(1) Prediction of Employee Attrition well before they actually leave: Since a talented employee is the most important resource of an organization, it’s very important to understand the dynamics behind their careful and sustained retention. It’s not merely about their compensation, their comprehensive demands 6, 12, 18 months in future need to be carefully understood. It is needed to record, track and monitor the workforce decisions on who is retained and who is shunted out.
(2) Prediction of successful employees based on incoming resumes, interviews and Technical/Psychological tests. It can save lot of dollars for an organization if employee pertinently successful to the organization based on their resumes, tests and interview can be predicted. The need is to arrive at advanced scoring models to predict the probability of success so that focus can be shifted to few candidates.
(3) HR Planning: The accuracy of predicted revenue and the talent demand needs to be enhanced by incorporating third part data in the existing HR Planning models
(4) Organization Simulations: Organizations can be modelled and simulated to arrive at decisions like rightsizing the organizations, optimum management layers, spans of control,
(5) Workforce safety analytics: Locate the underlying causes and contributing factors for workforce accidents and thus promote cost containment, safety and sustainability.
(6) Workforce optimization: Using appropriate workforce scheduling and effectively deploying the workforce in a manner that controls the costs and maximizes the revenue. Performance data can be used to align the recruitment and developmental initiatives with the business objectives.
(7) Evaluation metrics and models of employee training
Data Needed: ERP data, day-to-day transaction data, Open Source intelligence, Social Networking Data,
Techniques: Data Mining & Machine Learning Techniques for predictive modelling
Architectures: Big Data Architectures churning out the historical data across the corporates.
Tools: Oracle (OBIA), SAP (Workforce Intelligence), Workday (BigdataAnalytics), and SumTotal
Paradigm Shifts: Integration of ERP and predictive analytics turning raw data into useful analytics, focus on quality of critical data and not all data, broadening of analytical capabilities and not just routing analysis, continuous improvement of the models based on new data, making the decision processes as data driven
Best Practices:
- Start with the problem, not the data:We are all flooded with data: employee data, location data, social data, compensation data, and much more. There’s no end of collecting data. Rather you have to start with the problem: What big decisions would you like to be able to make? What problems would you like to solve? E.g. Sales productivity, turnover etc. Then you may have to derive the factors which contribute to a predictable high-performing sales person? After that you should know how can you better source, attract, and hire such people?
- Clean and integrated data is 90% of the job done: Until the data is clean and well defined, any analysis you do may be misleading. Most HR data is quite dirty. Fields are filled with incorrect, duplicate, out of date, and inconsistent information. And you’ll find one of your biggest challenges is clearly defining what various data elements mean. HR data (date of hire, age, experience, educational history), recruiting data (pre-hire assessment, interviews), performance data (ratings, job assignments), training data (program completion, certifications, scores), and leadership data (leadership skills and assessments) need to be well integrated with business drivers.
- HR Analysts do not have to be Computer Scientists: Most HR analytics projects can be managed in traditional relational databases or Excel. If you have clean and well organized data, the volumes are not that vast. So the skills most companies need are statisticians and well organized analysts. And if you really do need a BigData infrastructure, you can outsource it (and your IT department is already working on it). Remember the most important skills in an analyst is “curiosity.”
