2. Data silos
- We get there with IMPLEMENTATION of data collection systems
- Step up, when various departments have their own data collection and presentation tools.
Think Google Analytics for marketing, CRM for product, SAP for finance and something else for logistics.
They now can continuously answer critical business questions with little effort for data collection, but usually with limited flexibility. More importantly, each of these systems talks in a different language.
If you say Revenue, each department understands something different. So to avoid misunderstandings, they do not talk at all
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.