Models | df | CFI | TLI | RMSEA | SRMR | Δ | df |
---|---|---|---|---|---|---|---|
Full measurement model | |||||||
Model A | 498.61/146 | 0.82 | 0.78 | 0.13 | 0.13 | 261.68*** | 3 |
Model B | 371.73/146 | 0.88 | 0.86 | 0.10 | 0.09 | 134.80*** | 3 |
Model C | 842.54/148 | 0.64 | 0.58 | 0.18 | 0.16 | 605.61*** | 5 |
Model D | 412.52/146 | 0.86 | 0.84 | 0.11 | 0.12 | 175.59*** | 3 |
Model E | 459.60/146 | 0.84 | 0.81 | 0.12 | 0.15 | 222.67*** | 3 |
Model F | 669.10/148 | 0.73 | 0.68 | 0.15 | 0.16 | 432.17*** | 5 |
Model G (Harman's single factor test) | 1010.77/149 | 0.55 | 0.48 | 0.19 | 0.18 | 773.84*** | 6 |
Note(s): N = 153, *** p < 0.001; χ 2 = chi-square discrepancy, df = degrees of freedom; CFI=comparative fit index; TLI = Tucker–Lewis Index; RMSEA = root mean square error of approximation; SRMR= standardized root mean square residual; = difference in chi-square, = difference in degrees of freedom. In all measurement models, error terms were free to covary to improve fit and help reduce bias in the estimated parameter values. All models are compared to the full measurement model
a = HR analytics and evidence-based management combined into a single factor
b = HR analytics and technology combined into a single factor
c = HR analytics, evidence-based management and technology combined into one factor
d = Evidence-based management and organizational performance combined into a single factor
e = HR analytics and organizational performance combined into a single factor
f = HR analytics, evidence-based management and organizational performance combined into a single factor
g = All factors combined into a single factor
The authors would like to thank one of the reviewers for this point.
Thanks are given to one of the reviewers who raised this point.
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The authors would like to express their appreciation to Professor Randolph-Seng and the two anonymous reviewers for their valuable feedback and suggestions that significantly improved the paper.
Data availability statement : The data supporting the findings of this study are available at Reserved DOI: 10.17632/hfk7fxt9fm.2.
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A decade ago, someone touting the benefits of “people analytics” probably would have been met with blank stares. Was there value to be gleaned from HR data? Absolutely. But firms were thinking more narrowly about the potential—focusing on core HR systems and gathering straightforward information, such as snapshots of regional head counts or the year’s average performance evaluation rating, rather than using analytics capabilities to manage talent and make evidence-based people decisions.
Today, however, the majority of large organizations have people analytics teams, 1 Innovation generation: The big HR tech disconnect 2019/20 report , Thomsons Online Benefits, July 24, 2019, thomsons.com. 70 percent of company executives cite people analytics as a top priority, 2 “How people analytics can change an organization,” Knowledge@Wharton, May 23, 2019, knowledge.wharton.upenn.edu. and there’s little argument that people analytics is a discipline that’s here to stay. What’s striking, though, is the different ways that firms have approached building their people analytics functions. Team size, composition, and organization vary widely, and priorities for capability development and maturation differ significantly.
Most companies still face critical obstacles in the early stages of building their people analytics capabilities, preventing real progress. The majority of teams are still in the early stages of cleaning data and streamlining reporting. Interest in better data management and HR technologies has been intensive, but most companies would agree that they have a long way to go.
Leaders at many organizations acknowledge that what they call their “analytics” is really basic reporting with little lasting impact. For example, a majority of North American CEOs indicated in a poll that their organizations lack the ability to embed data analytics in day-to-day HR processes consistently and to use analytics’ predictive power to propel better decision making. 3 Based on responses of participants at a McKinsey roundtable of 45 chief human-resources officers in the autumn of 2016. Frank Bafaro, Diana Ellsworth, and Neel Gandhi, “ The CEO’s guide to competing through HR ,” McKinsey Quarterly , July 24, 2017. This challenge is compounded by the crowded and fragmented landscape of HR technology, which few organizations know how to navigate.
So, while the majority of people analytics teams are still taking baby steps, what does it mean to be great at people analytics? We spoke with 12 people analytics teams from some of the largest global organizations in various sectors—technology, financial services, healthcare, and consumer goods—to try to understand what teams are doing, the impact they are having, and how they are doing it.
It helps to think about the growth trajectory of a people analytics team as a stairway with five steps (Exhibit 1). The best teams don’t climb directly from one step to the next one; they are constantly iterating—retracing their steps and climbing the same stairs again—at every level of the journey to the top.
To move from the first step of the stairway (poor data) to the second step (good data), an organization must focus on building a foundation of high-quality data. This usually means that data needs to be extracted from the transactional systems where it is entered and then reshaped, cleaned, and re-coded into a more manageable and easier-to-understand structure that is aligned to the goals of the people analytics team. The more that analysts and data scientists need to clean and recode data to make it usable for even simple analysis, the less efficient the analytics team will be and the longer it will take to develop its skills and capabilities. This is arguably the most difficult step to get right. Significant resources, time, and investment are required to identify and manage core HR data systems, establish a common language and consistent data structure, and determine a basic set of guidelines for data collection, processing, and engineering. These are iterative processes, with no definitive solutions; rather, the processes and their outcomes change as the internal and external talent environments shift, systems are retired and renewed, and links are established among HR teams such as recruiting, training and development, and employee benefits.
