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When a financial services company wanted more from their data
Deloitte Analytics built a new IM framework from the inside out
The member firm client, a policy bank and member of the financial services sector, was experiencing difficulty with their information management techniques. They were getting involved in new areas of business and with new regulatory requirements they needed their Information Management (IM) strategy to evolve.
Recognizing the issues the member firm project team got to work standardizing the data for analytic needs. Using both internal and external data, an enterprise information management framework was built to help the member firm client manage and use their data in a more efficient manner. The model created has been used for other projects, both within financial services and in other industry sectors.
The member firm client required advice on IM to accommodate their long-term business and IT strategies. This included improving their current IM practice, as well as re-defining the IM strategy in line with their future business expansion in areas such as lending and investment. The IM system that was to be integrated needed to consider market demands, business expectations, and regulatory needs.
The member firm client was looking for long-term benefits through reduced systematic risk and better use of data, and to create a competitive advantage by leveraging the data successfully. They wished to manage the quality, consistency, usability, security, and availability of their organization’s data to facilitate business process decisions.
The member firm analytics team recognized that there were two major difficulties; firstly the management perspective for enterprise information, and secondly the technical perspective of managing information. They wanted to leverage a mature framework and best practice in Enterprise Information Management to provide recommendations and models to overcome the these problems.
The project went through four stages to integrate the IM system:
1. Data Standards – this involved laying out all data standards within the requirements for managing the information, while at the same time catering for the technical difficulties and standardizing all different types of data including the data quality matrix
2. Information Governance – setting up the governance framework for managing this information. This was broken down into areas according to the data standards. For example:
- Management framework with regard to data quality
- Management framework with regard to the analytic data model
- Governance framework with regard to data management
- Applications from a technical perspective – this was based on data standards and governance framework techniques. Using best practice in Enterprise BI, ODS, and ETL to standardize, analyze, and manage the data from a single point of view
3. Applications from a business perspective – there were two main areas within this phase, including regulatory reporting and workflow application for managing the data
How analytics helped
The model created has helped the member firm client understand the roadmap to information governance, based on their current and future needs. The IM model will align with the new application architecture and it is highly reusable as their data and business expands.
The majority of the data used was for analysis and reporting and a follow on project has been proposed to use some of the information for predictive and future purposes.