Master data management (MDM) is the process of ensuring an organization is always working with, and making decisions based on, one version of accurate, up-to-date data — often referred to as a “golden record.”
Sounds simple, but in modern business environments, awash with constant streams of data, master data management may be one of the most complex business challenges. And for an organization to achieve good data health – that is, data that supports effective, timely decisions and business objectives – a “golden record” like what MDM can provide is key. Ingesting critical data from diverse sources and presenting it as one constant, reliable source for verified, real-time information takes a combination of know-how, tools, and often a strategic partnership.
MDM: What is Master Data Management? now.
Tools, Processes, and Best Practices
Master data definition and benefits
Master data is data with a standard definition that defines and describes core business entities. This data is separate from reference data, which refers to datasets that are used to classify or categorize other data (such as units of measurement, exchange codes, currencies, and country codes).
What data should you manage?
The four general master data domains you should manage are product data, location data, customer data, and other data. Individual master data domains can be managed through multiple MDM or product information management (PIM) tools. However, multidomain MDM tools allow organizations to wrangle all of this master data in a single, unified data management platform.
When enterprise data is kept healthy across systems and departments, MDM can deliver substantial benefits, including:
1. Lower total cost of operations
Consider all the aspects of your business that rely on healthy data for peak performance:
- All applications and their dependencies
- Employee operations, from production to human resource events
- Data stores, including hot (working) and cold (archival) information
- Inventory schedules, supply chain logistics, and ordering protocols
- And more
Even the slightest discrepancy in any of this data from one system or department to another can trigger a chain reaction, rapidly impacting all associated info, increasing operating expenses, and jeopardizing the organization’s business. And this unhealthy data usually wreaks more havoc than it initially appears. According to one Harvard Business Review study, only 3% of the data quality scores could be rated “acceptable” using the loosest possible standard, and on average, 47% of newly-created data records contain at least one critical error.
On the other hand, healthy data can help businesses optimize operations, slash expenses, and deliver trustworthy insights for business decisions.
2. Reduced architectural bloat
Decreasing lost business isn’t the only way MDM impacts the bottom line. The cost of running and supporting network architecture — whether on-premises, hybrid, or in the cloud — is directly impacted by the amount of resources used. This includes storage space, processing time, and network throughput.
By using a platform to unify data assets into one single source of truth, you can significantly reduce the resources required to maintain multiple data sources, cut IT operational costs, and eliminate the accessibility challenges caused by data silos.
3. Faster deliveries
MDM is a core consideration for modern development approaches like continuous delivery, DevOps, rugged DevOps, and other design architectures that require shared and reliable data.
With a healthy data reservoir feeding development teams, apps and improvements race through the delivery pipeline far faster. This means MDM discoveries unearthed today can potentially be put to work in software immediately, rather than after an extended review and recode process.
4. Simplified compliance
A major challenge in the modern digital business world is compliance. HIPAA, PCI, CCPA, GDPR, and other regulatory frameworks create rapidly changing conditions that companies must meet in order to stay in compliance. Compliance alone can be (and is, in larger organizations) a full-time pursuit.
MDM can help take the grind out of performing mandatory compliance reports and audits by ensuring organizations meet all standards for verifiable, secure data integration, and successfully implementing MDM plays a critical role in many data governance frameworks.
5. Improved customer experience
As the saying goes, time is money. In a digital world that moves at the speed of modern business this has never been more true, especially when it comes to your audience’s time. MDM provides a previously unavailable opportunity to interact with your customers during every step of the transaction process — and improve your performance based on real-time feedback — by eliminating inconsistencies and errors that impact product delivery, from first app interaction through shipping, delivery, and feedback.
6. 360-degree view
A modern, cloud-based MDM process creates a complete, real-time single view of each customer. MDM creates a “golden record” that gives marketers up-to-date and accurate information for segmentation, web personalization, and a better understanding of the customer lifecycle.
7. Actionable business intelligence
Developing a clear and current picture of all business operations means decision makers can zoom in on problem points in business processes or pull back to a satellite view to see where national or global trends are impacting your business.
Since data is the foundation and life support of digital environments, the implications for MDM in any environment are as limitless as the data itself.
Master data management in the cloud: 4 key challenges
With the cloud, and the myriad opportunities it presents, comes an increasingly large number of MDM-related pitfalls that can occur in a public or hybrid cloud environment. Here are four critical challenge areas to address early:
- Account for wildly disparate data types. With all the devices — virtual and physical — involved with keeping customers engaged, no one data storage type will be sufficient for MDM. Structured and unstructured data will flow to and through an organization’s management tools, and those tools must be flexible enough to accommodate all of it.