As the operating environment changes at an increasingly rapid pace, both capabilities and the technology used to manage and transform data need to be increasingly flexible. In people analytics, as in many other tech-enabled fields, taking an agile approach is now a fundamental requirement. People analytics teams must work together with their enterprise-wide technology groups in a rapid and nimble way to institute new technology platforms, evolve existing infrastructure, and maintain consistent enterprise-wide standards.
Once a strong data foundation is in place, the people analytics team can climb to the third step, making the useful data accessible to the organization and experimenting with new technologies to analyze and disseminate the data. The sophistication that organizations are able to achieve at this step is variable. At the simplest end of the spectrum, teams might focus on automating and visualizing HR dashboards via standard business-intelligence platforms such as Tableau, in order to generate standard reports or respond to ad hoc requests. More advanced teams might prioritize custom builds and software development for self-serve applications, perhaps using their own front-end developers.
It’s evident from our interviews that organizations arrive in different ways at the ability to put data and actionable insights into the hands of decision makers. At several points, organizations must make decisions related to technologies and platforms—decisions such as whether to use homegrown talent or third-party vendors—and the answers vary by organization. As one would expect, the ability to attain advanced automation and self-serve capabilities depends greatly on the quality and accessibility of the underlying data.
Teams that mastered descriptive and automated reporting at step three are ready to climb to step four and build advanced-analytics capabilities. Data scientists, rather than business-information specialists, use programming languages like R, Python, and Julia to join disparate sources of data, build models to help understand complex phenomena, and provide actionable recommendations to leaders making complex and strategic business decisions.
We spoke to people analytics teams at a handful of organizations that are experimenting heavily at this level of the stairway and still have significant room to grow as their companies become open to new statistical tools, scale their data-science talent bench, and pursue a wide range of use cases. While some companies employ “broad-spectrum” data scientists who work cross-functionally to support a wide range of business needs, we found that the most advanced teams have created specific subspecialties in data science (for example, natural-language processing, network analytics, and quantitative psychometrics). These allow people analytics teams to increase their impact on their organizations by providing the advanced insights necessary to support strategic decision making on diverse and complex types of talent issues.
No people analytics team we interviewed has been able to take a full fifth step to reach the top level of the stairway: creating reliable, consistent, and valid predictive analytics. Reliable predictions will enable people analytics teams to analyze and explore practical options for management action. While some organizations have built fit-for-purpose predictive models—mostly for workforce planning—implementing predictive analytics in the context of employee selection, development, or engagement decisions requires a substantially scaled-up data-science operation, massive amounts of highly accurate data (“very big data”), cutting-edge algorithmic technology, and organizational comfort with how to address the impact on fairness and bias.
Beyond the required resources and the complexity of the analytics techniques, the infrastructure also poses a challenge to scalability and could require the use of cloud services. Most of the teams we spoke with are still working from on-premise technological infrastructures and show few signs of migrating their data and analytics capabilities to cloud services in the near future.
Our conversations with people analytics teams in leading organizations reveal a set of six best-in-class ingredients that have helped to propel the teams’ impact, success, and continued growth. These ingredients fall into three main categories: data and data management, analytics capabilities, and operating models. If we were to build a leading people analytics team from scratch, this is what we would strive for.
All great analytics teams are enabled by strong data standards, engineering, and management, and our interviews confirmed that this is no different in people analytics.
Significant and dedicated data-engineering resources. We found that the greatest team differentiator was the level of dedicated data-engineering resources available to it for propelling data creation and quality control. The leading teams have full ownership of their own data repositories, allowing them to rapidly test new ideas, iterate, and reduce dependencies on enterprise-level technology resources.
An added benefit of dedicated data-engineering resources is that they enable strategic alignment. Data engineers who are steeped in the strategic context of their organization’s people analytics teams are able to design the data foundation and analytics solutions more thoughtfully and deliberately from the beginning.
Breadth and depth of data sources. Leading teams have invested heavily in a strong HR-data foundation but also have advanced ways of going beyond the core HR systems to use several additional internal sources of data. The most straightforward way might be seamlessly linking the HR data with finance data, though data priorities will differ depending on organizational context. A few teams have begun to step beyond relational databases to build graph databases 4 A type of NoSQL database, graph databases are able to model relationships within data in a powerful and flexible manner. For more, see Antonio Castro, Jorge Machado, Matthias Roggendorf, and Henning Soller, “ How to build a data architecture to drive innovation—today and tomorrow ,” June 3, 2020. for advanced network analytics. In addition, leading teams have a robust and flexible survey strategy for monitoring employee sentiment. They are also able to integrate their survey data with multiple other data sources to create multidimensional quantitative and psychometric models that help explain employee engagement trends and dynamics.
While it is common for people analytics teams to feel constrained by a lack of easily available data, leading teams are more creative with data, acquiring new sources or combining existing ones in new ways to attack the problem at hand. For example, time-sheet data could be transformed and loaded into a graph database and linked by activity or project codes to allow better analysis of teamwork and collaboration.
Advanced people analytics projects can require both deep technical knowledge and the ability to integrate and translate across a wide array of expertise and input. The best teams are building their talent bench with breadth and depth.
