Implementation Fundamentals of Master Data Management
A well-known issue with legacy data platforms is what's known as the garbage in, garbage out (GIGO) obstacle.
As data began to flourish due to cheap storage options and increased digitization, data quality issues and fragmentation proliferated, creating GIGO issues that affected an organization's business intelligence. At the same time, Customer Relationship Management and Customer Data Integration practices assisted with back end reconciliation and scrubbing. They also required introducing more processes and systems, causing data to be further pushed away from a single source of truth.
Thus, there was a serious need for a more holistic technique and approach to diminish the GIGO factor by addressing data quality issues and challenges. As a result, organizations began implementing Master Data Management (MDM) programs into their data management practices which is a relatively new concept, and consequently, leaders of this initiative try to fast track its progress and outcomes.
The fact of the matter is a successful MDM program must contain four implementation fundamentals:
- Data Governance
- Data Stewardship
- Data Quality Management
- Data Access Management
Data governance is the fomal management of people, process, technology, and data at a high-level. A successful data governance program will enable an organization to integrate, secure, optimize, and utilize data to drive insights. While it's possible to initiate a data governance program without an MDM program, you cannot have an effective MDM program without data governance. It acts as the glue, and without data governance, MDM cannot succeed. You can learn more about the subject of data governance from our previous articles on the topic.
The data stewardship's main objective is to utilize an organization's established data governance guidelines and implementations so that any administered data assets adhere to organizational policy and regulatory obligations. In other words, data stewards constitute the vehicle that drives data governance. With that said, data stewards, in any MDM program, cannot be limited to just agents for data governance policies and techniques. This role also needs to be closely tied to the touchpoints and users of the master data in which the activities data entry, usage, and quality control can be most influenced.
Data Quality Management
Data quality management requires an organization to establish frameworks for proper data cleansing. These initiatives can be accomplished with continuous training, proper communication, effective collaboration, and an adaptive model that can deliver quick results despite phases of constant change. Data quality management is likely the most critical reason companies tackle MDM and require the highest IT and business collaboration.
Data Access Management
Data access management is the control and monitoring of access to data with the ultimate goal of helping to prevent potential misconduct, intentional or accidental. Typically, this exists as part of an IT-managed process and is mostly overlooked as a component of business discipline. However, information protection, and the ability to manage compliance and other risk factors more tightly can be greatly enhanced if the practice of data access management involves the business in the process. A breach of vital data can be detrimental to the development of an enterprise and its customers and business users.
Successful Master Data Management Implementation with Data Ideology
In conclusion, when an organization is seeking data management answers, Master Data Management programs and projects often give way to technology solutions. Unfortunately, these organizations learn the hard way that while technology is a good start, any data initiative that doesn't incorporate an MDM program will likely fail.
An effective MDM program must include the successful implementation of the four aforementioned fundamentals I touch upon in this article in some way, shape, or form. The benefits of such a program and a modern data platform such as Snowflake's Data Platform can consistently deliver trusted data in a timely manner, which is any company's ultimate objective.
At Data Ideology, our industry experts will work with you and provide you with quality resources to determine the solutions that will best support your company's data management needs. Then, we help you implement the best practices and standards to save you time, money, and headache for years to come. These best practices also help ensure that the data is safe and secure, which also protects the company's best interests and outcomes. Contact us today to learn more.
Written by Mike Sargo
Co-Founder & Chief Data and Analytics Officer at Data Ideology
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.