Unlocking the Power of People Analytics: Uses, Processes and Tools
Without data, you're just another person with an opinion.
– W. Edwards Deming.
As competition intensifies across industries, companies see people analytics as key to strategic talent management.
As workforces and jobs evolve rapidly, data-driven talent management becomes crucial for organizations. This emerging field applies data, statistics, and machine learning to uncover powerful insights about the workforce.
Building capabilities takes a concerted effort over time regarding skills, technology, and processes. This blog explores the immense potential of people analytics and how to develop this capability.
- Concept of People Analytics and Its Importance
- The Process of People Analytics
- People Analytics Use Cases and Its Impact on Businesses
- Key Trends in People Analytics
- How to Develop Core People Analytics Capabilities
- Top People Analytics Tools and How to Choose Them
What is People Analytics?
People analytics refers to the practice of collecting and analyzing people-related data to gain insights that can drive business outcomes. It involves leveraging HR data, organizational data, and advanced analytics techniques to optimize talent management decisions and processes.
The key objectives of people analytics are to:
- Gain a deeper understanding of the workforce and how it impacts the business
- Identify patterns and correlations between people data and business performance
- Forecast and predict outcomes related to the workforce
- Guide evidence-based talent decisions instead of relying solely on instinct
Although the terms People analytics, HR analytics, and Talent analytics are used synonymously in business, there are some key differences between them.
- People analytics has the widest scope, looking at all people data. HR analytics looks only at HR data. Talent analytics focuses specifically on talent data.
- People analytics aligns with overall business goals and outcomes. HR analytics focuses on optimizing HR processes. Talent analytics hones in on talent management.
- People analytics leverages advanced analytics on a range of data. HR and talent analytics rely more on metrics and reporting.
Both small and big organizations can use people analytics to manage their workforce and improve their levels of employee engagement. Companies that look for better ROI will significantly benefit from people analytics in the long run.
The Importance of People Analytics
People analytics leverages advanced analytics on workforce data to provide powerful insights that enable smarter talent management decisions and improved business performance.
- It strengthens competitive advantage by driving workforce agility, optimizing talent, and enabling data-driven decisions that reduce bias and boost productivity.
- Additionally, people analytics measures HR initiative effectiveness, uncovers workforce patterns and trends and predicts future scenarios.
Ultimately, it unlocks valuable workforce insights for data-driven organizations seeking to maximize human capital potential.
While people analytics adoption across organizations is accelerating, most organizations remain in the early stages of maturity.
According to SHRM research, 71% of HR executives view people analytics as essential, yet only 58% of HR professionals see signs of a widespread data-driven culture.
Additionally, most companies are still focused on basic analytics, such as analyzing past data. Only 19% of the companies utilize it for causal modeling and a mere 4% leverage predictive analytics. This indicates significant room for advancement to more sophisticated applications.
Further barriers include limited dedicated staff, with only 30% of companies having an established team or employee for people analytics. Hiring data scientists without the proper data infrastructure and governance is also proving challenging.
Though common early use cases revolve around turnover, hiring, and compensation, interest is growing in more advanced analytics for DEI, workforce planning, and beyond.
Companies that can develop analytics skills, combine data, and use analytics responsibly will gain a major competitive edge. But most have taken the baby steps but need to invest more in this field. Prioritizing the fundamentals now will enable more data-driven talent decisions in the future.
The People Analytics Process
The people analytics process is an ongoing cycle of interconnected steps that enables organizations to unlock deep insights about their workforce from HR data. This, in turn, powers more strategic, evidence-based talent management.
1: Identify Business Problems
The first critical step is pinpointing key talent issues or opportunities that people analytics can highlight. For example,
- Questions around improving retention and engagement,
- Reducing turnover,
- Enhancing performance,
- Optimizing learning programs,
- Increasing quality of hire, and more.
Defining the right business questions to solve is crucial.
2: Collect and Prepare Data
The next thing is to collect relevant HR and people data from secure sources. It can include-
- HRIS (Human Resources Information System),
- LMS (Learning Management System), and
- ATS (Applicant Tracking System).
