Our customer had a broader vision of what they want to achieve, namely, how to get better use of all the data that they have available within HR and Recruiting. Our first task, therefore, was to define more specific targets together with the customer and sharpen the requirements as well as customer expectations. The project offered a very ambitious timeline with approximately 4 months for task completion.
In the first stage, we defined a set of targets together with the customer stating what they wanted to reach with their new data strategy. For this purpose, we used a so-called Target Canvas to give the ideation and target definition a more structured approach. The targets we defined were:
- To find out which business cases are already available in this area and should be mentioned
- To analyse and create an overview of the existing application landscape in the department at the customer
- Find out which information is being already collected with the current applications
- To use the potential fully with the use of existing business cases
After that, we evaluated data use cases together with the team. Within this approach, business questions were gathered and clustered into data use cases, according to the example of Bernard Marr (2017). One of them was, how to find the right candidate for the given position with the help of the available data. Besides that, the customer also decided to go for improvement of the candidate experience and to make the status of the application better trackable for the candidate. These are some examples of the 18 data use cases that we raised. In a further step, together with the whole team, we prioritised these cases. After that, the top 3 data use cases underwent a more thorough data strategy analysis according to Bernard Marr. His approach was a huge support in finding out who are the data customers and who the data suppliers, what are the business targets and what the strategic targets. To finalize the procedure we identified overlapping topics which we then phrased recommendations and corresponding actions.
Based on the gathered information alongside the entire project we delivered the customer a set of outcomes collected in a final report. For application landscape analysis we created a System Application Map. For the top 3 Data Use Cases we did a feasibility analysis based on the insights gathered before. Within this approach we uncovered two major challenges. The first one: the data did not have master data system. This means there were two systems where the candidate had to feed his data into independently twice. This way, the customer had two data sets for one candidate that could not be evaluated as the single source of truth was not given. The second challenge was the inconsistency of the data model which needed to be unified. These two simple challenges would hinder a successful implementation of the analysed data use cases and should, therefore, be overcome to successfully initiate a data strategy.
In consequence, we proposed, as one of many smaller and bigger recommendations, to rethink the current “process-status-centered” model and establish “person-centered”. This would give the customer the possibility to attach the data set to one person throughout the recruitment, employment and development process, which then enables the opportunity for further data analysis as part of the defined data strategy.