The Who, What, When, Where, Why (and How) of Master Data Management
Data not only defined the last decade but will have a critical impact in the years to come. From serving as the fundamental building block of enterprises to becoming more valuable than oil, data has set the precedent for multiple revolutionary technologies such as cognitive intelligence and deep neural networks. But a deluge of data can wreak havoc if not managed properly.
Redundant, faulty or inconsistent data may not only lead to more storage costs but can lead to setbacks owing to huge losses or fines, often discovered by businesses much later after the actual impact. For instance, if the accounting team has redundant data, suppliers may be paid twice during invoice processing. How can businesses avoid such costly errors? The answer lies in data management and more specifically, master data management (MDM).
Before getting into the intricacies of implementing an MDM framework, let’s look at what MDM is and what it’s not. At its core, MDM is a framework—and not a software—that focuses more on identifying data than measuring or processing the same. It is a level above transactional data that contains the actual information needed for business functions. While product names, vendor lists, and branch details are good examples of master data, invoice numbers, employee personal details, and customer addresses, are considered ‘transactional data,’ which again falls under the category of master data.
MDM is a technologically driven discipline, comprised of tools and processes that define and manage a business’ most critical data assets. By creating a unified master data repository or a “single source of truth,” MDM establishes authority over data. It enables businesses to achieve accuracy, consistency, and completeness of data across the organization, as well as among partners, vendors, and customers. As the name suggests, MDM is all-encompassing, including analytical and reference data, and goes a long way in helping businesses arrive at efficient decision making, streamlined operations, and better risk management.
One of the biggest myths about institutionalizing data management strategies is that it is best left to IT teams. Contrary to popular belief, business stakeholders must get involved in orchestrating the change and organize it centrally to reap maximum benefits. In addition to the IT administrator who is responsible for the design, setup, and management, a data governance council must be formed. This committee will chart out data definitions, access rights, standards, and quality rules among other things. To enforce these policies, a data steward team is set up, which then takes care of carrying out data cleaning, fixing, and management.
Establishing an MDM framework essentially helps businesses collate all their digital data records into one master file, ensure data quality and integrity, centralize information and break down silos, along with preserving and governing data. Most importantly, organizations can do away with data duplicates and redundancies that can lead to hefty costs. For instance, any kind of inconsistency, such as repetition, overlap, missing records in product SKUs, order numbers, or customer data, can have catastrophic implications on the business.
There are certain wakeup calls that can be a sign that an organization needs an MDM setup. One of the most alarming ones is when the customer service department of a company is flooded with complaints. Across the value chain, the source of problems could be anything, ranging from duplicate entries, inaccurate data, a loosely defined data governance policy, or a lack thereof, resulting in botched up orders or delayed product launches. Additionally, when creative marketing initiatives fail to score the expected rate of conversions, it could be due to analytics functions acting on inconsistent data sets.
Some of the most common sources of master data are unstructured data (including social media, emails, etc.), metadata, transactional data (e.g., cross-channel interactions, etc.), reference data, hierarchical data, master data, customer data, product data and supplier data. The complexity and impact of master data in orchestrating streamlined operations is immense. When it comes to identifying which parts of the data ecosystem should be included under the master data umbrella, a few criteria, such as reusability, cardinality, value, and volatility, can be assessed.
At the outset, businesses should set up the governance committee by including members from both the IT team as well as the business stakeholders. Once the guidelines, KPIs, and best practices are defined and agreed upon along with the data that has to be included in the framework, the MDM platform can be tailored and customized to fit the bill. Choosing a solution that has flexible data models, high integration capabilities, and scope for scalability is essential.
What to Expect from MDMs of the Future?
In the times ahead, MDM will form the cornerstone for businesses in their effort to establish an “across-the-board” trusted view of information about customers, citizens, employees, patients, or products.
What was once adopted only by a select few industries like finance and healthcare, is now widely being considered by business leaders in other verticals such as retail, manufacturing, and telecommunication. With more sophisticated technologies such as conversational AI-based chatbots, smart devices, and VR/AR tools making their rounds among consumer-facing communication channels, there will be an enormous surge in enterprise data types. MDM strategists of the future will have to keep these in mind while designing their frameworks to cover all the bases, to account for cleaner and consistent data sets.