4 Main Master Data Management Implementation Styles
Your business’s master data management implementation style largely depends on your organizational structure and your business needs. Master data management (MDM) exists to break down functional data silos, and technology advances now provide clean, validated data sourced from and catering to multiple domains in real-time.
But technology often isn’t the main stumbling block to breaking down silos. It’s culture. The board room needs to trust the data is secure and reliable, and your teams need to know that a cross-enterprise MDM will serve their function-specific needs. Different MDM implementation styles and MDM uses can address wherever your business culture is on the trust spectrum and help power business transformation.
Our multi-model, multi-domain innovative master data management platform supports large data sets across any usage scenario, implementation style, or industry. With that said, we’ve had our hand in about every type of MDM implementation. In this article, we outline the four main MDM implementation styles and usage scenarios with the benefits and drawbacks of each. MDM implementation styles differ mostly on whether you choose to control data in a central hub or synchronize multiple hubs through coordinated updates, and that decision depends on how you want to use MDM.
Operational MDM versus Analytical MDM Usage Scenarios
Before getting into MDM implementation, you first have to establish how your business hopes to use your data. Do you just need clean, coordinated data for reporting (analytical MDM), or do you want validated data to help inform business processes in real time (operational MDM)? While most businesses want to work toward operational MDM, analytical MDM requires a much less invasive implementation architecture.
Analytical MDM
Analytical MDMs enable data for business intelligence and reporting. The MDM gathers and cleans data at a central hub, prepping it to fit the specifications of downstream applications. At its most basic, an analytical MDM creates master records and master IDs that connect to analytics, reporting, or data science applications, and it may or may not communicate any changes and insights back to data sources or to master profiles.
When used as an analytical context, the master data management in Reltio’s platform not only provisions master records and master IDs to downstream analytical applications and data science platforms, but also matches dimensions of interaction data from multiple sources before feeding the clean and consolidated data downstream. In addition, businesses gain key relationship insights before connecting the insight-ready data to various analytical or data science tools. Any insights from the data science platforms or the next best actions can be brought back to the master profiles.
Operational MDM
Operational MDMs inform everyday business processes. Data is sourced and leveraged in the same places, with product information and customer data being the two most common uses. Since operational MDM informs real-time decision making, it requires a much higher level of reliability, performance and accessibility.
95% of customers use the Reltio platform to create a single source of truth data for downstream systems like CRM, ERP, and procurement. All of these operational uses require an MDM platform that makes information available in real-time to validate, verify, and resolve matches, and Reltio averages less than 300 milliseconds to complete these processes and make data available to operational systems.
Multi-Domain MDM
If your business has multiple legacy MDM systems, chances are they are single domain. Five years ago, most companies were implementing separate, single-domain MDMs for enterprise functions like customer and product data, because out-of-the-box MDM solutions were specifically tailored to certain domains or industries.
But now these businesses have ended up with the exact problem they adopted MDM to solve: multiple, fragmented data sources that organize and define data differently. Not only are they paying double or more for MDM start-up costs and maintenance, but they’re also missing key connections across functions that can power business transformation.
Multi-domain MDMs bring together customer, product, supply chain, asset, employee, location, and other sources. They can make connections across domains, like bringing supply chain and location information together for procurement officers or customer and product data together for marketers. Without them, it’s impossible to make the connections you need to understand your customer holistically.
Reltio’s connected graph technology links master profiles of different domains to each other, and users can pivot from a customer profile to a product profile to a supplier profile with the click of a button. For example, in the healthcare industry, Reltio simultaneously implements models for Health Care Practitioners (HCP) and Health Care Organizations (HCO) giving sales teams reliable profiles under each model and signaling which doctors are affiliated with which hospitals.
4 Core MDM Implementation Styles
Knowing whether your business wants to employ analytical MDM or operational MDM and understanding the domains you want to involve should help you better assess which of the following MDM implementation styles best suits your needs.
1. Consolidation:
In a consolidation MDM implementation, source systems feed data into a central hub to create golden records. The golden records flow downstream to reporting applications and warehouse business intelligence, but the source system records do not change.
Consolidation is mainly used for analytical MDM and in cases when overwriting the source system records could cause regulatory complications. It’s an efficient MDM implementation style to get clean, matched, integrated data in a central hub if your business doesn’t need to make cross-functional connections in real-time. Reltio customers use consolidation, among other styles, for GDPR and CCPA compliant customer engagement.
- Biggest benefit: Efficient enterprise-wide reporting
- Biggest drawback: Does not update original source records
2. Registry:
The registry MDM implementation style works best for a large number of source systems with their own rules and structures, which can make determining information authority difficult. It assigns unique global identifiers across all systems to match records and merge duplicates. By indexing master data in real-time it can provide up-to-date views of products, customers, or the other domains in the MDM, but the indexing often causes latency.
Like the consolidation style, registry MDM implementation does not feed updated records back to the source systems. It’s most often used for analytical MDM since there is an emphasis on application-to-application integration. Most Reltio customers go beyond registry style since Reltio’s agile data model and limitless storage and performance are not a constraint.
- Biggest benefit: Indexes multiple unstructured source systems
- Biggest drawback: Provides a read-only view of records
3. Centralized:
In a centralized (sometimes called transactional) style, the MDM authors the master data and disseminates it to other systems or applications. This MDM implementation style works best in high control, top-down businesses, and requires the most change to your application infrastructure.
Centralized style makes data security and data maintenance much simpler and serves as a strong operational MDM for common services or workflows. But it requires everyone in the business to adopt a new data management system. Reltio’s comprehensive real-time operational APIs and business-friendly UI enable users to approve and create data in a central system before it’s distributed to downstream systems.
- Biggest benefit: Secure, accurate, and complete data
- Biggest drawback: Largest, most intrusive set-up
4. Coexistence:
Coexistence style MDM implementation creates a consolidated data hub that then feeds updated records back to sources. This style is the gold standard for large-scale distribution models and in businesses with a core need to mirror data.
In one sense, coexistence offers the best structure for constantly updating data as needed in an operational MDM. But it also poses the greatest risk over control and security. Reltio realizes the full potential of coexistence style by leveraging comprehensive master data governance controls with built-in workflows to connect bi-directionally with applications such as Salesforce.
- Biggest benefit: Allows data creation on multiple systems
- Biggest drawback: Complicated to deploy and requires constant data cleansing
Conclusion
Winning in the experience economy means leveraging your MDM for much more than creating and maintaining a single version of truth across the organization. Best-in-class businesses use their MDM platforms to power real-time operations at scale, improving data quality with every record touched. But at Reltio we realize that the best-for-your-organization MDM implementation style depends entirely on your business goals and structure.
Reltio’s Connected Data Platform can be implemented in any MDM implementation style and can evolve from one style to another alongside your business requirements. We provide full support during any MDM implementation, including managing reference data and customer, product, and supplier hubs to create the best versions of the truth for operational systems and coexistence with legacy MDM systems.
Related Content:
Master Data vs. Reference Data
