A high-end department store wanted to build a strong and practical data governance approach that can directly service business insights and analytics needs.
The store wanted to increase its use of customer, product, and other data to gain better business insights and drive informed business decision making. Such a data-driven approach necessitates a strong data governance strategy supported by a strong and future proof enterprise architecture.
We performed a data audit from the Point-of-Sale system through the auditing system all the way to the databases holding the financial, customer, employee, and sales data. We designed a new data structure to allow for easier, simpler, and more transparent reporting through centralizing all data into one entity.
The new structure would accommodate the needs for a lot of reporting planned for review and automation, with the reporting layer remaining the same to futureproof potential structural changes to data.
Benefits of the new data structure:
- Easier, simpler, and more transparent reporting through centralizing all data into one entity.
- “Fast” availability of clean data in a new entity without jeopardizing existing reporting, infrastructure, and operational activities. Data audit required on the current architecture.
- The new structure will accommodate the needs for a lot of reporting planned for review and automation e.g. private shopping, performance marketing, strategic divisional reporting, weekly trade report, etc.
- Additional data can be easily implemented to accommodate for the evolving reporting needs e.g. promotions, digital analytics, etc.
- An opportunity to evaluate new technologies in an agile and controlled way e.g. PBI as a visualization solution.
- Test run a fully documented process starting from requirements capture, priority, complexity, and time investment assessment all the way to automation and visualization.
- The reporting layer will remain the same in the situation of a potential future change of existing databases. The only affected part of the proposal will be the staging.
Model implementation and automation:
Added value to business strategy:
The solution empowered data and business analysts as well as business users to create and use reports in a simpler, faster, easier, and more transparent way, spending more time on taking strategic decisions based on data-driven insights.
Data users were able to focus on deriving data insights by using more advanced and complex tools from an infrastructure framework which will be sustainable despite planned technology changes.
Documentation best practices and templates were added for the Data Analysis team to follow and standardize. Therefore, the lack of documentation was not in the way of the team anymore. The decision-making process was based on facts and timely data insights.
Business impact:
