What are the next steps for People Analytics enablement, effectiveness and impact?
Establish one cleansed file for the complete employee dataset
• Identify errors and missing data and make corrections easily
• Design and broadcast web forms to collect missing data and then update the same
• Use data quality reports to drive consistency.
Visualize hierarchy within the organization and set up HR operational analytics
• Clarify reporting lines; produce instant organization charts
• Analyse heat maps for performance ranking, cost, risks, etc.
• Link datasets from traditional people sources and outcomes, e.g. sales, surveys, absence, performance
• Institutionalize first level dashboards across all key HR metrics.
Extend into advanced analytics linking to business strategy
• Go from people to roles, to Processes, Competencies, Clients and Objectives
• Track cost ratios, headcount ratios, identify drivers of best performance
• Link business outcome data to HR data.
Predictive analytics: Identify a “business challenge” and solve for it.
What are companies doing? Examples of positive momentum may be visible in the following areas:
Sales Performance and Recruiting
• Insurance companies have analysed the profiles of top salespeople and now know that screening candidates for grade point average or academic pedigree is no longer considered a strong indicator of future sales performance.
• An analytics model was developed by a technology company that was accu- rately able to predict those profiles of candidates who were likely to demon- strate negative or toxic traits (those who lie, cheat, or commit fraud) and drastically minimized the probability of hiring such profiles by deploying the model in a certain part of the selection process.
Productivity
• Organizations that hire on a mass scale such as ITes, consumer, retail and banks are starting to analyse the persona traits of high-performing employees, partic- ularly the customer facing or revenue generating cohorts, understanding their social networks, how they work and collaborate internally, and how they engage with customers. These insights then go into predicting results and business outcomes far more accurately than study of any historical data or training or operational metrics.
• Some evolved HR functions are now analysing e-mail metadata to understand why some people are more collaborative than others or manage stakeholders better than others or more productive than others; then leveraging the insights to optimize on results.
• A large cosmetics manufacturer sets up a “sales productivity centre of excel- lence” to study hiring patterns, training, compensation and other people practices in the sales force to optimize productivity using people-related data.
• A well-known retail company found that by linking sales data to the hiring and selection of store managers, analytics improved profitability both at the store and across the organization. In essence, the data revealed how more analytics- driven hiring practices, higher offer acceptance rates and reduced time to hire drove store performance and profitability.
• Some manufacturing organizations are studying the patterns of unplanned absenteeism to predict when specific people are likely to take an unauthorized day off, prescheduling extra casual or contract labour to make up for estimated periods of absence.
• Some Oil and Gas companies are using a predictive workforce planning and analytics model to identify current and future talent and skills gaps in critical oil and gas occupations over a long-term horizon. The model takes into con- sideration some macroeconomic factors like oil price and exchange rates that correlate strongly to the demand and supply of skilled labour.
Retention
• Some organizations are now collecting data from LinkedIn and other social networks to predict the “high-flight-risk” candidates among their high-potential employees.
• Companies are experimenting with smart badges, using them to gather data suggesting that offices with larger shared workrooms, more light and more inter- company collaboration have higher retention and productivity.
• Some organizations are also developing predictive models focused on enhancing the employee experience through a range of data sources that throw up patterns for analysis that then enable decision-makers to get on top of issues such as high-potential talent retention and predicting flight risk.
Compliance and risk
• Banks are studying patterns of fraud and non-compliance and can now predict
“persona traits” that will likely result in unethical behaviour.
• Some financial services companies use analytics to evaluate individual outliers, with a view towards identifying “rogue traders” and other compliance breaches as part of their risk management framework.
• Some large manufacturing and power companies also leverage analytics to iden- tify probable safety hazards or behaviours that could have been demonstrated to prevent accidents.
Copyright: Acumetric Global Solutions Pvt Ltd – all rights reserved Exhibit 9.1 Enablement framework and ecosystem
Culture
• Some organizations collect data to assess the strength of its cultural values and the degree to which they have got institutionalized, through analysing a combination of internal and external data points including running text analysis of external sites such as Glassdoor.
Each of these instances reveals the opportunity to take people data (some from HR, some from business and some external to the company) to make better management decisions. Organizations are now starting to see the value of this upcoming domain and have started investing behind it both through capabilities and technologies.
The trend towards analytics-driven HR will continue gathering strength over the coming years. Today’s People Analytics teams bring together data from a range of sources, including core HR systems, employee engagement data, survey data, external data (from LinkedIn, Glassdoor, and other systems) and text data from employee comments. Then they analyse these data points to understand company culture, find opportunities to improve retention or performance or diagnose key risks or other operational gaps (Exhibit 9.1).