When enterprises are on the path to designing and implementing an analytics solution to improve performance and decision making, key elements to success include a good architecture and design, a high performance and scalable technical platform and knowing where to source your data from and how it will integrate. Successful business intelligence and analytics solutions must have good quality master data - many businesses unfortunately learn this the hard way.
Master data is an attribute that provides definition to numeric facts. For example sales volumes or sales values might be associated with a product portfolio, geographical location, retail channel or perhaps even a customer.
When we design a data and analytics solution from scratch, master data is high on our list of priorities. Often, if insights aren’t as expected, our investigation can often follow a trail from calculations through to fact data and on to the master data. For any system that aims to give insights to users, master data is fundamental. A thorough understanding of your master data in an analytics project can save time and expensive remediation effort.
Why Is Master Data So Important?
Master data is part of good data management and is the digital footprint of any business. It changes and aligns with how a business grows and evolves. Over the years, companies develop many applications that depend on master data. Since it resides at the foundational level, any changes to master data echoes through all the applications using it. If the impact of changes to master data are not considered, disruptions to the business can be high and require onerous rework to realign datasets across systems.
So how do you ensure that you have high quality master data? And how should ongoing changes to this important data be handled by applications?
If your organisation commands a large set of master data, a few simple considerations will help in the long run:
1. Understand your data
Make understanding your master data a key part of the solution architecting process. Have a clear knowledge of your datasets, the master data they need and how they interact (e.g. aggregations, parent-child relationships, geographical diversity, etc.). The greater your understanding, the better your chances of designing a solution that correctly caters to the data structures and delivers the desired outputs.
2. Design your solution to cater for changes
Knowing your master data and having a good design with a point-in-time perspective could leave your system unable to adapt to future changes. Ensure the design includes an approach to handle future master data changes and that the data structures support changes at all levels. A well-designed system minimises disruptions to users.
3. Have a plan for regular alignment
Change is a constant and every organisation will go through reforms that will impact the applications they rely on for insights. Don’t wait until an application completely fails to realign the datasets and master data. Undertake regular refinements to save time and effort by keeping your applications healthy.
4. Invest in a good support system
When a new change (for example a new product or location) is introduced, it is often tempting to implement a quick fix to solve the issue. This might cause misalignment and make future issues harder to trace. Invest in a support service that knows the system logic, understands the data and related business processes, and can work with you to devise and execute an approach that ensures changes are made in all the right places.
Investing a little extra time during the early stages of a project to study and understand your master data can reap benefits in the long run. By reducing the number of issues faced during development, and keeping the application healthy with an ability to adapt to change, time and effort is saved for everyone in the end.