Master data management is undeniably essential to every company. However, to some extent, its implementation on a wide scale is still very rare. To innovate processes, it is crucial that companies control their business data with a strategic point of view. This article may be helpful to you by suggesting a list of 10 steps on how to store and prepare master data.
1. Manage multiple business data
In general, master data management (MDM) is a method used to define & manage the critical data of an organization to help them with data integration. The mastered data may include reference data - the set of permissible values, and the analytical data that supports decision making. Enterprises can consider using an MDM platform to ensure compliance within a single business division since MDM platform can handle multiple data types effectively. Therefore, they can help companies to demonstrate a rapid return on investment.
Moreover, the system can later be extended to accommodate other business divisions for even greater company value. Managers are advised to know three primary categories of MDM benefits including operational efficiency (e.g fewer erroneous deliveries), better business intelligence (e.g less reconciliation of disparate figures), and regulatory compliance (e.g Fewer touch-points for data) to estimate the savings as well.
2. Set up a governance process
Despite the huge benefit, MDM is not a long-term solution. Conflicts among data from multiple sources are unavoidable and they must be resolved. While data will be used for many purposes or will be heavily shared, it is difficult for a single person to resolve the problem.
Instead, companies should implement an authorization process involving multiple people. Managers should also know exactly who will be responsible for the accuracy of which pieces of data, and who should be involved in the authorization process (a process that often involves both business and IT).
3. Comply with your company’s standard workflow
Workflow is a vital component of both MDM & data governance. It is beneficial because it can be used to both monitor compliance in real-time and automatically alert the appropriate personnel of any potential violations in data management.
4. Choose which area to prepare master data first
This is the first step to do after you have set up all the above platforms. It is highly recommended that you should identify where to start and how to progress. One way to do that is trying to map your master data to one chart (mind chart, pie chart,.. and selecting where to start first to determine a course of action.
This chart should show complexity, business value and volume of data (volume shown as the size of the bubble). High-value, low-complexity, and low-volume subject areas are low-risk points to tackle first.
5. Conduct a data inventory
After you have established the best starting point, it is time to understand where the data is currently created, modified, and stored so that everyone involved can get a common insight into the scale of any problems happening to the master data.
More importantly, you can do this at a high level by identifying 10 data subject areas as in the chart drawn in the previous step or by developing a simple footprint matrix demonstrating systems against subject areas. In this matrix, you can record date creation, use, and modification.
6. Educate yourself about how to store and prepare master data
Good groundwork and research are required when using MDM. Knowing how to use several research avenues, such as Webinars, industry analysts, conferences, community forums, and contacts from other companies in the same or similar industries to gather research material that will help you in storing process.
The best way of learning is to practically get yourself involved in the real work, or you can join in training sessions to introduce the topic, public Webcasts, or independent training events from industry analysts or famous consultants.
7. Provide support for service-oriented architecture (SOA) systems
MDM is the technology platform providing reliable data, which means that all the changes happening to the MDM environment ultimately result in changes to the dependent SOA services (a style of software design which provides services to the other components through a communication protocol over a network).
The MDM platform will automatically generate changes to the SOA services whenever its data model is updated with new attributes, entities, and sources. By that, it protects the higher-level compliance applications from any changes made to the underlying MDM system.
8. Create a golden master record
You can create a golden master record with the best field-level information and store it centrally. It is obligatory that the MDM system is able to automatically create a golden record for any master data type (for example customer, product, asset, etc.) to enable compliance monitoring & reporting. Besides, the MDM system should provide a robust unmerge functionality as well to roll back any manual errors or exceptions.
9. Store history and lineage
It is a fact that companies care a lot about the history-storing capability of MDM. The ability to store the history of all changes and the lineage of how the duplicate has merged is a requirement in order to support compliance because every compliance initiative requires the ability to audit such changes in data for over several years.
10. Create a business model of the data
Ultimately, you will need a model of all your business data to program a set of formal definitions. Start by creating an initial “outline model” to help with data inventory and define what is in each subject area. You should also think about the most important business rules that govern your data. Then, find where data stewards are invaluable as they will be responsible for ensuring these business rules.
Basically, you have to spend hours & days of number-crunching, trying to find the way how to handle various master data of your business. Master data is especially important in various business software that utilizes technology like Machine Learning, Big Data Analytics. Among them, Abivin is the smart platform using AI and Machine Learning to optimize Logistics, ultimately resulting in a significant reduction of Logistics cost up to 30%. Abivin has been trusted by many worldwide companies such as FrieslandCampina, Kospa Logistics, A.O.Smith, Saigon Newport Corporation.