Robust data-science function. As we expected, all the leading people analytics teams we interviewed have invested heavily in acquiring data-science talent, though their approaches differ. Some teams focus on hiring “all-around athletes,” while others prioritize specialized backgrounds such as quantitative psychometrics or natural-language processing. Leading teams have sizable data-science “pods” that span a wide range of advanced analytical methodologies, programming languages, and academic backgrounds. The best teams hire and develop specialists in specific disciplines of data science but nevertheless expect all of these individuals to operate in a nimble, cross-functional way in order to meet evolving needs.
Strong translation capability. Leading teams also complement their high-caliber technical talent with skilled “translators”: specialized “integrators,” who bridge the gap between business leaders and technical experts. They translate strategic challenges into analytic questions and use evidence-based practice to interpret insights derived from the analytics, engage stakeholders, and ultimately propel business changes. Translators often serve as an entry point to the people analytics team, helping to raise awareness of the team in the organization and build the team’s credibility. Some of the leading people analytics teams have built benches of internal consultants to partner directly with individual businesses on their specific problems.
In a fast-developing field, people analytics teams need to deliver impact across the organization and stay ahead of the curve to maintain that impact into the future. The best teams align themselves well against organizational priorities while maintaining space for open experimentation and innovation.
Innovation as the norm. Members of leading teams are explicitly expected to explore and innovate beyond their day-to-day fulfillment of the needs of their clients. Some companies have rules of thumb for the percentage of time that teams spend on exploration as opposed to core foundational work. These expectations allow teams to fully experiment and build out proofs of concept.
This process can take a variety of forms, but the important distinction is that the areas of innovation need not directly support an existing business priority or client need; they might be purely exploratory. For example, some data scientists relish the extra time to play around in a sandbox and learn how analytic tools and services work in the cloud. Others might want to explore creative new ways to visualize data in order to equip business leaders with helpful insights. The goal is to ensure that all team members are constantly forming new ideas and looking for new ways to meet the analytic needs of the organization and thereby help it achieve its objectives.
Clear alignment with clients and organizational use cases. People analytics teams take different approaches to organizing themselves and aligning with different clients. What is consistent, however, is the presence of a mechanism for attaining an in-depth understanding of enterprise-wide priorities as well as the specific needs of individual clients. This mechanism creates feedback loops that enable continuous learning and iterative development, and it ensures that people analytics teams are working on the most pressing and high-impact topics.
A culture of trust, empowerment, and ownership is the critical foundation for ensuring that a people analytics team is aligned with its clients as well as the enterprise. People analytics teams routinely deal with urgent (and often ambiguous) client needs and questions, highly sensitive data, and challenges to extrapolating meaningful and actionable insights that will guide business decisions. The bar to entry for the best teams is high: members must own their work from end to end and be empowered to define the constraints of any analysis, protect privacy as well as fairness and equity, flag issues that arise, and use their own judgment to derive insights. Being reactive and incremental is not enough in human resources, where priorities change and the top ones require immediate attention.
Over time, as organizations become increasingly dependent on the quality of their insights, the best people analytics teams play a stronger role in shaping the HR agenda, influencing how the organization manages its talent at both a policy and a process level.
The COVID-19 crisis provided a natural experiment for one large, global organization with a strong people analytics team to use the ingredients outlined in the previous section by rapidly creating a homegrown weekly pulse survey to track the opinions and feelings of tens of thousands of employees around the globe. This capability enabled the organization to better understand the best ways to support employees in a challenging time and a fully remote work environment.
Setting up the pulse survey required intensive collaboration between diverse, highly skilled individuals already embedded in the organization’s people analytics team as well as rapid and close collaboration with the leadership of the organization. Translators navigated the need to craft questions that engaged employees, gathered high-quality data to feed the analytic models, and communicated insights back to leaders who had urgent decisions to make about how to best support their workforce in an external environment that was highly unpredictable and changing week by week.
To speed the time to insights, data engineers established an automated and continuous link among weekly survey-response data, core HR data systems, and a broader set of additional data sources, including data sets that data engineers had developed and customized for this purpose. This process cleaned, tested, and prepared the data for analysis. In addition to rapidly providing analysts with weekly data to examine and synthesize, it fed these data to a prototype self-service reporting tool, which gave leaders the ability to directly investigate aggregated pulse data within six hours of the survey’s close.
The customized data sets supported both exploratory and targeted analyses and helped generate actionable insights for the leaders. Analyses were designed to build on the organization’s current understanding of the health of its employees, marrying new and existing information to yield new insights that guided various efforts. For example, specialists in natural language processing used structural topic modeling to identify and quantify topics in the free-text comments that employees submitted as part of the survey each week. Sentiment analysis was used to understand the emotion behind each topic. These results were then married to the demographic information prepared by data analysts, allowing managers, leaders, and other decision makers to understand how the conversations and associated feelings varied by subpopulation, such as parents and less tenured employees. The combination of data sources and analytic approaches ultimately revealed population-specific needs, which allowed the organization to target specific groups and tailor the type of support it offered to maximize impact.
Exhibit 2 is a view of the major topics generated from the free text of the employees who responded to the pulse surveys and how their emphasis on these topics changed over the course of two months of the crisis. At the beginning, employees were thankful for the health of their families and peers and had generic concerns about the developing situation, but as the crisis evolved, their thoughts crystallized into the more particular concerns of isolation, remote work, childcare, and work-life balance.
The ability to rapidly develop this capability, turn around a wide range of sophisticated analytics within 24 hours after the survey closed, and repeat the survey weekly did not come easily to the organization or the people analytics team. The capabilities required to pull it off were tightly rooted in the data, analytics, and operating-model ingredients that we have identified as the hallmarks of great people analytics teams.
