It is universally acknowledged that good data can help company performance. In the human resources field, for example, data can help identify why top performers leave, what might be done to retain them, and how to make more employees satisfied going forward. In operations, it can help identify what processes are more productive. Across the board, data can be used to pinpoint the characteristics of creative teams. Data analysis can identify what characteristics make a team malfunction — and then be part of the suggestions to correct the impediments.
Data analysis is key to business strategy.
Good company data is an essential part of business strategy.
Yet, what constitutes “good data” isn’t clear to many members of an organization, let alone those exercising leadership skills in the affected departments.
Smart Collection of Data
Most companies are replete with data. Human Resources likely collects information from performance appraisals and exit interviews, among other sources. Many operations groups collect data on turnover and employee longevity. Departments may be rich sources of process knowledge and fostering creativity in teams.
However, the data may not
- be accessed to augment performance
- be known or accessed by departments outside (e.g., Human Resources may have a use for turnover and longevity data)
- have been collected or maintained in a way that makes smart use possible.
A March/April Harvard Business Review article identifies five steps to improving data quality and utilization within corporations. They are:
- Improve data quality
Corporate data is no different than any other data collected: it needs to be reliable and validated. In one instance cited, only 55% of supervisors completed performance reviews for their team. A shortfall like that means nearly half the potential results are unknown — and, therefore, that no valid conclusions can be drawn.
In another instance, dates were not included regarding promotion and turnover data. Anyone trying to extrapolate average times to promotion would be thwarted. The collection of data needs to be systematic.
In one organization, only 55% of performance appraisals were completed.
- Link related datasets from different departments
Oftentimes there is overlap between departments and the data each collection. Even more significantly, there could be potential synergies between data that exists in silos. Human Resources, for example, may be able to make good use of data on employee retention and time to promotion collected by Operations. They need to know it exists and be able to access it.
- Analyze your data
Statistical analysis of data is a sophisticated task. The appropriate analysis needs to be determined before the data to be collected is decided upon and before it is collected. It might be an occasion to call in the experts.
- Use theory to be predictive
One of the primary uses of data is to augment success going forward. A second use is to fix what’s wrong. Are cross-functional teams more likely to be productive and creative? Use data to find out the predictors of success — and implement them in hiring practices and team formation. Are top performers leaving the company? Use data to find out why. From there, either change recruitment policies or adapt company policies. Theory can help to chart a path to desired future outcomes.
- Keep track of any changes and their outcomes
Since so much data collection is done to augment success or troubleshoot existing issues, it is vital that companies keep a good record of any changes made, what they were, and when they were implemented. From there, the outcomes of the changes need to be tracked and that data collected.
Corporations are excellent data collectors for the most part, but the data is frequently not utilized effectively. These steps can help firms grow optimization of their data.