Today’s enterprises are highly complex, relying on potentially hundreds of different business applications and systems across multiple departments and business units. Each of these applications and systems generates and consumes data, which can easily become fragmented, duplicated, or obsolete. Data that is conflicting, incomplete, or just plain wrong has no value to the business and, in many cases, can cause harm. A master data management strategy can help maintain accurate, consistent, and trustworthy data by providing a single source of truth across all of your organization’s data assets.
Before diving into the implementation details, let’s take a look at the “whys” behind adding master data management to your overall data management strategy.
The most fundamental reason to embrace master data management is that these processes will reduce data errors and increase data quality throughout your organization.
Master data management provides a unified view of critical business data with a single master dataset that includes attributes such as names, addresses, dates, customer ID numbers and technical system properties. Ensuring the consistency of this master data, which is used in transactional, analytical, and operational processes, means every application in every department is using the same data for everything from procurement and customer billing to forecasting and financial reporting.
Additionally, with the massive volume of data enterprises generate and consume on a daily basis, manual master data management is inefficient or even impossible. Automating these processes keeps data records up to date and synchronized across all subscribing systems and your data warehouse.
Historically, master data management was considered an IT responsibility. But now that organizations rely on data to drive essentially all business processes, an effective data management strategy requires the combined effort of both the IT department and the business. Unsiloing the effort helps ensure that all shared master data is accurate, consistent, and uniform by enforcing governance and accountability across systems.
Today, master data management encompasses all of the technologies, processes, and policies that ensure the consistency and accuracy of an organization’s master data. This synchronized approach creates a single source of truth for business data that is accessible from the multiple domains and systems within marketing, finance, sales, procurement, and any other department that depends on the trustworthiness of the data to operate efficiently.
Master data management is generally segmented into four core domains, each of which can be further subcategorized into entities based on industry or departmental focus.
These are the physical sites where work is performed in an organization, including geographic region and, more granularly, branches, facilities, franchises, and stores.
The product domain tracks the attributes associated with providing and delivering products and services, including product inventory, bills of materials, equipment, and media.
This includes all of the relevant information about the goods and services being sourced from the suppliers, such as procurement history, supply categories, inventory data, contract records, and purchasing records.
The financial domain is used to obtain an end-to-end view of profitability, including chart of accounts, profit centers, cost centers, and business partners (both customers and vendors).
A successful master data management strategy ensures the data within these domains is accurate, consistent, and complete across your organization, and your upstream and downstream subscribing enterprise systems.
Once you understand your “whys” and your “whats,” it’s time to focus on how to kick-start a master data management initiative in your organization.
There are five generally accepted best practices for getting your master data management program off the ground:
Working with a master data management partner you trust can help streamline all of the steps above and ensure your master data is centralized, integrated, validated, and governed properly across all applications.
Although there is considerable value in implementing master data management, there are a lot of moving parts to consider, and the process can be challenging.
For example, you need to consider your master data management model’s agility and fitness for use with your organization’s structure.
Standardizing (or harmonizing) master data across all departments is another hurdle to overcome if it’s even possible based on the requirements of the technology. It’s often difficult to get consensus on terms and definitions for attributes that may be used in different contexts.
A solid data governance framework can help with data standardization and rationalization but only if you have the framework in place before you start your master data management implementation.
One of the biggest challenges enterprises face is integrating a master data management solution with your existing EPM, CPM, ERP, or other business applications. Many of these applications bring a financial nuance that require customizations to properly display the data model in a graphical interface. In addition, integrations to financial applications are seldom supported by MDM solution providers, requiring organizations to spend countless hours architecting integrations that business teams have difficulty supporting.
Deploying a solution with out-of-the-box, code-free integrations with popular business systems such as Oracle EPM, OneStream, EBS, SAP, and Workday ensures even your nontechnical team members can get the full value out of your master data management strategy.
Businesses can’t afford to ignore poor data quality. Implementing a comprehensive master data management strategy and solution is the first step in creating consistent, accurate, and reliable master data across all of your data assets.
Download your copy of our e-book, Navigating the Enterprise Data Governance Landscape, to learn why master data management and data governance are the dynamic duo your data needs.