Despite the vast differences that exist among organizations’ data quality, integration, and infrastructure, we all certainly have a lot to learn from each other. Answering the following questions will be helpful to leaders who want to identify where their organization’s people analytics is now and where they would like them to be:
While no single model is the “correct” one for developing the capabilities of a people analytics team, leading teams seem to have a set of ingredients in common. While the past decade has brought about real change, even the best teams—those that iterate at each step of the stairway and learn as they ascend—have barely scratched the surface of what’s possible with people analytics.
Elizabeth Ledet is a partner in McKinsey’s Atlanta office; Keith McNulty is a director, people analytics and measurement, in the London office; Daniel Morales is a director of analytics in the Washington, DC, office; and Marissa Shandell is an alumna of the New York office.
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HR as a function has undeniable importance from a business management perspective. With the advancement in technology, 2022 saw a huge technological shift in this aspect of business management as well. Apart from digitizing all other business aspects, organizations have begun to incorporate technology and data into HR practices as well.
An american mnc reduces attrition using people analytics and forecasting.
Case: This American MNC is a client of PeopleStrong and is suffering from a high turnover of employees at five locations. The company intended to install analytics in order to evaluate the main drivers of attrition and do forecasting for their occurrence at different business locations.
Solution: An integrated tool for workforce analytics was created and implemented. This tool could capture attrition results and their drivers and do a forecasting based on trends.
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Result: The forecasting report predicted that 500 of the 5000 employees were going to quit in the next 6 months. Better employee retention policies were designed which included rewards and incentives apart from better people strategies. Even though 250 people still left, the figure was 50% lower than the prediction.
Case: Under Armour, an American organization dealing with the manufacture of sports and casual apparel and footwear, is a global company. With more than 130 global outlets and 8500 employees, their ATS system received more than 30,000 resumes in a month. Thus, hiring was a cumbersome process for them as well as candidates applying for a job.
Solution: They engaged in a digital recruitment system called Hirevue. With Hirevue, managers could create interviews with candidates with the help of pre-recorded questions. This screening process helped managers call in only employees who met their requirements for webcam or mobile recorded interviews.
Result: Managers could now hire new employees much more quickly. There was a 35% reduction in time in the overall interview to the hiring process. Talent quality also improved.
These above case studies show the emerging trend of incorporating analytics in the HR function of business management . This can also be seen to have positive results in the recruitment and retention processes.
Human resource management is quite a recent term. Employees are treated with a lot of respect and regard nowadays compared to earlier. There were times when workers were considered to be expendable and they had few rights. Working conditions were miserable and people had no say in how organizations are operated or in the way they were treated. The industrial revolution is what brought changes. Companies started realizing that keeping employees loyal was essential for running businesses smoothly.
Caring For Employees During The Industrial Revolution
Courses for human resources certification online teach that before the industrial revolution there were hardly any large industries and a need for managing workers was not felt. Working conditions were dangerous for them and pay was hardly commensurate with what work they did. In the late 1900s, companies like the UK-based Cadbury and Jacob from Ireland appointed welfare officers. These firms introduced a system of payment during sick leaves and cheap housing for employees.
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It was F W Taylor during the early twentieth century who introduced a system for managing staff. He believed that people could be trained to become experts in certain jobs. The famous carmaker Ford adopted his methods. Tools in manpower management like job analysis, employee selection procedures, and training methods were introduced during this period. Certain fast food organizations also adopted Taylor’s theories. His mistake was that he did not think people can get bored with doing the same job.
Two events that changed many things for us are the first and second world wars. Employee unions had been formed during the first world war. As men went to fight wars, women came to be seen more in workplaces. In your HR training certification by IIM Raipur , you will learn how companies had to think about managing workers and form new rules. Recruitment, dismissal, bonus, and absence from work came under the scope of manpower management.
Researchers like Elton May opined that factors like motivation, job satisfaction, leadership skills, and group dynamics could influence performance. The improvement in the economy after the war saw many firms adopting a more flexible approach to staff members. Big companies used employee benefits to lure and retain people. Personnel and welfare work was in full swing during the second world war, but it was done in a bureaucratic style as government-run firms influenced law-making.
The Post-War Scenario
The 60s were not good times for industrial relations as it was found that none of the entities involved in negotiation had skills to discuss issues of employees. As the decade came to an end, employment opportunities improved, and along with this, people management techniques began to be used. When you study human resources certification online courses you will know that terms like motivation, organizational behavior, and management training were heard more commonly.
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In the seventies, much was talked about rewarding employees. The next two decades saw economies sliding and companies becoming less profitable. But it was also then that many organizations realized the importance of retaining people. They began looking at workers as an asset that must be taken care of if the firm wants to have an edge over competitors. Humans started to be regarded as resources that need to be effectively managed. Human Resource Management was born.
The Nineties To Now
It is no more only personnel management and administrative tasks for workforce heads. The HR training certification by IIM Raipur will tell you that it is more about employee engagement and development that people managers are tasked with now. Human resource departments are strengthening the culture in an organization and finding people who can fit that environment. They are also tasked with ensuring that every employee gets an opportunity to use his or her talents for the benefit of their companies.
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HR managers are more focused on workers than on processes. This department is also gaining more importance as management’s realize a need to attract and retain the best talents available in the market. HR leaders find themselves among the C-suite as their role in getting the best out of employees is increasing. They must understand the needs of a more diverse, multicultural, and multigenerational workforce and ensure to fulfill them. Retention of good hands has assumed much importance nowadays.
