The pandemic has proved to be a turning point for Human Resource Management (HR) functions for organizations all over the globe.
Remote hiring and working environments have accelerated the automation of HR processes and the adoption of cloud-based technologies. It has provided enterprises with centralized high-quality data and computing power for running analytical algorithms.
Also, employee expectations have undergone tectonic shifts, and enterprises need to leverage data to understand these trends. This unique combination of factors is responsible for the rise of People Analytics as a priority for enterprises today.
Contents:
Introduction to People Analytics
People analytics refers to applying statistical data analysis and visualization techniques to enterprise HR data to generate actionable insights.
While most organizations have basic reports to track HR metrics, it requires a strategic approach to mature to a level where data analytics can drive HR strategy. Typically, organizations can classify into one of the four below levels:
- Level 1 – Operational Reporting – Organizations can analyze past data reactively using reports.
- Level 2 – Advanced reporting – Organizations have automated real-time reports for monitoring operations.
- Level 3 – Advanced analytics – Organizations can create analytical models to proactively identify patterns that show how certain variables affect business outcomes.
- Level 4 – Predictive analytics – Organizations can analyze historical data to predict future business outcomes. These predictions are the basis for strategic decisions.
According to Deloitte research, companies that reach level 4 in the People Analytics Maturity Model generated 30 per cent higher stock returns than their peers.
Typical Application of People Analytics
Typical applications of People Analytics include:
- Talent Sourcing and Acquisition – Analytics helps you determine which sources of candidates are the most effective. It also helps predict a candidate’s performance and chances of attrition based on technical and personality assessment scores.
- Employee Engagement – Analytics help create compelling employee value propositions (EVP) to attract talent and improve employee engagement. It includes configuring salaries and non-financial benefits, designing work environments, and building career progression paths.
- Employee Retention -Predictive analytics help identify top performers at flight risk and allow managers to take early corrective action.
- Workforce Planning – Workforce analytics help foresee the demand expected for new skill sets and identify skill gaps in the current workforce. It also provides insights into the capacity utilization of teams, their efficiency, and their scope for growth.
People Analytics Strategy
According to the Gartner 2019 Future of Talent Analytics Survey, only 40% of senior leaders seek HR data for strategic decisions. And only 23% of heads of talent analytics believe leaders are effective at using HR data to drive business decisions.
The core objective of an effective People Analytics strategy is to increase the actionability of People Analytics. It is achieved by ensuring the credibility of people’s data and communicating the insights effectively to business leaders.
How to Set Up Your Analytics Strategy
Research by Bain & Company says that automated processes and improved decision-making using analytics can increase HR process efficiency by up to 20 to 30%.
Following is a step-wise approach to a fool-proof, sustainable people analytics strategy,
1. Identify Goals
The first step is identifying strategic goals for people analytics in your organization. Determine the HR outcomes you want to optimize and set measurable metrics. For example, the objective could be to reduce attrition by 2 per cent.
2. Determine Business Use Cases
Based on the goals, you can list the business uses cases where you need to develop People Analytics capabilities. You can build a phase-wise roadmap prioritizing the most valuable use cases.
For example, for the above objective, you may use analytics to hire resources less likely to quit or try to engage existing employees and increase retention.
3. Address Data Gaps
Assess what data is needed to meet the requirements of the analytical models. This data could be demographic, behavioural, or perceptual. Evaluate if your existing HR and other enterprise systems gather this data. Also, list external data sources that could be required to integrate.
4. Discover Skill Gaps
According to a 2018 McKinsey survey, 66 per cent of executives said that addressing potential skills gaps related to automation was at least a ‘top ten priority.’
A long-term analytics strategy needs to address the talent and skillset needs of a dedicated People Analytics team. You can build these capabilities in-house or rope in external experts to bring the necessary expertise in statistics, machine learning, and artificial intelligence (AI).
5. Collect Data
Start collecting business data in the required formats for analysis. Data such as employee profiles, performance KPIs, and salary data may already be present in most HR automation systems. However, you may want to conduct surveys, interviews, and focus group discussions or extract data from social media to gather unstructured data for specific use cases.