- Security! First, foremost, and always in modern digital environments, security must be the prime directive. If the advantages of MDM stem from maintaining a central source of truth from which an organization can operate, then inbound risks and threats targeting that source have the potential to stop operations dead in their tracks. Hacks, malware, and even ransom attacks can and will be the result of MDM solutions that don’t keep security first.
- Governance. MDM unleashes great potential power, but the responsibility for managing it is equally monumental. Through advances in areas like automation and machine learning, many of the interactions used to sustain an MDM solution can occur automatically in the background, but it’s still up to business leaders to decide which data is heavily weighted and how to interpret business intelligence. The right governance approach codifies not just the scope of the data but also who keeps and interprets it. This is the difference between just having a single source of truth, and putting it to powerful use.
- Expertise. Finding the right mix of experience and eagerness to learn quickly is probably the biggest MDM challenge many organizations face. Unfortunately, many businesses lack the internal knowledge for crafting a holistic solution that’s customized to meet their needs. Training and development versus outsourcing is a decision to address early.
With these challenges in mind, you can evaluate which of the most frequently used designs best fits your needs — and, just as importantly, comes with pricing that fits your budget.
MDM: What is Master Data Management? now.
Tools, Processes, and Best Practices
Which master data architecture is right for me?
No single MDM strategy fits every need; however, one of MDM’s advantages is its flexible, customizable approach to managing and governing your master data repository. That said, there are four general architectures into which initial MDM designs fall:
1. Registry Style MDM
In this approach, MDM works with abbreviated records, or “stubs,” that detail the data’s source, current location, and more. Registry style is the fastest and least expensive architecture to deploy because it minimizes the amount of data actually moving through MDM tools, and instead consolidates stubs into a working repository.
The disadvantages of registry style include higher latency inherent in gathering and comparing master records with remote device information. Additionally, registry is a one-way collection, and changes made at the master level do not propagate to remote sources like CRM, ERP, and other systems, resulting in inconsistencies between data in the master source and remote sources.
2. Consolidated Style MDM
A consolidated architecture is similar to a registry architecture, but adds the functionality of actually moving data from sources to the master repository.
This approach is popular in environments where latency is expected, and consolidation generally takes place during scheduled batch process windows. However, as with the registry style, data in the master repository is not synchronized with downstream sources.
3. Coexistent Style MDM
This architectural approach takes consolidated MDM a step further and adds the critical step of synchronizing master data back down to the sources, creating a master record that ‘coexists’ in both the prime repository and at the individual system level.
This involves a more complex workflow and still comes with high latency, as data needs to be collected and disseminated back downstream via separate batch processes. This architecture is common with small and mid-sized companies that can afford to synchronize master data multiple times per defined period.
4. Transactional Style MDM
The most complete architectural approach, transactional style MDM is also the most costly in terms of overhead. Master data is migrated from the sources to the master repository, where it is processed, cleaned, and standardized according to business rules, and then returned to the sources.
This style reduces latency through direct coordination between master and source, and comes with the advantage of enforcing data governance rules across the enterprise. However, it requires a high level of expertise and the right tools for custom coding to ensure proper flow and prevent flawed data from propagating across the environment.
It’s not uncommon for organizations to begin with one MDM architecture, and then evolve into another. The measure of a successful MDM build is the efficiency, speed, and consistency with which master data is moved and stored.
Master data management and service-oriented architecture
Master data management tools take on a new significance — and power — in the cloud through their interoperation with service-oriented architecture (SOA). When almost everything — including infrastructure — is virtualized, the costs of inconsistent or corrupt data can be crippling. Cloud-native MDM solutions provide service-oriented architectures (SOAs), including Internet as a Service (IaaS), to work from one source of truth, making enterprise-wide change consistency achievable in near real-time.
A core challenge of MDM in SOA is a data governance approach that standardizes data structure and rules between the repository and the host of remote systems, services, and software. Coordinating a working protocol for exchanging and overwriting data between different systems can be a daunting challenge for existing IT staff. That’s where partnering with a trusted expert simplifies the MDM picture.
Taking the Next Steps with MDM
Integrating a master data management solution in your data model can propel your organization to close the gap between delivered products and users to near-real-time, turning data environments into almost living organisms that react and respond to the modern business world.
A holistic platform like Talend Data Fabric gives organizations a single, secure point of control for data, while also providing profiling, enrichment, validation, and stewardship capabilities in a unified user interface, so both IT and business users can participate in building a healthier data environment.