The human resources certification online courses will teach that it is not just enough to employ and retain people, but they must also be trained and developed. The speed at which new technologies emerge, there is a need to keep employees abreast of modern developments. HR managers must continuously update themselves with modern technology and arrange training programs to empower workers with new skills. The journey of staff members in an enterprise will be that of continuous learning.
Acquiring best talents and retaining them will remain the focus of any progressive organization. People managers will have to find innovative means to attract those who are equipped with the latest skills required for a job. Engaging with prospective employees through social media platforms will be practiced by more HR heads. There will be increased use of automation for screening resumes and conducting initial interviews. This will speed up the process and reduce costs.
HR departments will be trying innovative methods to improve employee experience in the company. They will find out the requirements of the new breed of recruits. Learning opportunities will be improved. Promotions and salary hikes will no longer be based on experience or seniority. New procedures for evaluating employees will be used. Getting HR training certification by IIM Raipur will teach new methods that are used by global enterprises for appraisal and rewarding.
Looking at the evolution of human resource management can show you that there has been a shift from looking at employees as only a means to achieve company objectives, treating them as individuals, and satisfying their needs. There is a realization that it is equally important to ensure that their goals are achieved and these objectives are in line with that of the organization. HR departments will play a more important role as retaining good talent becomes crucial. Combining the human force with machines and using that synergy will be highly important in the future.
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SmashFly Recruitment Technology Blog
JULY 5, 2016
The post Case Study : Optimizing Recruitment Marketing Strategies to Exceed Hiring Goals appeared first on SmashFly Blog. In 2012, Bright House Networks started a large and essential initiative to improve recruiting across the company. It’s first crucial steps were to create a Center for Excellence.
JANUARY 7, 2019
A lot of people analytics professionals are looking into the drivers of performance in their respective companies. Not too long ago, we undertook a study to establish the drivers of performance for a financial institution.
SEPTEMBER 5, 2018
Case study . From the perspective of evidence-based HRM and HR analytics , I have researched the expatriation process and its implications for several years at multiple global organizations. Here, I would like to take you through the study we conducted at two large multinationals looking at employee retention specifically.
Strategic HCM
OCTOBER 7, 2014
Yes - it''s the Analytics , Analytics , Analytics focus which kicks off HR Technology (US) this year. But analytics isn''t even close to playing the role that HR tech can play. But other than filling a conference room, all of this hype around the topic does us (HR, and HR Analytics professionals) no good what so ever.
ClearCompany HRM
APRIL 21, 2022
To view more real client case studies and see their success using ClearCompany, take a look at our client page. ClearCompany Reporting and Analytics helped Milan spot gaps in their recruitment process, leading the company to increase branding and recruitment marketing efforts. Chad Dunnam.
Bersin with Deloitte
OCTOBER 14, 2013
Not surprisingly, analytics was one of the hottest topics at the conference (I think the only way to avoid the words “Big Data” these days is to escape to a desert island.). Just about every vendor I met had some type of analytics solution. SAP/Successfactors, Oracle, Workday) had their analytics solutions on display.
Littal Shemer
OCTOBER 11, 2022
This list of People Analytics and HR-Tech books is not exceptional. So here is my People Analytics and HR-Tech reading list on Kindle (no paper books, as I like the trees), ordered chronologically from newest to oldest. People Analytics – Build the Value Chain This book, by Littal Shemer Haim , is not a typical textbook.
Cornerstone On Demand
MARCH 23, 2017
In the world of enterprise software applications, transactional processing and data analytics have long lived independently—largely because the latter was an afterthought. As our IT architect Reza Seraji said in a case study about the upgrade, “Without this project we wouldn't be able to provide a near [RTDW] solution to our clients."
MAY 11, 2016
We’re currently listening to Christian Vie — Head of HR Analytics at AXA speaking about Social Transformation: How People Analytics Can Help Drive the Digital Change. This concerns the largest analytics project at AXA supporting digital and social transformation. But a really important compelling case study nevertheless.
MARCH 25, 2024
. - Advertisement - Talent Acquisition Excellence: Using Digital Capabilities and Analytics to Improve Recruitment (KoganPage) is co-authored by Kevin Wheeler —a seasoned HR, talent acquisition and L&D consultant from Fremont, Calif.—and Without analytics , all decisions are subjective and potentially biased,” Wheeler told HRE.
OCTOBER 22, 2014
The one of the four which seemed to have grown in emphasis, becoming a bit of a prima donna at the HR Technology (US) conference, is analytics and it''ll be interesting to see whether we have the same take on its growing importance over here. That same agenda is now coming to analytics too.
Pixentia - HCM
MARCH 7, 2022
Maybe you’re in the procrastination phase and feeling unsure about taking the plunge to apply people analytics , despite being sold on the potential value to your business.
JULY 10, 2024
The report, called The Team Network Effect , is based on over 200 interviews, case studies and a survey of more than 1,500 participants from i4cp’s boards in 76 countries. Edward Jones builds teams with people analytics According to the i4cp findings, few organizations track team performance metrics across the enterprise.
APRIL 28, 2016
The amount of commentary around HR analytics continues to grow, but in my view, a lot of it is misleading. There's also a real lack of good case studies . I've written about this in a post on Workstars's blog where I've also suggested that social recognition systems provide a great way into HR analytics too.
