Master Data Management 101

Have you ever found a duplicate customer record in a business system like a CRM (customer relationship management) and wondered which version was correct? If you have, you already know why Master Data Management is necessary. When you add another version of the record in an ERP (enterprise resource planning) system and yet another version in a team spreadsheet, finding the correct customer data can become impractical and time-consuming.?

That’s where Master Data Management comes in.

What Is Master Data Management?

Master data management (MDM) is the process of creating a single master record (or golden record) for each person, place, or thing across the enterprise. MDM as a practice includes data governance, or the processes and guidelines that ensure data accuracy. MDM solutions automate how data is handled between business systems to keep track of internal and external data sources and unify them into one single source of truth.

Types of master data used in the chemical, ingredients and polymer industries include:

• Customer data
• Sales and marketing data
• Employee data
• Product data?
• Manufacturing data
• Regulatory data
• Safety data
• Supply chain data
• Financial data

Key Benefits of MDM by Department

Master data management is not just an extra but a necessity when you have data from multiple sources. MDM is the solution that ensures the data flowing into systems supporting functional areas of the business is clean and harmonized. Procurement, digital initiatives, finance, sales and product management, technology and regulatory teams all rely on data to function. Clean, harmonized data unlocks opportunities and fuels business growth.?

Procurement

MDM enables procurement to aggregate demand within and across regions. This expands the procurement team’s ability to optimize sourcing and leverage purchasing power. MDM does this by standardizing raw material and ingredient names. Analyzing opportunities for optimization is easier when you can see the full picture.

Digital Initiatives

When MDM is in place, digital teams can build and maintain digital experiences at scale. Access to comprehensive, centralized product data is the critical first step to creating an experience where customers can browse, search and filter the entire product catalog. Well-managed product data is also foundational to developing rich product pages on a website.

Finance

With a single source of truth for all data, finance can accurately report performance at the product level within and across regions. More accurate forecasting is also possible thanks to the ability to see historical performance data at the product level.

Sales

Sales teams can promote the entire product offering in their markets and territories with easy access to centralized product data. This also means cross-selling and recommending the right product to customers is more manageable.?

Technology

Clean, consistent product and SKU names will feed business systems like CRM and ERP. This will make it easier to view pipelines in CRM and report on performance from the ERP, and it will also simplify IT management of these systems.?

Regulatory and EHS

Teams get easy access to the most recent compliance data and product documentation, allowing them to be more responsive to customers. MDM also provides structure around data maintenance for creating audit trails and tracking the most recent revision dates on product data and related certifications.

Key Components of MDM

MDM includes:

• Data governance
• Data quality management
• Data integration
• Master data modeling

Data Governance

Data governance is the process of ensuring data is secure, private, accurate, available and usable throughout its lifecycle. An example of data governance is security and access control. A company may keep data on manufacturing processes for a product. Only people directly involved in manufacturing that product should have access to the data.?

Establishing data policies and standards is a key part of data governance. Policies are the guidelines that ensure an organization’s data are managed consistently. A policy may also outline the procedures for specific scenarios such as a data breach. Standards define expectations around quality and adherence to policies.

Clearly defined roles and responsibilities are another critical ingredient of a complete data governance plan. This decreases confusion, especially when a team changes.

Data Quality Management

A solid master data management plan ensures the accuracy, consistency and completeness of master data. It also includes strategies for data cleansing and enrichment.?

For example, a schedule is set for a subject matter expert (SME) to review product master data. The SME reviews the data, editing or removing outdated data. Then, the SME can add supplemental data or related product documents to the product master data to enrich it.?

Continuous monitoring and improvement of data quality keeps master data accurate and complete.

Data Integration

Data integration is the process of integrating data from various sources. This helps you connect the dots between systems that use master data such as CRM, ERP and BI tools.

Real-time data synchronization between systems reduces friction in business processes, freeing up associates’ time and energy to be more strategic and ultimately provide a better customer experience.?

Integrating data across business functions, systems and regions is a challenging project.??

Data integration challenges include data quality, consistency, duplication and large volumes of data. Chemical, ingredient and polymer companies may need help with consistency in naming conventions and formats and the presence of duplicates.

Master Data Modeling

Master data modeling is the process of diagramming how data is used in business processes. Having this model can help businesses achieve their MDM goals more quickly. To build a master data model, start by mapping the relationship between departments, departmental software systems and the hierarchical structure of master data elements. For example, a master data model would show how product data (a master data element) is used by customer service and their software systems, plus how the supply chain team uses that data.

Four Steps to Implement Master Data Management

1. Start by assessing organizational readiness. You will need to understand the current state of your data and managerial processes to know how much change is necessary to achieve the transformation.?
2. Next, select an MDM tool purpose-built for the chemical, ingredient and polymer industries. Knowde has connected more chemical data than anyone else in the world. The Knowde AI Knowledge Engine references data from 8,000+ chemical, ingredient and polymer suppliers and distributors.
3. Plan and execute MDM implementation with a trusted partner. Knowde has perfected the process of ingesting, harmonizing and cleaning data. We can finish an MDM implementation within weeks, which would take years for others to complete.
4. Follow change management best practices for successful MDM adoption. We do the heavy lifting on the technical side so you can focus on stakeholder engagement and business processes.

Real-World Applications of Master Data Management

One Knowde customer, a $50M specialty chemicals distributor, faced significant challenges managing its master data across product, customer and vendor data.

Challenges They Faced

• A massive ERP consolidation project necessitated harmonizing all data sets into a consistent format.
• Inconsistencies in naming conventions, formats and the presence of duplicates.
• The company needed to be able to syndicate clean, accurate data into their ERP and critical business systems.

Solution

The company saved a year’s work, resulting in lower implementation costs and faster time-to-value.

• A new, consistent global product hierarchy was established, and all product documents were organized.
• ERP system is now fed with clean, consistent data and they can maximize the efficacy of their software investments.
• Faster and cheaper than they could have done themselves or with their existing agency.

Future Trends in Master Data Management

MDM technologies have evolved from the domain of data scientists to touching every part of enterprise business across industries. Integration with emerging technologies like AI and machine learning will empower companies to manage ballooning volumes of business data.?

Predictions for the future of MDM:

1. Cloud-based MDM platforms
2. Integration of AI/machine learning
3. Integration of IoT (internet of things) devices
4. Dispersed / blockchain data storage

Conclusion

Master data management is crucial for businesses dealing with data from multiple sources. It ensures clean, harmonized, and accurate data, unlocking opportunities for business growth and optimization across various functions. Components such as data governance, data quality management, data integration and master data modeling play pivotal roles in maintaining accurate and consistent data across the organization.