In addition, related organizational data (like sales or budgeting) and external data can provide a broader context. The data then needs extensive preparation. It must be cleaned, standardized, and organized so it is tidy and consistent enough to analyze. Data preparation is a complex but foundational step.
3: Analyze Data
The prepared data set can now be analyzed using various techniques, including -
- Machine learning,
- Predictive analytics, and
- Data visualization.
Data scientists apply their tools and skills to uncover trends, correlations, patterns, and insights buried within the data. Multiple techniques are often combined for a thorough examination that breaks down the complex data and makes it easier to understand.
4: Interpret and Storytell
Meaningful conclusions and insights must be drawn from the analytical output. Data scientists must interpret the data analysis and effectively translate the key insights into an engaging story for leadership and stakeholders. Compelling data visualization and presentation is crucial to bringing numbers to life.
5: Drive Action
After seeing the workforce insights from people analytics, stakeholders decide what actions to take. This could mean changing HR policies, processes, programs, systems, or strategies. The insights help them make impactful decisions to manage talent better.
6: Measure Impact
Diligently quantifying the business impact of actions driven by people analytics is critical. This demonstrates the tangible ROI, value, and benefits people analytics generate. It also highlights opportunities to refine approaches to make them more impactful within the organization.
7: Continuously Iterate
Repeating this process rapidly with new questions and data enables people analytics to provide a constant pulse on the workforce. Evidence-based management empowers leaders with continuously updated insights to optimize human capital and talent outcomes.
What Amount of HR Data is Required to Begin People Analytics?
To get started with the people analytics process, you would require some foundational data points. With the help of these data points, you will be able to perform basic workforce analysis to address common HR-related questions, such as turnover rates, workforce diversity, compensation equity, and departmental performance.
Let’s have a look at the data points below.
The Employee ID serves as a unique digital fingerprint for each employee in your organization. It allows you to tie all other data points to a specific individual by tracking and analyzing individual performance, tenure, and history within the organization.
Employee demographics encompass essential information like age and gender. Understanding the age distribution of your workforce helps in planning for generational differences, while gender data is vital for tracking diversity and inclusion efforts. This data provides insights into your workforce's composition and potential areas for targeted HR strategies.
An employee's job title or position defines their role and responsibilities within the organization. This data point enables you to categorize employees by their functions, which is fundamental for tasks such as comparing performance metrics across different job roles or analyzing department-specific trends.
An employee's onboarding or hire date marks the beginning of their journey with your organization. This key data enables you to calculate tenure accurately, which is crucial for understanding employee loyalty, career progression, and potential retention strategies. It also helps in identifying long-term contributors to the organization.
Information about an employee's compensation, including base salary or hourly wage, is essential for various HR analyses. This data helps you assess pay equity, identify compensation disparities, and ensure that employees are fairly compensated for their roles and contributions.
The department or team to which an employee belongs provides insight into the organizational structure. It aids in analyzing team performance, departmental dynamics, and resource allocation. Knowing an employee's department also helps workforce planning and optimizing team composition.
The physical location or office where employees work can be vital for various purposes, especially in geographically dispersed organizations. Location data helps HR teams manage remote work arrangements, assess regional labor markets, and address location-specific challenges or opportunities.
These seven data points lay the foundation for effective People Analytics. By collecting and analyzing these details, organizations can gain valuable insights into their workforce, make data-driven decisions, and formulate HR strategies that align with their business objectives.
People Analytics Use Cases and Impact
Let's explore some People Analytics use cases and their real-world impact on specific HR processes:
Use Case: Predictive Analytics for Hiring
Impact: Predictive analytics uses historical hiring data and candidate attributes to forecast which candidates are most likely to succeed in a particular role. This method enables HR teams to focus their efforts on candidates with a higher likelihood of success, reducing recruitment time and costs while improving the quality of hires. It also aids in identifying biases in the hiring process.
Example: Google's exhaustive interview process once involved up to 25 rounds for each candidate. To hire 1000 new employees took a staggering workforce of 125 full-time recruiters. This marathon recruitment consumed months of time and resources.