Empxtrack - HR Tech
JULY 24, 2024
Analytics and Reporting : Multiple reports were customized to meet client requirements. The images shown in this case study regarding the workforce management, contain dummy data. Read More Case Studies The post A Customized Workforce Management Solution for a Global Mining Company appeared first on Empxtrack.
JUNE 26, 2024
The reports and analytics provide real-time insights into employee hours, project timelines, and resource allocation. Narayanan Subramanian | AVP – Human Resources | Saksoft This case study is about a leading technology service provider looking for a time keeping software to manage employee time effectively.
APRIL 21, 2021
Outsmart, the leading people analytics and workforce planning conference, takes place online on May 5-6, 2021. There’s a strong chance that your organization is either planning on adopting people analytics or expanding your existing people analytics capabilities in the near future. 4 tips for people analytics success.
MARCH 12, 2019
Any leader–whether a CHRO, an IT director or a leader in reporting and analytics –that’s thinking of buying or deploying people analytics has seen that data-driven organizations outperform. Last year, we further saw that “advanced” organizations outperform those early on in their analytics journey ! Incorrect data methodologies.
MAY 14, 2018
These books can change your career: People Analytics and HR-Tech reading list. This list of People Analytics books is not exceptional. So here is my People Analytics reading list on Kindle, ordered chronologically, from newest to oldest. People Analytics & Text Mining with R. Be careful! Let’s face it.
MAY 24, 2016
Jennifer Tracy, Senior Director of Talent Acquisition and Diversity at Bright House Networks, and Mary Grace Hennessy, Chief Product Officer at SmashFly, took a deep dive into recruiting analytics in our latest webinar. Continue Reading → The post Do You Know the Answers to These Recruiting Analytics Questions?
JULY 24, 2019
People Analytics Leader – Survive Your Onboarding! We share a lot of case studies within our People Analytics professional community. It enables us to jointly educate ourselves with great examples about connecting business questions to analytics projects and products. Background.
FEBRUARY 29, 2012
This definition leads the authors to including things like engagement surveys as example of analytics whereas to me, these are clearly measurement approaches rather than analytical ones (the analytics then follows the measurement of engagement). And I’m largely unimpressed by the book’s case studies .
MARCH 6, 2018
Perhaps more important to Christine, he’d been recommended as a 96% match for the job by HR’s new people- analytics system, which she had championed. The goal of the HR initiative was to use data analytics to inform hiring, promotion, and compensation decisions. The post Case Study : Should an Algorithm Tell You Who to Promote?
AUGUST 12, 2019
This article lists the eleven best HR analytics courses in the world today. Getting started with HR analytics – also called People Analytics – is a big step for a lot of people and organizations. An HR analytics course that answers these questions can be invaluable. 2: People analytics – University of Pennsylvania.
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Case study on how the HR analytics team were able to increase data maturity and improve business performance
This case study provides insight into Coca-Cola Enterprises’ (CCE) data analytics journey. Given the complexity of the CCE operation, its global footprint and various business units, a team was needed to provide a centralised HR reporting and analytics service to the business. This led to the formation of a HR analytics team serving 8 countries. Read the full case study to find out how the HR analytics team were able to increase data maturity and improve business performance. |
The HR analytics journey within CocaCola Enterprises (CCE) really began in 2010. Given the complexity of the CCE operation, its global footprint and various business units, a team was needed which was able to provide a centralised HR reporting and analytics service to the business. This led to the formation of a HR analytics team serving 8 countries. As a new team they had the opportunity to work closely with the HR function to understand their needs and build a team not only capable of delivering those requirements but also challenge the status quo.
"When I first joined Coca-Cola Enterprises in 2010, it was very early on in their transformation programme and reporting was transitioned from North America to Europe.
At that point we did not have a huge suite of reports and there was limited structure in place. We had a number of scheduled reports to run each month, but not really an offering of scorecards or anything more advanced."
The first step was to establish strong foundations for the new data analytics programme. It was imperative to get the basics right, enhance credibility, and automate as many of the basic descriptive reports as possible. The sheer number of requests the team received was preventing them from adding value and providing more sophisticated reports and scorecards.
CCE initiated a project to reduce the volume of scheduled reports sent to customers, which enabled them to decrease the hours per month taken to run the reports by 70%. This was a game changer in CCE’s journey. Many of the remaining, basic, low value reports were then automated which allowed the team to move onwards in their journey and look more at the effectiveness of the HR function by developing key measures. The analytics team was soon able to focus on more "value-adding" analytics, instead of being overwhelmed with numerous transactional requests which consumed resources.
"In the early stages requests were very basic. For example, how many people am I supporting? How many people have started or left? How many promotions have there been in my part of the organisation? The majority of requests were therefore very descriptive in their nature. There was an obvious need to automate as much as we could, because if we could not free ourselves of that kind of transactional reporting, there was no way we were going to add any value with analytics.’’
The team soon found that the more they provided reports, the more internal recognition they received. This ultimately created a thirst within HR for more data and metrics for measuring the performance of the organisation from a HR perspective. The HR analytics function knew this was an important next step but it wasn’t where they wanted the journey to end. They looked for technology that would allow them to automate as many of these metrics as possible whilst having the capability to combine multiple HR systems and data sources.
A breakthrough, and the next key milestone in the journey for CCE, was when they invested in an "out of the box" system which provided them with standard metrics and measures, and enabled quick and simple descriptive analytics.
