HRA 5TH MODULE Defining metrics and Demographics.pptx

Defining metrics, Demographics, data sources and requirements, Types of data, tying data sets together, Difficulties in obtaining data, ethics of measurement

1. Module – 5 HUMAN RESOURCE ANALYTICS Dr Nagarajan Professor Rajarajeswari College of Engineering Bengaluru

2. Defining Metrics “A System or a standard of Metrics” “Simply put, Metrics are a way to quantify, measure, and track key performance indicators” In HR, Metrics are used to measure and track the performance of the company’s largest investment.

3. Defining Metrics Some questions need to be asked - Why do we track metrics? - Why are they important? - What metrics do we track? - What do we do with the information we track?

4. Defining HR Metrics Human Resource ( HR ) metrics are measurements used to determine the value and effectiveness of HR initiatives , typically including such areas as turnover, training, return on human capital, costs of labor, and expenses per employee .

5. Human Resources’ role in Metrics and Analytics - Shift in focus from administrative to strategic - Focus on revenue, Growth, Market share, Productivity. - HR has direct impact on data driven decisions - Data is the key

6. The connection between the organizational strategy and HR In some way that an organization has a specific strategy, so must HR. To demonstrate HR’s value to the organization, metrics must tie to what is most important to the C- Suite In other words, HR metrics must tie directly or indirectly to the organizational strategy

7. How HR can bring value to the organization through metrics Understand WHY you are measuring Don’t measure just for the sake of measurement What do you hope to discover, improve, increase/decrease with metrics Have an action plan once metrics are attained Understand WHAT you are measuring Metrics must relate to business Metrics must be important to the leader Metrics should be easy to gather, analyse and disseminate

8. How to define and implement HR metrics into your organization Organizational Strategy Defined - Cost cutting - Improve customer satisfaction - Develop new technology to remain competitive HR Strategy Defined Decrease recruitment cost - Increase customer service training Source and hire better talent HR Metrics Defined - Reduce recruitment cost by 20% - Increase performance level - 10% of new hire performing above the average.

9. Key Points To Remember Matric should give the whole picture, including the cost, quality, quantity, time, cost and effectiveness Focus on key area where change is necessary Develop a benchmark to use for evaluating progress towards goals Set goals and establish metrics for measuring progress If possible, compare with Competitors Use the language of your leaders Hard metrics(real data) are better than soft metrics HR Metrics are directly related to important business issues Easy to understand and data should be readily available Don’t keep metrics as secret Use metrics to identify trends and head off problems on the horizon Don’t be afraid of metrics and measuring data

10. Demographics As you create the measurement plan, consider which demographics have are a reasonable connection to the investment, as well as the type of demographic data that is available.

11. Demographics Two Categories: - Individual – Individuals personality traits, such as age, gender, education level, ethnicity and so on. - Organizational – derived from some unit the individual is part of – Region, division and work unit and so on These data come from different sources and are linked back to individual. Organizational demographics are more fluid than the individual demographics

12. Operations Data sources and requirements Compensation Customer service Human resources information systems (HRIS) Learning management systems (LMS) Social media and non- traditional learning systems Engagement Surveys Performance management systems Interviews and estimation by experts Public data from outside the organization

13. Types of Data Operational Data: Tracks the business processes Sales commission, revenue, Call centre information, defects, safety incidents, This has a advantage as it is closest to the cash flow and likely to be well organised and closely tracked It doesn’t have privacy issue as it is with HR Data The results with the analysis of this data has got instant credibility because the metrics align with those tracked by executives.

