planning for FedEx is more complex than simply planning for the number of new hires for the following year or two. FedEx needs to also consider the current skills and capabilities that can be leveraged across vast business units spanning multiple geographies. For example, before FedEx acquires a company, its HR department uses Big Data analytics to aggregate employee data from the acquisition target, including employee engagement survey results, and compares them with FedEx data. “Our analysis provides management with another data point before they make their decision,” says Bob Bennett, chief learning officer and vice president of HR. “I try not to use the term ‘Big Data.’ It scares people away,” Bennett says. “The important message is that now, more than ever, deriving value from data is critical in the business environment. HR has an important role because it has to use data to drive employee behaviors, making sure those behaviors are measured, monitored, and shaped to achieve business goals.”⁷

Other frontline stories include companies like Dow Chemical, Black Hill, Bullhorn, and Société de Transport de Montréal that we broadly covered in the workforce planning chapter. Those companies have successfully leveraged predictive analytics and Big Data to optimize their workforce planning.

SOURCING ANALYTICS PILLAR

Once you’ve completed your workforce planning stage, you will have a solid plan of how you will help your organization achieve its business goals. At this stage, you will also start investigating how you will staff your plan, and what channels you can use to accomplish this. Sourcing analytics is about harnessing all the data and talent information available to optimize your sourcing results.

Successfully searching for candidates in today’s globally competitive talent market requires an approach that leverages the power of analytics to identify and locate candidates, assess their potential, and engage with them. The Sourcing Analytics pillar is about harnessing all the data and talent information available to optimize your sourcing results, including how to determine staffing resources and what channels will be most effective to engage potential candidates. We define talent sourcing as a talent management process that consists of proactively searching for candidates to fill specific positions (clearly defined from your workforce plan), leveraging job boards, employee referrals, staffing firms, headhunters, and offline, online, and social media tools and resources.

The Sourcing Analytics pillar is also about understanding and capturing data from both the employer’s decision journey and the candidate’s decision journey to optimize your outcome. It can help a business address questions such as:

• How can a business move a candidate from passive or visitor viewer to a job applicant?
• What are key candidate decision points during job consideration?
• What sourcing channels will optimize candidate search results?
• Where are the best places to search for a specific niche of candidates with tech skills such as those in science, technology, engineering, and math fields?
• How can a business best allocate searching spend and efforts?

A great example of a company applying Big Data analytics to sourcing comes from Wells Fargo. After Wells Fargo bought Wachovia Corporation in 2010, the company began to centralize recruitment functions for its community banking division. The new team would recruit for Wells Fargo’s 6,200 retail branches, call centers and online functions, business banking for customers with up to $20 million in annual revenues, and wealth management for customers with up to $1 million in investable assets. Overall, the newly centralized team recruits between 50 percent and 70 percent of Wells Fargo’s 270,000 employees, including most of the customer-facing roles.

To aid in standardizing recruitment across the company, Wells Fargo implanted Big Data analytics to narrow the sourced pool of candidates to a more manageable volume of candidate flow the bank believed were more likely to succeed if they made it through a full selection and interview process. The bank attempted to focus on the most qualified candidates for teller and personal banker positions based on their background experience, career motivation, performance, and life/work skills.⁸

The predictive sourcing model Wells Fargo developed focused primarily on easy-to-identify or answer things that can reasonably be verified, such as “How many jobs have you had? How long have you stayed in those jobs? How many promotions have you had? What is the highest level of education that you’ve completed?” The bank ended up with 65 questions that each candidate for Wells Fargo’s teller and personal banker positions would answer online and that would be scored in real time. If candidates score high, then they are automatically scheduled for an interview as soon as they complete the assessment.

From the start of the rollout through the end of the year 2012, Wells Fargo collected roughly one million job seeker records and found statistically significant differences in performance metrics and retention rates between those team members that the tool would prioritize for hire and other team members whom Wells Fargo would just hire without the early screening step.

Other frontline stories include companies like Facebook and Bloomberg. For example, Facebook uses StrengthsFinder in a clever way to deploy talent efficiently. Regardless of the job openings it has available, Facebook simply hires the smartest people it can find, and then uses StrengthsFinder results to understand their talents and create a job tailored to each new hire. On the other hand, Bloomberg leverages its People Analytics attribution model to efficiently source for candidates and to help assess the performance of every source of hire, whether it is the company’s career site, traditional job boards, social media, employee referrals, or university campuses. Armed with this data, the talent management team can anticipate what source should be leveraged for specific positions within the company and can adjust the tactics and hiring budget accordingly.

