Work ability knowledge management process
Knowledge management requires managing data, which includes data collection, processing, arrangement, and analytics. Everything is based on high-quality data – also in work ability knowledge management.
High-quality data is the basis of work ability knowledge management
High-quality data on work ability and its antecedents is the basis of work ability knowledge management. If possible, the data must be available in employee level, even though the work processes are the unit of analysis. Thus, it must be possible to focus, for example, the recording of working hours on an individual employee, and not for example a team. On the other hand, the employer cannot process health information on an employee-specific basis.
You can use detailed data to create all group-level analyses, which improves accuracy and reduces mistakes. The less your analyses are based on group-level indicators, the more precise your analysis will be.
All data cannot be collected and saved
Remember that it is not possible to collect and save just any data on employees. Generally speaking, data that is important with regard to work processes is related to doing work, such as absences, working hours and work performance. The collection and saving of sensitive personal data, such as health data, is prohibited on an employee-specific basis in all situations. However, it is possible to request statistical data on these from the occupational health services and to combine the data with your company’s group-level data.
Ensure the quality of the data you collect
Knowledge management involves ensuring that data is not missing and that it is accurate. Data entered into systems by people is especially subject to quality problems. Create practices with which you can monitor the quality of the data entered by people and intervene in case of deviations. The more automatic the data collection and the less data is entered by people, the fewer errors will occur.
If possible, try to collect data as complete entries automatically saved in different systems. Only use surveys and data obtained from people when you are sure that it does not already exist or that it cannot be derived from other data.
Data analysis can be divided up into descriptive, diagnostic, predictive and prescriptive levels. The value you gain from the data grows as you move from the descriptive level towards the prescriptive level. At the same time, this increases the skills and understanding required from specialists analysing the data.
1. Descriptive level
At the descriptive level you receive basic statistics such as averages and the number of events. Example: a trend showing sick leave days and the average duration of sick leave due to musculoskeletal symptoms.
2. Diagnostic level
At the diagnostic level you understand the connections between things by combining and grouping variables. Example: a table of whether a specific profession has more incidences of musculoskeletal symptoms.
3. Predictive level
The predictive level involves statistical methods that you can use to find differences between groups and find out cause and effect relationships. Example: a risk model of the connection between age, job, education and pay level and musculoskeletal symptoms based on data from the past 10 years.
4. Prescriptive level
At the prescriptive level, you use data to boost processes so that you immediately implement recommendations and monitor changes in real time. Example: smart work shift system that minimises musculoskeletal symptoms by ensuring sufficient rest periods or less strenuous tasks between strenuous shifts.
Sometimes it is thought that there is a fifth level above the prescriptive level: AI and machine learning. However, AI and machine learning can also be considered methods that can be used at each of the levels. Machine learning methods are rapidly becoming normal alongside traditional methods.
Make use of specialists in analyses
Make use of specialists’ insight and experience in data analysis. The higher you aim on the analytics levels, the more you need expertise. Work ability management analyses are often at the descriptive or at the most at the diagnostic level in knowledge management, although companies have already become used to predictive if not prescriptive analytics in their other operations, such as plants’ automation control or logistics planning.