As the torrent of information businesses create grows, the ability to manage it becomes ever more urgent. IFL Science estimates that the world creates 3.5 quintillion bytes of data every day, and 90% of the information created in the world's history was created in the past two years.
Data management was easier when IT controlled the creation, movement, and use of information inside an enterprise. Today’s free flow of data has made centralized control challenging. People have become accustomed to having information about anything available instantly and become dissatisfied when the information they need to make decisions and do their work isn’t available and useful immediately.
In a February 2023 report from HFS Research, 80% of senior executives said they trust their data, but when questioned about its usability, the results were much different. Only 60% say their data is usable. The gap points us to problems with compliance, system fragmentation, and a lack of coordination in data management.
Master Data Management (MDM) is a discipline and/or technology that supports business and IT working together to ensure the quality and availability of master data. MDM aims to provide uniformity, accuracy, stewardship, semantic consistency, and accountability of the master data assets.
The data management industry has attempted to keep up with the growth of information in a progression from databases through ERP, data hubs, hubs of hubs, data lakes, cloud storage, and emerging technologies like edge computing. But without disciplined management, democratized data becomes chaos. Master data management and data governance have emerged as the solution.
Until recently, master data management (MDM) was the domain of only the largest and most data-dependent organizations, but in today’s data-driven business world, it has become necessary for organizations of all sizes and types. Every business needs to manage information creation, flow, and consumption today.
Master data management and data governance aren't the same. Data governance is “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” It can refer to governing bodies, rules, accountabilities and enforcement practices related to data.
Master data is the core data that is essential for running operations within a business enterprise or unit. It is data about core business entities such as customers, products, suppliers, and other business entities. Master data provides context for business transactions and represents a single source of truth for a company's most critical information.
The right time to act on master data management is before your data gets out of control, so we recommend not waiting for a crisis.
How do you know if you need to act now? Here are a few indicators:
Data governance is only one component of MDM, but it is a good place to start if it’s time for your organization to get control of your data. Governance gives you a framework for making decisions about data, so you have a meeting of the minds across your organization.
Data governance is only one component of MDM, but it is the essential first step.
Beginning by searching for the right technology is a solution looking for a problem, so start by defining the problem so you know what you need to solve. Then, work through the steps to implement a sustainable MDM program.
Model your essential business data and processes for common data. Identify data sources and define the metadata. Governance gives you a framework for making decisions about data, so you have a meeting of the minds across your organization.
Using your governance framework, identify your data sources, evaluate the data flow and transformation rules, and define master data quality requirements.
Now, it’s time for technology that satisfies the data requirements you defined in the first two steps.
Define and structure access rights, change management processes, and quality standards. Thoroughly test your processes, data availability, and quality.
We recommend a mindset of delivering the right data to the right people at the right time, presenting it in a way they understand.
Implement change management for the first iteration and evaluate the results to plan the next stage. Reiterate until you have rolled your program out.
If you aren't already headed in the right direction, we urge you to get started right away. The pace at which we create data will only speed up. The sooner you get control, the better.
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