ACQUISITION/HIRING ANALYTICS PILLAR

Whether you have a small company or manage a large organization with thousands of employees, choosing the wrong candidates can have a lethal impact on your business. So ensuring that your organization makes wise talent investments is critical to both long-term and short-term success.

The Acquisition/Hiring Analytics pillar uses analytics to optimize the interview process, helping to determine the best ways to vet candidates, to set up interview questions, and to create some tests that can be used to analyze the correlation between a candidate’s performance during the interview and his or her performance in a particular job function.

By applying advanced analytics to talent data and to the information generated through talent acquisition, businesses can better address talent acquisition questions, including:

• What are the best sets of questions to ask during an interview?
• Is there a correlation between interview performance and job performance?
• How many interviews should we conduct before hiring?
• What is the impact of candidate experience and the interview outcome?
• Do referred candidates tend to perform better than other candidates?
• Which job applicants should you meet for an interview?

A great example of a company applying Big Data analytics to hiring is Transcom. Transcom is a global company providing customer care, sales, technical support, and credit management services through a network of contact centers and work-at-home agents. The company employs more than 29,000 customer experience specialists at 54 contact centers across 23 countries, delivering services in 33 languages to more than 400 international brands in various industry verticals. As a result, the effective hiring and retention of high-performing service professionals is a key component of Transcom’s business strategy.

Using Big Data analytics, Transcom discovered that the trait of honesty was actually a good predictor of future performance.⁹ As a result, the company conducted a pilot project to improve hiring and selection using data analytics. It screened for softer traits like honesty by asking candidates how comfortable they were working on a personal computer and whether they knew simple keyboard shortcuts for a cut-and-paste task. If they answered yes, the applicants were later asked to perform that task. Those who scored high on honesty typically stayed in their jobs 20 to 30 percent longer than those who didn’t.

According to Neil Rae, an executive vice president of Transcom, in the call-center world 5 percent attrition a month (60 percent a year) is great performance. Dropout rates are relatively high in the industry and are calculated at 30-day intervals. Also, it takes from four to six weeks to train a worker, so the cost to hire a replacement for one customer service person who leaves is about $1,500.

Transcom was able to hire fewer people using this analytical approach (about 800 instead of a more typical 1,000 hires) to get 500 workers who were still on the job at least three months later. The big payoff, he says, should come in cost savings and better customer service with less worker churn in call centers. “This makes hiring more a science and less subjective,” Rae says.

Other frontline stories include companies like Microsoft, CISCO, Xerox, and Bloomberg that we broadly covered in Chapter 6. Those companies have successfully leveraged predictive analytics and Big Data to optimize their talent acquisition. Bloomberg, for instance, uses Location Intelligence People Analytics Solutions that tells them things like:

• How popular is a specific skill set?
• How popular is a specific skill set within a certain function?
• How popular is a specific skill set with a certain experience level?
• How many companies are looking for similar roles?
• What schools are nearby that teach courses relevant to those roles?

ONBOARDING, CULTURE FIT, AND ENGAGEMENT PILLAR

Once the right candidates have been hired, they need to be properly onboarded to ensure they are aligned with primary business goals and the overall mission of the company. New hires need to have the best first impression of you as a manager and of your company. Depending on the role and position of your new hire, this should be accomplished within the first 6 to 12 months (depending on the role and candidate) by assisting the new employee with a list of resources and tools along with clear guidance on expectations and goals.

We define talent onboarding as an ongoing talent management process that consists of introducing, training, mentoring, coaching, and integrating a new hire to the core values, business vision, and overall culture of an organization in order to secure new employee loyalty and productivity. Analytics from the Onboarding, Culture Fit, and Engagement pillar can be used to enhance a new hire’s first impression and create business value from your onboarding activities and efforts. It will also help your organization address vital talent management questions, including:

• How can a business improve time to performance?
• Does your new employee fit with company culture?
• What is an appropriate talent onboarding budget?
• What impact does talent onboarding have on employee turnover?
• What is the impact of talent onboarding on employee loyalty?

In this diverse workforce demographic where multiple generations have to work together, cultural fit is critical for the successful integration of your new hire; and employee and company value mismatches are one of the major reasons for early turnover.

For a great example of a company applying Big Data analytics to onboarding, let’s revisit our Wells Fargo example. As part of the plan to improve the effectiveness of its sourcing through simple candidate questions grounded in real-time Big Data analytics, Wells Fargo gathered a lot of valuable information during the sourcing process to help with onboarding its new hire classes. The bank was able to get a sense of the areas of best fit, as well as the areas that needed more training and education for each candidate. This enabled the bank to create personalized onboarding experiences that make it more likely that each candidate will succeed in his or her role.