14. Types of Data Customer service data: Addresses the important business processes – in particularly where there is a high ratio of customers facing employees i.e. “Surface Area” It can be measured in many ways Reported satisfaction Business Results

15. Types of Data Human Resource Information System: Primarily they provide demographic information – education, tenure, job title and other details It includes compensation data Provides master list of participants in the measurement of project Most likely source for mapping data that ties different data set together

16. Types of Data Learning Management System. Contains information about training, which is a common focus of human capital investment measurement. Training received and date on which the training received Online training virtually always contain ways of tracking this information Traditional classroom method has an issue of tracking – Logbook of classroom booking and simple spreadsheet maintains the same is ideal

17. Types of Data Social media and informal learning system. Organizations use social media in different fashion Promoting and sharing information about the company with the outsider Recording the public sentiment about the company and its Products and Services Use social media internally providing a forum for employee to browse and post useful content. Mind set of social media is still a challenge as it requires relinquishing central control and allowing free, unstructured exchange of information. If we collect the information about who has collected the information, it is possible to directly measure impact on individual. External social media may be difficult to or impossible platform on which to measure information.

18. Types of Data Engagement Survey. Effective instrument for gauging sentiment by employees and are gaining the popularity Includes gauging the employees satisfaction with their managers or with their careers Because of its confidentiality these surveys are very difficult to map to a particular individual or to a “Manager” Here the involvement of third party will make better sense Confidentiality is the important issue and all employees should not worry their information?’’ and the further about “Who is might read consequences - There are issues with the surveys

19. Types of Data Psychological Testing: These show promise in predicting the performance on job metrics, both individual sense and “fit” towards the team. The role of “Psychological and social capital” in creating and maintaining a dynamic, productive workplace is an area of growing importance. - Research resilience suggest are that the concepts such as “Self efficacy, hope and important constructs in understanding employee performance. -

20. Types of Data Performance Management System: Internal rating and planning systems designed to evaluate employees or teams or to plan for future development for those employees. 360 Degree evaluation system KPI These can results in proposing someone for training programmes Relationship between the KPI’s and Performance system can be tested

21. Types of Data Expert estimation: This is one way of collecting data from many things Information such as estimation of cost of security breach, the likely wood of success for particular projects, or the amount of revenue a new project could generate This method is commonly applied to costs and risks

22. Types of Data Public date from outside the organization: Bureau of Labour statistics Stock performances – Positively with compensation and negatively with turnover Currency exchange, in particular to multinational organizations Benchmarking

23. Tying your data sets together Crucial task is to combine date from different sources “Unique identifiers” – Employee ID, E- mail ID, Social Security Number or Aadhar Number. To make connection between data sets, your data analysts will need one or more unique identifiers. With people or employees generally identifiers are the employee ID The numeric identifiers are clean, efficiently stored and unambiguous. And they also protect the privacy of the individual. Proper names are the messy identifiers – There are multiple issues. IF the performance management system uses employee ID, a training system uses proper name, and the other systems that use E- mail address, all will not get into how mapping can be created, but good analyst will be able to manage.

24. Where the data may exist? Human Resources Learning Operations

25. Difficulties in Obtaining Data Data availability in many systems; difficulty in comparison and consolidation Need Approvals and conditions to get the data. Problem of negotiation in sharing the data; convincing that the amount of data was of advantage to no one. The stakeholder’s apprehension about the results Some data stored externally and will require cooperation between different companies

26. Difficulties in Obtaining Data Conti…. Systems have different criteria for including and excluding employees, such as terminated employees, summer interns, contract and temporary workers and more Some systems may use a convention for identifying employees that does not exist elsewhere Employees may have slightly different identifiers in different systems. Not all employees belong in all data sets. Identifiers may change over time

27. Ethics of Measurement and Evaluation Sensitive information – Confidentiality – Using employee ID can safeguard – “hashing” of identifiers, unique and reproducible but does not give information to prying eyes. Secure and encrypted channels to safeguard the information. Justification to some decision – by knowledge and techniques, which may affect their life. Presence of wisdom and kindness in your process – HR analytics should provide toolkit to make tough decision

28. Ethics of Measurement and Evaluation Seeking the help of the stakeholders and compliance officers regarding understanding on “What data are off limits for making decision?” Example- Pharmaceutical companies giving continuous education on the treatment. Considering the race, gender and age, as they are very critical in making the sensitive decision

29. Human Capital Analytics Continuum Regression and Causation Correlations Benchmarks Scorecards & Dash Boards Anecdotes / Reports Optimization

30. Thank You