All modern workflows are highly dependent on data, and enterprises must learn the best practices of Master Data management to improve their business performance. Master data typically serves as the centralized repository of stored data for an organization. Master data governance is the application of data governance factors to a subset of data called master data. Master data is the overall data accumulated through core business entities. The elements of data governance include documenting definitions, sources, processes, policies, rules, metrics, and people to improve data management. Enterprises can also use tools like boomi integration services to enhance their master data quality. Here are some of the industry-focused best practices that take into account all the factors of data governance.
Focus on the governance of key master data entities
Identify the master data entities critical to your business processes, such as procurement, record to report, and order to cash. Different systems and regions will use different attributes for master data, which can introduce inconsistency. The goal is to rationalize terminology to align it and gain cross-system consistency.
Define and design your roles and requirements
Design the roles and responsibilities based on the desired business outcomes rather than on the personnel involved. Define the technical skills and business knowledge required for a candidate to succeed in their role. Later, you must determine the right in-house people who can train the individuals to fill those roles, or you can source them externally.
Optimize and automate workflows
Coordinate with stakeholders to understand the activities of different connected groups. Evaluate the level of coordination needed and the tasks that should be designed around a highly structured workflow. Then, automated routing, prioritization, and notification will be initiated to increase efficiency.
Automate process flow mapping and master data lineage
When the sources of master data expand exponentially, so too do the data integration and movement jobs. In these scenarios, AI and metadata tools can help you scale by automating the data lineage mapping process. This will help you identify data movement processes. To facilitate greater collaboration and productivity, it is advisable to map the owners of applications and data stores as part of the lineage map.
Embrace cloud best practices
The goal of this best practice is to find a cloud that is a good fit for your system, not adapting your system to meet the needs of the cloud. The cloud you select should have the tools your business needs and be flexible enough to adapt to your future MDM needs. Using a cloud that supports your needs should reduce the overall costs of developing master data management. Additionally, the cloud provides a highly secure, scalable infrastructure that offers additional storage and computing power.
Organize your master data for increased scalability
Previously, MDM systems were typically built as monoliths and had trouble scaling. However, modern MDM is built on a scalable architecture that supports a phased approach to enable agility for adapting to evolving market conditions. Adding more data attributes on the fly allows you to scale MDM to bring in more data quickly. In reality, data models change over time, so it’s vital that organizations have a flexible data model that allows them to make changes faster and the inclusion of new attributes when needed.
Constantly update data for privacy and security
As concerns about protecting consumer privacy increase, technologies must comply with regulations. Critical data is a valuable asset to the organization and will likely be targeted by hackers. Legacy MDMs will slow down updates and might require days of downtime for security patch updates. One of the best practices involves automating security updates, which connect with the customer and simultaneously update.
The following are elements that a modern MDM platform should have in order to achieve these best practices and adhere to the changing master data management trends:
Multi-domain and low-code: Low code or zero code platform that can integrate customer, product, location master data, and more applications.
Cloud-native: Not necessarily hybrid but built for the cloud.
SaaS friendly: To enable security and scale automatically.
AI Integrated: To ensure automated data governance.
Quality Interface: To provide data in the hands of business users.
PreludeSys is Boomi’s advanced integration partner. We utilize Boomi Master Data Hub to customize Master Data governance solutions. Our specialized boomi integration services will ease your transition to a connected ecosystem and enhance your data management process.
