How to start data-led talent transformation

Call center workers found to be creative in surveys conducted by Xerox, a printing firm, stayed around longer with it than those who asked too many questions. As did active social media users. 

Such data-backed predictions on employees helped the firm, which was battling soaring turnover costs, cut the attrition rate by 20%. Xerox integrated the findings from the surveys into its recruitment process, wherein it focused on hiring creative candidates.  

Personality over experience mattered more, Xerox learnt, through meticulous data analysis.  

With such insights now part of the hiring process, candidates get to choose between statements like: “I ask more questions than most people do” (inquisitive) and “People tend to trust what I say.” The firm easily finds its fit, and employees are happier. 

Overcoming limitations of intuition and bias – a traditional HR paradigm- firms today are undertaking data-driven talent transformation.  

Business advisor Bernard Marr explains, “companies are turning their data into insights.” They now predict when employees will leave, where to recruit and how to keep them happy.

Intelligent HR is a people analyst

We are transitioning from business analysis to people analysis. In today’s competitive hunt for talent, the imperative is to create “intelligent HR” in organizations.  

This means climbing over the proverbial ‘Wall of Boudreau’ – which separates unskilled managers, who rely primarily on simplistic scorecards,  to those who undertake advanced predictive analysis, as in the Xerox case. 

And solely relying on intuition can impair organizational growth, claims Harvard Business Review. This may lead to overconfidence bias, the availability heuristic (relying more on recent information) and status quo bias. 

Human capital decisions will increasingly become a combination of art (human intuition and experience) and science (the intelligence of data analytics).

– Jean Paul Isson & Jesse S. Harriott in People Analytics in the Era of Big Data.

Sounds familiar? The intuition-led marketing domain underwent a revolution in the 1990s, fueled by data. Similarly, people analytics today is crystalizing the gut-feeling of HRs with numbers. 

People analytics is a data-driven approach to managing people at work, according to Gal, Jensen & Stein. And it helps in aligning the HR policy with the firm’s strategic objectives to maximize profits. 

Take the case of Credit Suisse, which was able to predict who might quit the company based on behavioral patterns, as The Wall Street Journal reported. It could not just retain talent by mitigating against attrition risks but saved around $ 70,000,000 a year. 

Bag of virtues

Common sense can sometimes be our enemy, explain Jenny Dearborn and David Swanson in The Data Driven Leader. “Data, however, can remove guesswork, biases, anecdotal reasoning, and other human foibles that can throw strategic efforts off course.” 

Illustrating its utility, McKinsey found people analytics increased recruiting efficiency by 80% and business productivity by 25% of firms. So, its scope is wide, and benefits many: 

Hire faster, better 

First demonstrated on a large scale by Google, data analytics can help structure interviews better, remove bias from them and even predict candidates’ future performance. Employee diversity can get a shot in the arm too. 

Transform work culture 

Harvard Business Review considers data storytelling a lightweight way to building trust among stakeholders and bring behavioral science to cultural transformation. Data insights can foster better work ethic and team work. 

Boost retention rates 

In the “war for talent”, many not-so-apparent factors may nudge employees to switch over. A predictive model can help lower attrition risk by 5 to 8% annually, Neeraj Tandon, a workforce analyst, told a magazine. 

Bridge skill gap 

Using data to identify who to, when to, why to and how to train has boosted performance of companies. For instance, a Dutch FMCG retailer used analytics to gauge skill development’s impact on business, and clocked a 400% return-on-investment on their training program. 

Trump crisis 

Numbers can help predict, avert and manage a crisis too. A prescriptive analysis aided Unilever in 2017 to successfully battle a hostile takeover. The firm tracked attitudes of employees and drew up ways to reduce costs to ward off the bid. 

Types of analytics 

What do data have to offer to transform talent? Organizations can leverage different levels of analytics based on questions they seek to answer: 

Descriptive – What has happened 

Example: Rate of job applications in a firm has declined 

Diagnostic – Why did it happen 

• The rate declined owing to skills mismatch 

Predictive – What could happen 

• The rate may decrease further

Prescriptive – How to achieve goals 

• Diversify job board to attract talent and increase application rate 

Know your HR Analytics Capability

HR analytics should focus less on measuring HR and more on creating business results or value from human capability initiatives.

– Dave Ulrich, Co-Founder & Principal at the RBL Group 

Vouching for an impact-based system, Ulrich believes human capability initiatives should be prioritized based on their possible effect on stakeholders such as customers and investors.  Thus, analytics focus shifts from “activity” to “impact”.  