“This has given us the ability to say that, for those team members we are bringing on board who may not have the in-depth experience and life skills that we would want, we will coach them to help them be more successful,” says Sangeeta Doss, senior vice president, recruiting manager for community banking. “We can determine if we need to give them a different onboarding experience or a stronger coach, and/or buddy them up with other team members for mentoring.”¹⁰

Wells Fargo measured the retention rate after each of the hires was on board for six months and found that teller retention improved by 15 percent and personal banker retention improved by 12 percent.

PERFORMANCE ASSESSMENT AND DEVELOPMENT AND EMPLOYEE LIFETIME VALUE PILLAR

To stay competitive, it is paramount to keep your employees fully engaged in order to meet and exceed your customers’ expectations and achieve your corporate goals. A key component to accomplish this is to monitor the engagement level of your employee population.

We define an engaged employee as happy, enthusiastic, and motivated, and as an individual who eagerly relishes the challenges of her job. Analytics helps to understand the various drivers of employee engagement that deliver happier, more productive workers, and decrease unplanned turnover. It can also help human capital management teams sift through data and talent information to better understand employee engagement and help address some talent management questions such as:

• What are the key drivers of employee engagement?
• How does employee engagement affect productivity and financial bottom lines?
• What is the impact of hard-to-fill positions or hard-to-find skill sets on employee engagement?
• How do talent engagement elements, such as relationship with manager and confidence in leadership and company, affect turnover?

Talent engagement analytics can also provide insights on methods for increasing employee engagement via existing channels such as performance appraisals, the voice of the candidate, industry standards, and other metrics that can boost employee satisfaction and assist in paving career pathways. This pillar can also help organizations assess the correlation between engagement scores and employee performance in the past, present, and future—which is important information for reducing and mitigating the cost of bad hires and ultimately optimizing employees’ lifetime value.

A great example of a company applying Big Data analytics to engagement and performance comes from The Container Store. Founded in 1978, The Container Store operates a chain of more than 60 retail stores in 22 states carrying over 10,000 home organization and storage products. It also provides design and installation services and sells its products online.

The Container Store full-time staffers receive 263 hours of training during their first year, compared to the 7-hour retail industry average, and its salespeople reportedly make 50 to 100 percent more than the industry average. The Container Store also looks for ways to empower its retail staff with technology, and is currently using wearable tech. Although the technology is designed to improve communication within its stores through the application of Big Data analytics, it is also used to monitor employees when they’re at work to ensure engagement and performance.¹¹

Using this technology, store management can access performance data, including how employees communicate with coworkers and customers and where they spend most of their time. Applying these Big Data analytics to employee performance can also help The Container Store identify and acknowledge top performers, along with workers who may be struggling in their positions.

Other frontline stories include companies like Goldcorp and GE that we broadly covered in Chapter 9. Those companies have successfully leveraged predictive analytics and Big Data to optimize their employee performance management. Goldcorp utilized an advanced analytics platform to review 792 million data points at an employee-day level in an effort to find patterns among high-impact incidents. Information on behavioral factors such as month of the year, marital status, age, or compensation structure were the most important predictors of incidents and helped the company to anticipate potential performance issues and apply appropriate adjustment to mitigate the risk and maintain its overall workforce performance.

EMPLOYEE CHURN AND RETENTION PILLAR

Your ultimate goal with all employees, both new and existing, is to earn their trust, commitment, and engagement, so that they can fully achieve their goals and help your organization be successful. However, some of your employees will be high-value creators and the top performers of your organization. Others may require multiple trials in order to address performance issues, which can have a negative impact on your organization. The separation with your employee, whether it is voluntary or involuntary, is called churn or turnover. Voluntary churn occurs when an employee decides to leave an organization due to favorable conditions elsewhere, for instance to work for the competition. Involuntary churn, or attrition, refers to the termination of a position. Leveraging employee churn analytics will help to create business value from employee attrition knowledge by analyzing internal and external talent data intelligence, and help an organization address major attrition questions, including:

• Which employees will experience performance issues?
• Who are the top performers that are at high risk of leaving, and why?
• When are they more likely to quit?
• What proactive actions could be done to retain employees?
• What is the cost of losing top performers?

Employee retention is about proactively identifying and understanding which of your valuable employees are employees at risk of leaving, and when and why they would leave. Analytics can help to marry employee data, company data, and market data to predict and interpret top-performing employees’ behaviors, giving you competitive insights for your retention strategies.

A great example of a company applying Big Data analytics to workforce churn and retention is Omnitracs. Omnitracs provides fleet management solutions based on software as a service to transportation and logistics companies in North America and Latin America. It provides technologies, including solutions for safety and compliance, fuel efficiency, driver retention, fleet productivity, GPS fleet tracking, and fleet maintenance. The company’s mobile fleet management solutions and information services help users to manage assets, handle fuel management, reduce costs, retain drivers, and stay safe on the road. Omnitracs helps more than 40,000 private and for-hire fleet customers manage over 1.5 million mobile transportation assets in more than 70 countries.