Google's analytics team used predictive analysis to take a closer look at the data. They discovered a crucial insight - after just 4 rounds of interviews, they could predict a candidate's viability with 86% accuracy.
Armed with these findings, Google streamlined its hiring. The median time to extend an offer plunged from 180 days to 47, a 75% improvement. By optimizing the process, Google sharply reduced the recruiter workload and hiring duration.
Use Case: Turnover Prediction Models
Impact: Turnover prediction models use data analysis to identify employees at risk of leaving the company. This insight allows HR departments to proactively retain valuable talent, such as adjusting compensation, offering career development opportunities, or addressing workplace issues. It reduces the cost and disruption associated with employee turnover.
Example: HP had a problem of replacing mid-level employees that cost upwards of 150% of their annual salary. They decided to solve this problem by using a prediction model. They generated what they called a “Flight Risk” score that predicted the likelihood of leaving each of HP’s 300,000 plus employees.
The Flight Risk scores worked like an early warning system. They alerted managers to act before employees quit, and helped them plan ahead. HP saved about $300 million by using these predictive scores to identify flight risks.
Use Case: Performance Metrics and Feedback Analysis
Impact: Analyzing performance metrics and feedback data helps organizations identify strengths and weaknesses in their performance management processes. It shows which performance measures matter most. It helps make performance reviews fair and objective. It also allows continuous improvement in how employees perform.
Example: Adobe's old system rated employees on a bell curve, which often demotivated employees and hindered collaboration. They moved to a system called "Check-ins," where managers and employees have ongoing dialogues and real-time feedback rather than just an annual review.
Check-ins are focused on goals and growth. It helped employees get continuous coaching rather than just criticism during reviews. The process is lightweight and flexible. Check-ins can happen in under 15 minutes, unlike laborious review meetings.
With check-ins, Adobe saw much higher engagement, productivity, and retention of top talent.
Use Case: Employee Surveys and Sentiment Analysis
Impact: Employee surveys and sentiment analysis help gauge the workforce's overall engagement and satisfaction levels. Organizations can take targeted actions to improve workplace culture, enhance employee experience, and boost retention rates by identifying areas where employees may be dissatisfied or disengaged.
Example: Australian law firm Lander and Rogers leveraged employee sentiment analysis software to achieve key HR metrics and promote a productive working-from-home culture. Amid the COVID-19 pandemic, the firm prioritized maintaining a positive employee experience and safeguarding mental health and well-being.
They utilized employee pulse surveys to gather regular feedback from team members, inquiring about their experiences coping with pandemic-related stressors and the challenges they encountered while working remotely. By analyzing this employee sentiment data, the leadership team at Lander and Rogers gained valuable insights into the workforce's emotional well-being and work-from-home challenges.
Learning and Development
Use Case: Skill Gap Analysis
Impact: Skill gap analysis involves comparing employees' skills with those required for their roles. It helps organizations identify skill deficiencies and design training programs to bridge these gaps. This approach ensures that employees have the skills needed to perform effectively in their jobs and supports talent development.
Example: Using the skill gap analysis, Amazon is working to close the skills gap by providing on-the-job learning opportunities and apprenticeships to train employees for technical roles like robotics and manufacturing. This has helped Amazon build a skilled workforce for current and future jobs.
They also offer a Reliability and Maintenance Engineering Mechatronics and Robotics Apprenticeship program. This involves classroom learning, on-the-floor training, and job placement in robotics. With this kinds of training, Amazon has created many new technical jobs. It also allowed employees to move into higher-paying roles and pursue new career paths.
Diversity and Inclusion
Use Case: Diversity Metrics and Inclusion Initiatives
Impact: Tracking diversity metrics, such as gender and ethnicity representation at various levels of the organization, allows companies to measure progress toward diversity and inclusion goals. It helps set specific targets, promote diverse hiring practices, and foster an inclusive workplace culture, ultimately enhancing the organization's reputation and competitiveness.
Example: Sodexo Americas has used a Diversity and Inclusion (D&I) scorecard since 2002 to measure and enhance its commitment to diversity. This scorecard undergoes continuous refinement. It comprises both quantitative and qualitative components. The quantitative part evaluates metrics related to the hiring, retaining, and promoting women and minority employees.