Along with Thomas Rasmussen, Vice President HR Data & Analytics, Shell plc, and others, Ulrich has identified ten actions to implement analytics successfully. The actions can also be taken as criteria for assessing analytics capability.  

Assess your capability based on each question below on a scale of 1(low)  to 10 (high).  

How people data-friendly are you? 

The Bersin Talent Analytics Maturity Model lets organizations gauge their current level of analytics capabilities, and plan for future upgrades. Check for yourself: 

Level 1: Operational reporting 

A must-have for HR hygiene, this involves describing the current situation and includes data such as salary and training costs. It is of relatively less value to the organization. 

Level 2: Advanced reporting 

This approach uses the same data as operational reporting but to track trends and progress towards goals. 

Level 3: Advanced analytics 

This is valuable to the organization as it involves problem-solving using analytics 

Level 4: Predictive analytics 

The approach helps in simulating possible future situations and responses to them using machine learning methods. This can aid informed decision-making and long-term planning. 

While the majority of the firms remain in Level 1 and 2, the higher levels can be achieved. Isson and Harriott moot a talent analytics center of excellence by investing in people (the analytics resources), processes (the IMPACT Cycle) and technology (to manage, master, analyze, and leverage talent data) 

Stages of digital transformation 

According to the Deloitte 2023 Global Human Capital Trends, technologies are not just a substitute or supplement for workers but “actually help them improve who they are—enabling humans to become better humans and teams to become better teams”.  

Further, another report suggests leadership is nudging HR to lead digital transformation across organizations. They are helping organizations “be digital” and not just “do digital”.  

Here are the steps of the HR digital transformation journey based on research by Brian Solis, Head of Global Innovation, ServiceNow:  

1. Business as usual 
2. Present and active Formalized 
3. Strategic 
4. Converged 
5. Innovative and adaptive 

People Analytics or talent analytics is another type of business analytics that is obtained by simply replacing the word customer from business analytics with the word employee or talent.

– Jean Paul Isson & Jesse S. Harriott in People Analytics in the Era of Big Data.

According to an article by Pascal Occean, Michael Stephan and Brett Walsh, you can start your HR digital transformation journey by keeping the following things in mind:  

1. Redefine your mission 
2. Upgrade core technology 
3. Develop a multiyear HR technology strategy 
4. Build a digital HR team 
5. Organize HR into networks of expertise with strong business partners:  
6. Make innovation a core strategy within HR 
7. Rotate younger people into the HR profession
8. Benchmark 

How to use data analytics for HR: IMPACT cycle 

Coined in ‘Win with Advanced Business Analytics’, the cycle presents steps to leverage data for talent management. Let’s dive in: 

Step 1: Identify questions you want to answer 

 When to hire employees for a particular role? How to diversify the workforce by demographics and skills? – posing clear questions, aligned with organizational goals, is the first step. Build a hypothesis on a problem that data can help resolve better.   

Step 2: Master the data 

Collect, process and analyze data and present them in visual representations such as charts and graphs. 

Step 3: Provide the meaning 

Interpret numbers to identify patterns and present them clearly. 

Step 4: Act on the findings and recommendations 

Recommend course of action based on interpretations. “Where possible, tie a rough dollar figure to any revenue improvements or cost savings associated with your recommendations,” say the authors. 

Step 5: Communicate insights 

Spread your insights in the organization horizontally and vertically to pique interest and action. 

Step 6: Track outcomes 

Set up a mechanism to track action on insights. This can help locate newer areas of inquiry. 

How to integrate company vision in data-led talent management  

An “integrated HR” ensures all people’s decisions, including talent transformation using data, are guided by the company’s strategic vision. Here’s a way to integrate your vision in the digital transformation:  

1. Enable quarterly or monthly reporting: You must gauge investment and outcomes of your data initiatives and report them to the leadership on a regular basis. The leadership can then ensure the initiatives are in line with the vision.  
2. Incremental integration: Don’t overwhelm the process by integrating the vision as it is. Try to see where your initiatives fit the vision and what elements of it suit it the best. You must be clear on how your initiatives will further the march to achieving them.
3. Measuring culture and fine tune it: Using people analytics, you can measure opinions, behaviors, belongingness and sentiment, which inform the work culture. This can reveal cultural cues to you, and you may analyze them to see if they are in line with the vision. 