Omnitracs also has a strong focus on Big Data analytics on behalf of its customers and does a lot of work to help clients address personnel issues—a big driver of success in the transportation industry. In the trucking industry, a lack of drivers means less revenue and more trucks sitting idle. Omnitracs is a believer that Big Data analytics can provide some powerful insights into driver retention.¹² Predictive models can help indicate when a driver is likely to quit and why, so the employer can improve the situation and prevent the loss of behind-the-wheel talent. Part of this equation is driver satisfaction. Raising the satisfaction within the driver workforce not only mitigates the cost to hire and train a new driver (estimated at $8,000 to $23,000), but prevents a negative ripple effect into the interactions your drivers have with customers when delivering shipments.

As an example, an Omnitracs client’s trucking carrier with 1,400 drivers was experiencing high driver turnover. By using a custom Big Data–powered predictive model, they were able to prevent 290 truck drivers from quitting—reducing driver turnover by half and saving the company $1.2 million. Without knowing anything about any particular driver, the predictive model could analyze thousands of real-time data points and determine with high probability when a driver might be ready to quit for any number of reasons, like frustrations with a fleet manager, a skills gap, or family or financial problems. And, armed with that knowledge and with specific dialogue directed toward a solution, fleet managers were able to connect with their drivers at the right time and in the right way, so that their drivers felt heard and supported.

Other frontline stories include companies like AOL, Google, Deloitte, and Pfizer that we broadly covered in Chapter 8. Those companies were able to optimize their talent retention activities by leveraging the power of Big Data and predictive analytics, demonstrating significant ROI to the business.

EMPLOYEE WELLNESS, HEALTH, AND SAFETY PILLAR

To be successful, organizations have to create and design an environment and culture that promotes the safety, health, and well-being of their employees. This means finances and resources need to be allocated to support these endeavors, which requires a demonstrable linking of investments in employee health, safety, and well-being to company business performance. Best practices include proactive activities such as wellness visits, preventive checkups, and vaccinations to avoid the high cost of urgent reactive procedures.

Leveraged properly, this pillar provides a competitive advantage that can assist organizations in differentiating themselves from their competition, and further showcase the impact of that investment on their bottom lines by addressing questions such as:

• What is the impact of employee well-being and health on company productivity?
• What is the impact of employee satisfaction on customer satisfaction?
• What is the impact of employee health and well-being on company retention and acquisition metrics?

By investing in programs that promote the health, well-being, and safety of their workforce, companies can proactively increase the happiness of their employees. This boosts engagement and improves the quality of services they provide to the customers they serve. Ultimately, the result is a healthier company bottom line.

We can turn back to our Omnitracs example for an illustration of a company using Big Data analytics to improve workforce health and safety. In the trucking industry, everything comes down to safety and ROI. From Omnitracs’ perspective, truck and driver safety improvements will be the most immediate benefit of Big Data technologies for the trucking industry. Any time you can prevent an accident, that’s a good thing—and Big Data will make that possible.

For example, when fleet managers understand when drivers are stressed—and why—they can talk to their drivers about the right topic at the right time, resulting in happier, well-rested drivers who are content with their jobs, produce more miles, earn more money, burn less fuel, and have better safety records. Yes, carriers may be excited about preventing car–truck crashes at the outset, but they’ll quickly find that relieving driver stress becomes less art and more science, returning big benefits for drivers and carriers alike.

To help transportation carriers identify drivers at risk, Omnitracs created an analytics model for accident prediction.¹³ They took driver logs and turned each one into about a thousand distinct data points—from the amount of time the truck driver drove each hour of the day, to how many hours of sleep that driver got and when those hours occurred, to how many times that driver drove through sunrise. Then, they took 27,000 severe accidents from their customers’ data sets and reverse-engineered a severity model, which allowed them to identify predictive data points in a particular driver logbook that indicated the potential for a bad accident. The model is so precise that they can take any driver’s logbook and predict the likelihood of a bad accident each hour of his or her day.

Other frontline stories include companies like SAS Institute and WSIB that we broadly covered in Chapter 12. Those companies have successfully leveraged predictive analytics and Big Data to demonstrate ROI of employee wellness health and safety on their company bottom line as well as the overall talent management key performance metrics.

As a tool for summarizing our approach, Table 13.1 lists some of the key items of information needed in order to have analytical impact (see our IMPACT Cycle framework from Chapter 2) across the seven pillars, as well as examples of companies excelling in each.