Instead of building a new set of standards from scratch, CCE piloted preexisting measures within the application and applied these to their data. The result was that the capability to deliver more sophisticated descriptive analytics was realised quicker and began delivering results sooner than CCE business customers had expected.
‘‘We were able to segment tasks based on the skill set of the team. This created a natural talent development pipeline and ensured the right skill set was dedicated to the appropriate task. This freed up time for some of the team to focus on workforce analytics.
We implemented a solution that combines data from various sources, whether it is our HR system, the case management system for the service centre, or our onboarding / recruitment tools. We brought all that data in to one central area and developed a lot of ratios and measures. That really took it to the next level.’’
As with any major transformation, the evolution from transactional to more advanced reporting took time, resource and commitment from the business, and there were many challenges for the team to overcome.
"There were a lot of lessons. With the workforce analytics implementation we probably underestimated the resource and the time needed. Sometimes less is more and we provided too many metrics at first. The key was to really collaborate with our HR leaders and understand what the key metrics were."
With the standards in place CCE then turned to establishing a basic scorecard approach to illustrate the data. Scorecards are a common instrument used by many organisations to provide an overview of the performance of a function. Typically they consist of clear targets illustrated in a dashboard fashion and are utilised by senior management to guide their leadership of the organisation. The leadership team's familiarity with the scorecard methodology meant that the analytics team could simply fit in to a standard reporting process. But for CCE to create its HR dashboard it was apparent that a clear purpose and objective for the analytics was needed, and that the development of future scorecards should be as automated as possible.
At CCE it’s clear that HR analytics, insights, and combining HR and business data is an illustration of the value that HR can add to the business. CCE has developed a partnership approach which demonstrates the power that high quality analytics can deliver, and its value as a springboard to more effective HR practices in the organisation. By acting in a consultative capacity HR is able to better understand what makes CCE effective at delivering against its objectives, HR ensures both parties within the partnership use the data which is extracted, and find value in the insights which HR are developing.
"To be a consultant in this area, you have to understand the business you’re working in. If you understand the business problem then you can help with your understanding of HR, together with your understanding of all the data sets you have available.
You can really help by extracting the right questions. If you have the right question, then the analysis you are going to complete will be meaningful and insightful."
There are numerous examples where the HR reporting and analytics team have partnered with the HR function and provided insights that have helped to develop more impactful HR processes and deliver greater outcomes for the business. As with many organisations it is the engagement data with which the majority of HR insight is created. Developing further insight beyond standard survey outputs has meant that CCE has begun to increase the level of insights developed through the method, and by using longitudinal data they have started to track sentiment in the organisation. Tracking sentiment alongside other measures provides leaders with a good indicator for sensechecking the power of HR initiatives and general business processes. The question is whether the relationship between engagement and business results is causal or correlative. For CCE this point is important when explaining www.valuingyourtalent.org 4 the implications HR data insights to the rest of the business.
"There have definitely been a number of examples where we are starting to share insights that are being acted upon. One example is our engagement survey that is run every couple of years. Within the survey there are three questions related to communication.
The business was keen to understand if there was a correlation between how an employee scores a manager, in terms of communication, and key performance indicators across our sites.
We demonstrated that across all of our sites there was a positive correlation between how leaders communicate and business outcomes. That is great but it is not implying causation. There is something there to explore further, but we cannot go and say, good communication causes better business performance."
For CCE's analytics team one of the most important next steps is to share the experience and knowledge gained from developing the analytics function with their colleagues, and build capability across HR.
"We are also reviewing the learning and development curriculum for HR to see what skills and competencies we need to build. One of the competencies that we have introduced is HR professionals being data analysers.
For me, it is not only understanding a spreadsheet or how to do a pivot table, it is more understanding what a ratio is, or understanding what their business problems are, or how data can really help them in their quest to find an intervention that is going to add value and shape business outcomes."
As with any long journey the analytics team at CCE have faced numerous barriers. The challenges they list are common to most HR professionals attempting to establish a significant new process, but it is the challenge of establishing new capability and embedding fit-for-purpose technologies which has created the greatest challenge at CCE.
"In terms of barriers, technology is one. For example having the right data warehouse in place that allows you to extract the data very quickly. From a HR perspective we are well placed, however extracting data from the rest of the business, is a challenge. At CCE HR is trying to branch out and get the data from other parts of the business, which is probably quite unusual. People probably do not expect HR to be that kind of driving force.’"
CCE recognises a recruitment challenge centred on sourcing the capabilities to develop high-impact HR analytics, which includes hiring individuals with the ability to analyse data, develop insights and the communication know-how to share across the business. One challenge for HR is to sell the profession as suitable for analytical high-potentials to build their broader business acumen: to move away from the traditional view of transactional HR with little or no analytical capability, to a function based around high-quality data and business insights. For CCE this represents a significant opportunity – high-calibre analysts must see HR as a profession in which they're able to build a lasting career.
"At conferences I have listened to major firms who have PhD students in their business intelligence teams, who appear to be very good at not only analytics but also presenting information. They are few and between and I believe that people who have that skill set would not naturally go into HR. If I reference the recent big data conference I went to, and the projects that some of these companies were doing outside of HR with customer data, Twitter data, really what I would call ‘big data,’ it may seem a lot more appetising and appealing than HR analytics. If I was a PhD student, I am not sure I would consider HR as a place to go to develop my career and also, whether I would see any longevity in it. As a function we need to change that."
