Integration is the beginning stages will largely be manual by delivering reports or ad hoc analysis of a specific business question. It is important to cooperate with managers to get the correct questions they need answers to. Standardized reports can be created which can be reused for monthly or quarterly updates. Software packages can provide automated integration for some specific tasks, but often big gains can already be made by manually relaying back insight to the business. Timing is, in this stage, of less importance than correctness.
Define which information is needed by decision-makers.
Standardize reports.
Further integration takes place in this stage. The analytics team is coming up with new recommendations by digging deeper into the available data. They deliver detailed reports with their findings and recommendations to upper management, whose task it now is to put the changes on the agenda for implementation. Automated reporting and dashboards are getting more sophisticated. Where in the past they had to wait on a report, they can now query a standardized report on demand whenever it suits them.
Create a process to implement recommendations backed by analytical evidence.
Create on-demand available reports and dashboards with the right information.
The Optimizing stage focuses, for a large part, on the automatic integration of analytical insight. More analytics is done on the fly and directly available for use in production systems. Automation means speed, so turning data to insight to actions is getting more rapid. This requires a coordinated effort between analytics and those responsible for production systems. It is not only an analytical challenge as much as a technical challenge.
Automate the use of analytical insight into existing processes.
Focus on automatically turning data into actions instead of mere insight.
Analytics is spreading everywhere in the organization. From centrally coordinated integration efforts we are moving to analytics being performed closer to the source. Analytics close to the related domain gives the advantage of better domain knowledge and stronger integration. Departments can respond faster to opportunities when they are in charge of their own analytics. Systems with a highly scalable architecture provide easy access to data and analytics, for applications anywhere in the organization. These central hubs allow rapid and easy integration of existing analytical capabilities into new products and services.
Distribute analysis closer to the relevant departments
Provide easy integration of analytical capabilities into new offerings
We see analytical results further integrated into all business activities. It tells us what to do on a macro-scale: what business ventures to pursue, and on a micro-scale: what do we recommend this customer?. Analytics moves further to the point where data is generated. For example an IoT devices can already have machine learning capabilities built-in. The advantage is that the data is generated and analyzed on the same device. The computational efforts are also distributed. This architecture allows for massive scaling.
Use analytics to make all decisions on a strategic scale and on an individual customer scale
Integrate analytical processing capabilities into all products without centralizing data collection