5. Automatization
- Identification of activities which are
- High value
- Scalable
- Reliable on manual work, such as researching inputs or entering data into a system
- And replacement of the human actor with an algorithm
- This algorithm never gets tired, never forgets and as long as we feed it properly, never abandons its services
- First of we replicate human made activities with the algorithm and design it to get on the same quality of output as the human activity. After this is achieved, we enable the algorithm to learn on its own and surpass the possibilities of human delivery. And just watch how much the activity improves
Manual data inputs, need to request data from IT. Single question is answered once. To answer the same question in the future, whole process from data collection to analysis has to repeat. Slow, tedious and often off target.
Departments have data collection and visualisation systems in place. Marketing looks into Google analytics, product management watches CRM and accounting evaluates it all in SAP. Step forward, but no accessible single source of truth exists. If someone asks “what was our revenue after tax yesterday?”, each department might answer something different.
Connection of department data into one integrated data warehouse. On this integrated dataset, custom business intelligence reporting systems are developed. Reporting is both tailored for the needs of each department as well as captures a single source of truth on results of the whole company.
When we have sharp reporting, but our people do not get smarter from it, we are far behind its potential. Here we train and mentor our clients in order to establish firm understanding of practical work with data and how to base both managerial and executive decisions on it. Not a technical step but rather a cultural one, it takes time and discipline but the fruits of this work become evident quite soon. But still, activity is done by human which brings a whole lot of issues with it.
For activities which are high value, manual work intensive and scalable we find ways for their automatization. Thus we reach the final step of becoming data-driven. We eliminate the human actor from the process and let the data run the show. Instead of the automated tasks people can busy themselves with tasks which bring additional value. Or they can just take it easy and clock out earlier. We let you decide on that.