Additionally, the qualitative part assesses aspects like mentorship programs and support for employee resource groups. The scorecard is regularly reviewed and reported monthly. Importantly, it plays a significant role in determining bonuses within the company, with 15% of executive bonuses and between 10% and 15% of manager bonuses linked to D&I performance.
In essence, Sodexo uses its scorecard to track diversity metrics. This helps them evaluate and reward efforts to improve diversity and inclusion.
Key Trends in People Analytics
Increased Focus on Diversity, Equity, and Inclusion (DEI) Analytics
Organizations today recognize the critical importance of cultivating diverse, equitable, and inclusive workplaces. However, good intentions must be paired with evidence-based action. This is fueling a major increase in the use of people analytics to drill into the data around current DEI across talent processes.
Companies can analyze representation data, promotion rates, attrition patterns, pay equity, etc. These insights reveal where progress is falling short and enable targeted interventions, like refining hiring practices in certain areas to reach more diverse candidates.
People analytics provides granular diagnostics and ongoing tracking to turn promises into measurable improvements in DEI.
Storytelling and Visualization with People Data
People analytics teams are expanding their skills from compiling workforce metrics to crafting compelling data stories. Using principles of data visualization, HR analytics specialists can make data insights intuitive and impactful for business leaders. This goes beyond presenting tables of numbers.
For example, an interactive attrition map can spotlight which departments or roles are hemorrhaging talent.
Powerful visuals tap into our inherent cognitive capabilities. At the same time, HR teams must hone their business storytelling abilities to contextualize data. The combination of expressive visuals and resonant storytelling makes people analytics insights more memorable and engaging.
Focus on Ethical Use and Privacy
As people analytics gains sophistication, ethics and privacy considerations become paramount. Companies must earn trust that employee data will be handled responsibly. This means implementing strong data access controls, transparency on how data is used, and robust policies to prevent misuse.
Analytics teams should follow privacy and ethics by design principles. For example, aggregating data and limiting access to individual identities wherever appropriate. With tools like anonymization, data can be studied at group levels without exposing private details.
Ongoing employee communications about analytics practices and obtaining consent for new projects also help maintain trust. People analytics delivers immense organizational value only if anchored in true data ethics.
Automating Predictive Modeling
Sophisticated predictive analytics has traditionally required hard-to-find data science expertise. However, AI and machine learning advances are democratizing predictive capabilities for people analytics. New tools can automate building complex models that predict outcomes like future attrition risk, high-potential employees, or top sales performers.
HR provides relevant employee data, and the computer algorithms experiment with various methods and models to make customized, accurate predictions. This makes exploratory predictive analytics accessible without coding or statistics knowledge. It allows HR to validate model fairness and compare scenarios.
How to Develop Core People Analytics Capabilities
A mature people analytics function requires a combination of hard data and soft business skills. HR teams aiming to elevate their analytics prowess should focus on building capabilities in the following areas:
1. Data Skills: SQL, Python, Data Modeling, Statistical Analysis
The foundation of people analytics is working with data. Practitioners need fluency in essential data skills like -
- SQL queries to extract data from databases,
- Python for automation and analysis,
- Statistical methods to correctly interpret results, and
- Data modeling to properly structure and integrate different datasets.
These technical abilities let analysts access, prepare, and analyze employee data to derive insights. Ongoing development of core data science skills ensures accuracy and allows more advanced analytics.
2. Technology Skills: Familiarity with Analytics Tools and HRIS Systems
Along with data skills, people analysts should be conversant in key technologies like analytics platforms and HR Information Systems. Understanding how these tools work and their capabilities allows analysts to make the most of available technology.
For example, knowing how to leverage built-in reporting dashboards and visualization features or properly configure SaaS analytics systems. Keeping up with the latest HR tech developments helps identify opportunities to streamline analytics processes.
3. Soft Skills
People analytics professionals must tailor communication of findings to each audience and deliver impactful data stories that influence. Analysts serve as ambassadors between data and organizational stakeholders. Managing those relationships via excellent communication, collaboration, and consulting skills makes the adoption of analytics programs smoother.
