Management Information (M)
Data Analytics and Insight

“We knew at a high level that huge amounts of data flows through the process, but we didn’t know enough about it to make it useful.

EmmaProject Manager, Investment Bank

“We thought that the majority of the data was system fed, but we soon found out over 50% of our data was being manually manipulated in excel and word documents.”

JamesExecutive Director, Investment Bank


Our client was undertaking a huge customer/user experience project, aiming to completely re-engineer a costly, heavily manual, time consuming internal process. An external UX consultancy was brought in to analyse the process from a customer/user perspective, conducting interviews and creating customer journey maps. Our separate role was to understand the end to end process from a data perspective.

Our key deliverables were to understand what data was flowing across the process, when, and through which systems. Once that was established, we could detect trends and rank each of the data elements in terms of their criticality of the process.

Key challenges were:

  1. 10 key ‘stages’ of the process were identified by the UX team, but within those stages were tens of sub stages. We had to break down the high level stages into their component parts before the analysis could begin.
  2. There was no ‘single source of truth’ or ‘golden source’ of data. Each piece of the process had to be collected manually, meaning we needed to implement strict governance around approvals and sign offs at each stage once data had been collected.
  3. Data could only be collected at an interface level, meaning interface to source system mappings would be required to make the analysis useful.

We adopted the following approach to this project:

Stage 1

Working closely with the external UX consultancy, we first had to index the informal process ‘Blueprint’ into a formal database format to allow column and row referencing.

Stage 2

We designed a robust data collation process to ensure data entry consistency. This process was then followed by members of the HR operations team. This method allowed the process blueprint to be divided into sections and allocated to individuals, meaning the data collation process was completed in weeks rather than months.

Stage 3

Once Stage 2 was complete, the data elements were transferred to the master database where the metrics and analysis took place. This analysis was highly detailed and for the first time, management were able to see the quantitative evidence behind what was already known to be a ‘bad’ process.

Stage 4

Management selected the data points that they wanted to highlight to senior management, and we set to work on producing the final presentation formats.

Stage 5

We implemented change control and governance ensuring that any changes to the data or analysis could be executed simply, and effectively.

We created the quantitative  evidence required to understand, analyse, and explain the data issues of a highly complex process in a robust, structured format providing management with transparency, information, and insight.


Here are some more examples of how we have helped the largest firms in the world deliver their goals.

Click here to see all our case studies covering process automation, data intelligence and advanced management information (MI).