6 Questions about Master Data Management
1. What is Master Data?
Master data is usually defined as the set of core entities within an organization, depending on the industry it may include customers, prospects, suppliers, sites, hierarchies etc.
The purpose of managing this data is to assure a consistent definition of these business entities and data about them across the organization’s multiple systems, establishing a standard definition for business-critical data that represents a single source of truth.
2. When to consider Master Data Management?
Most organizations that are planning initiatives like updating its data warehouse to enable near real-time data, creating a big data environment to support analytics, increasing digital capabilities, migrating data and analytics to the cloud, business intelligence, big data analytics, machine learning or artificial intelligence.
It’s important to understand that Master Data is a key component for the success of any of these initiatives.
- Increasing data volumes from more and more sources.
- The gap between business and IT, leading to often contradictory strategies.
- Difficulty in managing a siloed data ecosystem.
- Difficulty to identify and define data across sources.
- Lack of standard business and data management rules and data protection policies.
- Rising data-security concerns around providing employees with remote access to data.
- Difficulty to identify, cleanse, standardize, and curate data for sharing.
- Existence of duplicate, erroneous, inaccurate, and incomplete data.
- Negative impact of regulatory requirements, either data protection regulations, or industry regulations.
3. What are the benefits of MDM?
Master data management provides various benefits to the efficiency and success of a manufacturer, either directly or as a support component for many of the digital transformation initiatives, enabling the capability to:
- Base decisions on a consolidated and consistent enterprise level view of all the critical entities
- Deliver consistent, quality data across the organization, from supplier to point of sale.
- Reduce risk of data discrepancies between different data silos, channels (internal or external)
- Create a single, central repository for gathering and integrating data across the entire value chain
- Reduce time to market by effectively managing information as it flows across the organization and external channels.
- Establish a unified view of process and data
- Achieve a clearer knowledge of the full value chain
- Ensuring consistency and traceability for all core business data, across all channels
4. What are the major obstacles to MDM?
- Data silos,
- Volume of data,
- Incompatible systems,
- Integration cost,
- Data redundancies and inaccuracies,
- Poorly defined use cases,
- High data maintenance,
5. What are the business drivers for MDM?
- Compliance and regulatory reporting,
- Data privacy,
- Mergers and acquisitions (M&A),
- Synergies for cross-selling and up-selling,
- Legacy system integration,
- Customer satisfaction.
6. What are the key success factors for a master data management Implementation?
- Data governance — Having a data governance framework in place prior to initiating a master data management initiative, even if limited to the same scope as master data.
- Change management — Master data management touches every area of the organization, it’s critical to assure the awareness of the importance of this data across the organization.
- Process management — Another key component, closely related with data governance.
- Quick wins — A full master data management implementation can be a very long process, it’s critical to identify and prioritize the most important and strategic data.
