Hr analytics inter relations plot
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The document outlines a structured approach to data analysis, emphasizing the importance of understanding business questions and creating hypothesis frameworks. It presents comparative statistics between two units, highlighting differences in age, product quality, training days, manager performance, and service quality. Additionally, it discusses methods for identifying causal relationships between factors influencing performance, using inter-relationship plotting to determine root causes and bottlenecks.
Hr analytics inter relations plot
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- 2. Problem/ Question Understand the business question Create the hypothesis and concept analysis tree 2 Data Analysis Insights Look for data sources, define and/or create Collect data Cleansing and enhancement Run analysis Review analysis tree Combine and translate analysis results to real insights and actions Presenting Reporting (Action) Effective reporting and visualization of results
- 3. 1. 2. unit A : average age 32 year; unit B: 40 year unit A : average product quality 2.6; unit B 4 unit A : average number of days of training 19; unit B 52 unit A : average tenure 7 years; B average 13 jaar 3. Managers in unit A on average have a span of control of over 8 direct reports, while managers in unit B on average have 6.5 direct reports. 4. The performance score of managers within unit A is, on average, 3.2. The average score for managers of unit B is 3.1. 5. Service quality of unit B is rated with 8 (out of 10), service quality rating for unit A is 6 (out of 10) 6. Managers of unit A have been working for the company on average for 12 years, while this is 13 years for managers from unit B 7. Managers in unit A had on average 27 days of training, B 58 days 8. 3 The quality of the service an employee delivers depends on the quality of the product, the age of the employee and the number of training days. In unit A the bonus is on average 10% while this is 9% on average for B
- 4. Statistical tools will help you find correlations between events. However, they do not tell you about the cause and effect. The inter-relationship plot helps you identify relations of cause and effect between factors (by reasoning). It's a bit like 'mind-mapping'. Put all the factors on a board and discuss the relationship between each combination of two factors. Not the "strength" of the relationship (which comes from statistics) but the direction of the relationship: between the two what is the cause and what is the effect? An important rule: Only one direction possible! So choose. Draw an arrow between the two factors in order to show the direction of a connection. No connection or weak means no arrow. Do this for all aspects. Then add together the number of outgoing and incoming arrows per factor. The factors with the most outgoing arrows are called the 'root causes' or 'drivers'. The factors with the most incoming arrows, are the 'bottlenecks‘, i.e. the visible (final) problem you want to solve. 4
- 5. 3/1 2/0 Low quality Managers High demands of customers 3/0 0/5 Less skills and experience Lower customer satisfaction Lower working pace 1 /4 5 Low quality of tools and instructions 2/1
- 6. "A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.“ Wayne Gretzky 6 Questions? Mail: info@analitiqs.com Call: +31 75 20 22 603