For organisations like CCE, natural progression in analytics is towards mature data processes that utilise the predictive value of HR and business data. For most organisations this can too often remain an objective that exists in the far future, and one which without significant investment may never be realised. Alongside the resource challenges in building capability there also exists the need to understand exactly how data may provide value, and the importance of objective and critical assessment as to how data can be exploited. Without appreciation for methodological challenges, data complexity and nuances in analysis, it may be that organisations use data without fully understanding the exact story the data is telling.
"Predictive analytics is difficult. We are very much in the early stages as we are only starting to explore what predictive analytics might enable us to do, and what insights it could enable us to have. If we can develop some success stories, it will grow. If we go down this route and start to look at some predictive analytics and actually, there is not the appetite in the business, or they do not believe it is the right thing to do, it might not take off.
If you think about the 2020 workplace, the issues that we have around leadership development, multi-generational workforces, people not staying with companies for as long as they have done in the past, there are a lot of challenges out there for HR. These are all areas where the use of HR analytics can provide the business with valuable insights.’"
For CCE it appears that analytics and HR insight are gaining significant traction within the organisation. Leaders are engaging at all levels and the HR function is increasingly sharing insights across business boundaries. This hasn't been without its challenges: CCE face HR's perennial issues of technology and the perceived lack of analytics capability. However their approach of creating quality data sets and automated reporting processes has provided them with the foundations and opportunity to begin to develop real centres of expertise capable of providing high quality insight to the organisation. It is clear CCE remains focused on continuing its HR analytical journey.
"It's a great opportunity for HR, and we should not pass up on it, because, if executed well, HR analytics combined with business data allows us to highlight the impact of people on business outcomes.
It’s about small steps, pilots, where you start to demonstrate the power of combining HR and business data. If you understand the business problems and can come to the table with insights that had previously not been seen you enhance HR’s credibility and demonstrate the value we can add as a function.
What amazes me as a HR professional, with a lean six sigma background, is that companies are often great at measuring and controlling business processes but very rarely consider the importance of people in that process. People are without doubt one of the most important variables in the equation."
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Data is a hot commodity in today’s marketplace. While digital tools generate a vast amount of readily available information, data holds little value in its raw form. That’s where HR analytics comes in – transforming data into insights for resolving workforce and business challenges.
1. hr analytics in recruitment at google.
HR tip If you’d like to read more about how data can change hiring practices, we recommend Laszlo Bock’s book ‘Work Rules’ . Laszlo Bock was the senior VP of People Operations at Google and describes in more detail how hiring practices changed at Google after analyzing recruitment data.
3. hr analytics in absenteeism at e.on.
2. data selection.
4. data analysis, 5. actionable insights, how to transition from descriptive to predictive and prescriptive analytics in hr.
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Apply Your Skills with a Power BI Case Study. In this Power BI case study, you will be exploring a dataset for a fictitious software company called Atlas Labs. This course focuses on helping you import, analyze and visualize Human Resources data in Power BI. Building on your existing knowledge of the platform, you'll learn how to effectively ...
Ingredients for success. Our conversations with people analytics teams in leading organizations reveal a set of six best-in-class ingredients that have helped to propel the teams' impact, success, and continued growth. These ingredients fall into three main categories: data and data management, analytics capabilities, and operating models.
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15 HR Analytics Case Studies with Business Impact. Analytics in HR. NOVEMBER 5, 2018. It is time that HR analytics starts to show the value it delivers to the business. This is hard to do as people analytics is still an emerging field. For this article, I have collected 15 of the best HR analytics case studies I've come across in the past two years. 15 HR Analytics Case Studies.
Case Study: Implementing HR Analytics in a Multinational Corporation. Overview of the Company. ABC Corporation, a global leader in the tech industry, faced challenges in talent acquisition ...
More HR Analytics resources 16 Video: 5 (More) HR Analytics Case Studies In this 30-minute webinar, you will see HR Analytics in action at 5 different organizations from around the world, from entry-level analytics to more advanced projects. Cheat Sheet: 51 HR Metrics Your handy quick reference guide for the 4 major HR metrics
Valuing people. This case study provides insight into Coca-Cola Enterprises' (CCE) data analytics journey. Given the complexity of the CCE operation, its global footprint and various business units, a team was needed to provide a centralised HR reporting and analytics service to the business. This led to the formation of a HR analytics team ...
In this case study, we will explore Human Resources data for a fictitious software company called Atlas Labs. The primary goal of this case study is to monitor key HR metrics on employees.
Abstract. The use of data analytics in the field of human resource development is becoming increasingly common. This rise in popularity is accompanied by skepticism about the ability of human resource professionals to effectively utilize data analytics to reap organizational benefits. This article provides a review of literature both supportive ...
For more real-world HR analytics examples, you can refer to the case studies we published in the past. Here are links to three of them: Case Study 1: Key Drivers of Retail Sales Performance; Case Study 2: Reducing Workplace Accidents Using People Analytics; Case Study 3: How We Determined Optimal Staffing Levels
Curious how People Analytics can add value to your organization? 📈These 5 case studies demonstrate just how impactful HR Analytics can be.David Millner (HR ...
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Analyzing HR Data in Tableau In this Tableau case study, you will explore a dataset for a fictitious software company called Atlas Labs. This course focuses on helping you import, analyze and visualize Human Resources data in Tableau. Building on your existing knowledge of the platform, you'll learn how to effectively work with Tableau using ...
Understand the factors influencing employee attrition and job satisfaction.