Find out how we gave the board of a client the data it needed to make crucial investment decisions.

Business Intelligence – the devil is getting the data

Making decisions is the day to day business of any board. Sometimes the decisions are easy, but mostly they are difficult and the difficult ones need good data to ensure the best outcome. We were delighted to be engaged to build a framework which will gather the data to enable the board to make good decisions and keep making those decisions.

The business was good at what it did. It make wise decisions, but sometimes the decisions needed a little more evidence. Every time a complex decision needs information, it would take a long time to get the information needed. The information was out there, it just needed pulling together.

What compounded the issue was there was a common disinterest in gathering such data among the different stakeholders. There were many challenges and as this wasn’t the first time this type of work had been tried.  

Finding the data

The data appeared to be scattered across multiple repositories and stores. We needed an underlying meta model to help bring the data together. We devised one based on the TOGAF Content Meta Model and expanded it to include the activities of the board, and the delivery framework the client was adopting.

The meta model was very comprehensive and contained a lot of detail. It was clear there was going to be difficulty in consuming it all at once. Questions were asked about what was the key questions they needed to know. This trimmed the meta model down to a more manageable level and enabled more targeted data collection.

With the meta model and attributes of the meta model decided, the sources for the data needed to be sought, It also allowed the creation of the reports and dashboards, the whole purpose of the whole endeavour.

Automate

Some of the data lay in repositories that automatically collected data, and these were a good source to begin with. They laid the foundation of the core meta model. It’s not just about the data, but the cleanliness of the data. Different repositories contain different styles of data. Cleaning these was a big part of the data collection.

Modelling the business capabilities ensured there was understanding of what the business did and the gave each capability context. As more data was located, this began to light up the reports which were developed as requirements came in from the board.

The meta model helped keep the collected data in order. Relationships were important to link each part of the model, and these revealed important ‘derived’ relationships. These derived relationships showed where items three or four relationships away could give vital support to decisions.

For example, knowing what data supports core business capabilities, or what business capabilities are provided by applications, can give vital information to support investment questions.

More to be had

The core meta model is only the beginning. We had created the basis for the expansion of the data collection. The reports and dashboards were showing real value. The board no longer has to wait for important information to support decisions.

While we had laid the foundations for the run and operate side of the meta model, the strategy and direction side of the meta model will slot into the current data set. Inserting the board’s vision, and the OKRs that come out of their strategic work, can feed into the delivery part of the model. Epics will be related to the parts of the business they will be changing, giving a complete dashboard showing the performance of the business, the moving parts, and the initiatives which will change them.