4. Building an Interdisciplinary Team
The most effective analytics teams blend technical data acumen and business acumen. This means team members with backgrounds spanning IT, data science, statistics, psychology, and business domains.
Diversity of perspective strengthens the analytical process and allows rigorous data investigation and nuanced interpretation of social and organizational factors. The synergy of hard data and soft business skills on cross-functional teams produces the richest insights.
Choosing The Right People Analytics Tool
Selecting the optimal people analytics tool is crucial for building a highly functional analytics program. The right tools remove data access barriers, automate reporting, and provide advanced analytics capabilities to unlock workforce insights.
When evaluating options, organizations should keep several factors in mind:
First, determine the must-have features to address the organization's pain points and analytics needs. This may include data integration with HR systems, self-service dashboards, predictive modeling, or workflow automation. Defining these needs focuses the search on tools that will deliver the most value.
With needs defined, compare vendor offerings to find the best feature match. Key aspects include data connectivity, visualization options, predictive analytics, data security controls, and customization flexibility. Look for platforms robust enough for both current and future analytics ambitions.
Assess Ease of Adoption
The tool's interface and ease of use matter for user acceptance. Platforms with intuitive navigation that require minimal training lower barriers to adoption across the organization. Complex systems with steep learning curves often fail to gain traction.
People analytics technology is an investment that should demonstrate clear ROI through increased productivity, better decisions, and other impacts. Calculate the expected dollar value gained from key features like automation and benchmark against costs. This helps justify the business case.
Top 5 People Analytics Tools
Here are the top 5 People Analytics Tools as per the list created by People Managing People.
Teramind offers a cloud-based employee monitoring solution, providing a holistic perspective on your team's workday activities. This tool supports businesses of all sizes in monitoring employee productivity, ensuring security, and managing time effectively. Think of it as an aerial vantage point for your entire workforce, offering valuable insights for managers and HR professionals.
2. One Model
One Model provides access to an extensive array of workforce contextual data sources, allowing you to seamlessly integrate and extract data from various areas such as finance, sales, labor market trends, and customer experience. Their robust platform also encompasses comprehensive people analytics capabilities. They offer a wide range of third-party integrations to facilitate this data aggregation, and you can even connect with form and survey tools to gather your customized, self-generated data.
Crunchr integrates data from various sources, including HR information systems, recruitment platforms, compensation systems, and employee surveys. It then aggregates and presents this data in a user-friendly format, making data analysis more efficient and convenient. With an array of rich visualizations such as dashboards, charts, graphs, and key performance indicators, you can easily spot workforce trends and performance gaps. This empowers you to make well-informed, data-driven decisions to enhance workforce management.
Tableau excels at collecting and tracking your HR data in real time. Its user-friendly interface allows you to effortlessly construct comprehensive dashboards using pre-designed templates, which are also fully customizable to align with your business requirements.
Tableau's robust reporting capabilities simplify trend identification and elevate data to a pivotal role in your decision-making processes. The software brings attention to anomalies through its graphical representations, enabling you to extract valuable insights from your workforce data, which can then be effectively communicated to stakeholders.
Moreover, Tableau's capacity to dissect and analyze large datasets makes it particularly well-suited for the needs of large organizations, where managing extensive data is essential for informed decision-making.
Visier is a valuable tool for organizations that rely on hourly workers, enabling them to proactively identify patterns and anomalies within their workforce data. Doing so can address potential issues before they escalate into significant problems. Additionally, Visier assists businesses in ensuring compliance with local and national overtime regulations, promoting a positive work environment, and avoiding legal complications.
Visier empowers organizations to adopt a proactive stance in enhancing their workplaces. The software allows for tracking absenteeism, providing insights to address this issue effectively. Moreover, it offers the capability to measure the impact of overtime on workplace safety, enabling companies to implement measures that create safer working environments for their staff.
People analytics unlocks immense potential for elevating workforce and organizational performance. However, it requires dedication to honing skills, implementing enabling technologies, and institutionalizing analytics-based practices.
The journey can seem daunting initially. However, taking an iterative, focused approach to build capabilities will pay dividends over time.