According to a Harvard Business Review article, data can help meet your diversity, equity and inclusion goals. Organizations looking to make the best use of their diversity data must:  

1) Present it in a way that’s simple, salient, and comparable;  

2) Leverage it to empower the right people to act;  

3) Use it to set goals that create accountability and increase follow-through; and  

4) Leverage it to shift social norms around DEI 

Seven-Pillar Framework of People Analytics Success 

To drive talent management through data, the Seven Pillars of People Analytics Success, created by Isson and Harriott, can be deployed: 

#1. Workforce Planning Analytics 

  This pillar assigns an economic value to HR data. Define the company’s mission and its goals. Then, deploy analytics to plan the “right number of employees with the right skill sets, at the right place, at the right time, and at the optimal cost”. 

Harvard Business Review has covered how Black Hills Corporation, an energy conglomerate, dealt with the predictions of losing employees by leveraging data to identify retiring employees, replacement needs and talent pools. 

  #2. Sourcing Analytics 

  This involves creating sourcing pipelines relevant to the company’s needs. It helps in optimizing candidate search results through diverse channels and reducing time and costs. For instance, Cisco expanded in newer regions by first mapping the availability of talent there. 

  #3. Acquisition/Hiring Analytics 

  Around 80% employee turnover is due to bad hiring decisions, claims Harvard Business Review. This pillar involves drawing up scorecards to filter candidates and picking the best for interviews. It aids in developing pre- employment skills tests that could simulate work situations. 

  #4. Onboarding, Culture fit and Engagement Talent Onboarding and Culture Fit 

 Analytics here help instill the organizational values in the new employees more conveniently, and ensure their timely absorption in the company culture. 

Talent engagement 

Data insights, revealed majorly from pulse surveys, can produce a productive and happier workforce through strategic engagements. This also facilitates drawing up correlations, such as linking engagement to employee performance. 

For example, shoe retailer Clarks noted 1% improved engagement yielded 0.4% improved business. 

 #5. Performance Assessment and Development and Employee Lifetime Value 

Performance analytics 

This seeks to answer questions like which employees should be promoted, will their performance improve as a result and should firms hire internally or externally. For example, General Electric (GE) launched an app called PD@GE, wherein employees gave or requested feedback anytime across the hierarchy, reported Quartz. 

Employee lifetime value analytics 

 Analytics here employ a customer lifetime value model for employees. This can aid retention and create successful careers. 

  #6. Employee Churn and Retention 

This involves answering attrition questions using data from internal and external sources to retain and develop the best talent. It seeks to identify employees at risk of leaving, reasons for attrition and ways to curb them. 

 #7. Employee wellness, health and safety 

The pillar seeks to study the link between employee satisfaction with performance and profits, as well as customer satisfaction. It analyzes investment in health on the employee’s productivity and retention.  

Mining firm Goldcorp used analytics to find patterns of work incidents involving employees. It found factors such as marital status, month of the year, compensation structure as determinants, and was able to curb incidents. 

Ingredients of a successful data-driven model 

 Today, CEOs are increasingly seeking business leaders as CHROs for their business acumen especially in using analytics to improve performance, claim Dearborn and Swanson. Thus, traditional HR domain knowledge merely may not suffice. 

Foster a data-driven culture at work 

  “You cannot force people to embrace a data-driven decision framework,” according to Forbes. HR professionals must leverage data in work to inspire replication by others. Further, demonstrating the utility of data analytics to frontline teams can secure their attention, and subsequent adoption. 

Define metrics 

  These include time to hire, acceptance rate, turnover rate, diversity and productivity among others. Pick metrics keeping in mind data collection methods available, comparisons, relevance to hypothesis and validity. 

Join hands 

  Build cross-functional teams. Abandon a siloed HR management approach and join hands with finance and research departments. “People analytics is an overlap of HRM, finance, and data analytics,” says Erik van Vulpen, an HR analytics expert. “This means that organizations need varied skillsets in order to implement people analytics.” 

HA-OOH! Let’s conquer the roadblocks 

75% respondents cite talent analytics as important, but just 8 % believe their organization is “strong” in this area

– Deloitte Survey 

Infrastructural deficiencies, lack of data governance and tardy exploitation of data are major roadblocks, according to the World Economic Forum. So, a transformation becomes vital. 