6. Filter and Analyze the Data
Run data quality checks to ensure accurate, consistent, and complete data. You may want to remove or retain outlier data based on specific use cases. Before going for predictive analytics, use the data to generate descriptive reports and examine patterns that could be useful.
7. Interpret and Communicate Results
Enterprises must focus on the insights that are actionable, impactful, and relevant to the business goals. You must present the proposed improvements with a business context and clearly defined expected outcomes. You should also highlight the processes and IT systems that could be impacted by implementing the changes.
Linking Business Performance and Strategy Through People Analytics
The real prize of companies that can harness People Analytics is that it goes beyond improving specific processes. It can drive business performance through tangible financial impact on your bottom line. Strategies devised based on insights supported by data can be directly linked to measurable improvements in performance KPIs.
According to a study by The McKinsey Global Institute, companies using an HR-analytics solutions portfolio could realize an increase of 275 basis points in profit margins, on average, by 2025.
People Analytics Best Practices
There are certain guiding principles and best practices that you must adhere to ensure the business success of people analytics projects.
Maximize Automation
Automation is the only true catalyst to enable People Analytics. Analytics requires a centralized database and extensive data collection capabilities across HR processes. Manual data entry is time-consuming and prone to errors. It is not possible to generate analytics-driven insights without automating the process first.
Use Data Story-Telling
Plain vanilla reports or complex data models will not help generate stakeholder buy-in. Present the analytical insights using compelling visualizations with a business context. Weave projected financial benefit figures supported by concrete data into the narrative to spark interest and initiate business changes.
Focus on User-Adoption
HR professionals adopting automation is a prerequisite for the success of analytics initiatives as it ensures complete, consistent, and continuous data. Explore solutions with intuitive user interfaces and mobile access to drive adoption. Focus on the training and change management needs that smoothen the transition from manual processes to automation.
Foster a Data-Driven Culture
Build data literacy among business users to ensure they understand the applications of descriptive and predictive analytics. Ensure strategic business decisions are backed by hard data and minimize the influence of human biases and prejudices.
Prioritize Compliance as a Part of Analytics Initiatives
People Analytics deals with personal employee data and financial figures that are sensitive and confidential. This data is subject to several government regulations, business policies, and industry-specific rules. Ensure adherence to data privacy and confidentiality terms to minimize compliance risk.
Measure Outcomes and Refine Models
Analytics initiatives are iterative. Measure the outcomes of each analytics initiative and compare them against goals. It will enable you to modify the model parameters until the measurable improvements in business outcomes align with your objectives.
Strive for Better Quality Data and Governance
Ensure consistent frameworks and formalized collection processes to maintain data quality across the organization, not just HR. Analytics tools use HR and other enterprise systems data to provide strategic insights. Consistent data structures, formats, measurement units, and currency conversions are required to maintain the accuracy of data flowing into analytics tools.
Be Open to Engaging External Experts
Data science skillsets are crucial to analytics success. People Analytics is a rapidly evolving stream with new tools and algorithms introduced every day. While building in-house resources and capabilities is imperative, you can also involve external consultants, if required, to bring in the necessary expertise and unbiased outlook.
Innovate with POCs and Scale
Adopt an agile work methodology to implement analytics insights. Experiment with incremental proofs-of-concept for small employee segments, geographies, or business units to determine the business effectiveness of analytical models. Once you establish the business value, scale it at the organizational level.
Final Thoughts
According to a recent Fortune Business Insights report, the global HR technology market will grow to a staggering USD 35.68 billion by 2028. Enterprises that automate end-to-end HR processes and use this high-quality data for generating analytical insights will stay ahead of the curve.
A 2019 Gartner survey found that 23% of organizations piloting or using AI were doing so in the HR and recruiting domain. Advanced AI and ML-based algorithms will increasingly find applications in HR processes, particularly in talent acquisition, one of the biggest challenges enterprises face today.