Privacy and data protection 

  People analytics can be construed as invasive and unwarranted if undertaken arbitrarily. The Chartered Institute of Personnel and Development advises employers to be transparent with employees, hold consultations and avoid discrimination. 

Goodbye, feelings? 

 This may broach the 19th century debate between positivists, who claimed humans could be studied in an action-reaction framework like animals, and idealists who foregrounded human consciousness and feelings. People analytics today represents a blend- aiding intuition with evidence. 

Yawning skill-gap 

  Boudreau says the HR gets ‘stuck’ and continues to use traditional methods of analysis owing to lack of skills. Besides, expertise of employees with a background in sociology and psychology, disciplines rooted in quantitative analysis, is rarely leveraged. Dearborn and Swanson advise hiring analysts with expertise in business, technology and human resources. 

Up your analytics game: Study interactions 

 While current analytical models study attributes of employees, their interactions within the organization are also “telling”, suggest Paul Leonardi and Noshir Contractor in Harvard Business Review.  

They cite research which shows that success of many employees is based on their relationships, and studying these relationships constitutes a discipline called relational analytics.  

 The key is finding “structural signatures”: patterns in social networks that predict who will have good ideas, which employees have the most influence (it’s not senior leaders), which teams will be efficient, which will innovate best, where silos exist, and which employees firms can’t afford to lose.

– Paul Leonardi and Noshir Contractor in Harvard Business Review 

The authors point out that the raw material for relational analytics already exists in companies. This essentially includes data trail and the digital footprint of employees. “e-mail exchanges, chats, and file transfers—the digital exhaust of a company. By mining it, firms can build good relational analytics models,” they write.  

To effectively deploy relational analytics, the authors say:  

• Passive collection is easier on employees – Relying on the digital exhaust doesn’t affect productivity of employees. Yet, companies collecting information must clarify policies on data collection to employees.  
• Behavioral data is a better reflection of reality – Insights based on digital exhaust may be more valid than surveys. “In surveys people may list connections they think they’re supposed to interact with, rather than those they actually do interact with,” the authors highlight.  
• Constant updating is required – this is required as relationships change over time
• Analyses need to be close to decision makers – relying entirely on data scientists may create roadblocks as they may not know the employee base under study or the context of the analysis
• Dashboards are key – “A system that identifies structural signatures and highlights them visually moves analytic insights closer to the managers who need them,” the authors write.  

 The future is here, are you ready? 

Factors contributing to emergent trends in HR technology:

1. Use of newer ways to quantify human beings
2. Shortage of adequately skilled labor
3. Long working hours and human-machine interface issues.

– Senior Director at Hitachi Group.

  Today, smart systems, AI and industry 4.0 are disrupting traditional HRM, paving the way for newer means of conducting business and managing employees, observe Shivinder Nijjer and Sahil Raj in Predictive Analytics in Human Resource Management. 

Take the case of Feedo’s Amber chat box, powered by AI, which uses predictive analytical tools to identify disengaged employees in a firm. Its outcomes are interpreted by a team of psychologists, statisticians and HR specialists, who recommend action. Such is its precision that employees of an e-commerce firm believed Amber to be a real person! 

Yet, people analytics is not entirely new. In 1900s, Ford applied Taylor’s principles of scientific management to workers to increase efficiency- bringing smaller parts to stationery large ones for car assembly, a method still followed today. And Jac Fitz-enz in 1978 gave us the first HR metrics. 

But today, the pandemic-induced digitization, growing inequalities and dearth of skilled labor have necessitated faster adoption of data-led approaches. Even the World Economic Forum’s six people strategies for the fourth industrial revolution underscores the thrust required on people data. 

Begin your data-led HR journey  

As an integrated platform, Keka allows HR specialists to keep pace with a competitive talent market and undertake data-led talent management throughout the lifecycle of employees. For instance, the 360-degree feedback system enables performance evaluation by all the managers or groups of co- workers. Additionally, the OKR (Objectives and Key Results) solution allows you to track performance at all levels – company, team and individual. Claim your free trial here.  

If not for the fear of being left behind, staying relevant in the market today and boosting profits begs a data-led approach to workers. Sure, you might be tempted to don the role of Hard Times’s Thomas Gradgrind: choosing fact over fancy, and numbering – not naming – people. But today a synthesis is needed, of empiricism and emotions. 

Crude facts will only produce workers, not individuals. As Nijjer and Raj point out: Humans are the only unique resources an organization can have today. All others can be replicated.