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August 17th, 2023  |  11 min read

Why modern master data management (MDM) is to MDM what lakehouses are to warehouses

As the volume and variety of data continue to grow, organizations face the challenge of effectively managing and utilizing all of their data assets for maximum business impact. Several solutions have emerged to address this challenge, including Data Lakes, Data Warehouses, Data Lakehouses, and Master Data Management (MDM) platforms.

Each of these systems has its own purpose and distinct role to play in data management. This means that data owners and users need to have a sound grasp of what each system is designed to do, its benefits and restrictions, and its relationship to other data management systems.

In this article, we’ll take a look at Data Warehouses and Lakehouses, and traditional and modern MDM systems in turn, evaluate their strengths and weaknesses, and discuss how these platforms have evolved in response to present data challenges.

How the Warehouse and the Lakehouse differ

A data warehouse and a data lakehouse are both essential components of modern data management strategies, but they differ in their approaches to handling and processing data. A data warehouse is a structured and centralized repository that focuses on storing well-defined, organized, and cleansed data, making it optimal for supporting complex querying and reporting tasks. On the other hand, a data lakehouse combines the strengths of a data lake's flexibility in handling raw, unstructured data with the performance enhancements and query capabilities of a traditional data warehouse. It offers a unified platform where both structured and semi-structured data can coexist, enabling organizations to efficiently store, process, and analyze data at scale while accommodating diverse analytical use cases.

One of the notable differences between a data warehouse and a data lakehouse is the skill set required to interact with and utilize each platform effectively. A data warehouse is typically designed with SQL-oriented querying in mind, making it more accessible to individuals with SQL skills. SQL is widely understood by data analysts, business intelligence professionals, and other roles familiar with structured data manipulation.

In contrast, a data lakehouse leverages the power of tools and languages like Python for data processing and analysis. Python offers greater flexibility in working with unstructured and semi-structured data formats, making it appealing to data engineers, data scientists, and analysts who are comfortable with programming and data manipulation beyond traditional SQL capabilities. 

Key Features of a Data Warehouse

  • Structured and Consistent Data:Data Warehouses follow a schema-on-write approach, ensuring consistency and simplifying querying and analysis.
  • Mature Ecosystem: Data Warehousing offers a mature ecosystem of tools, methodologies, and best practices.
  • Performance Optimization: Techniques like indexing, materialized views, and pre-aggregation optimize query performance.
  • Data Governance and Security: Robust mechanisms enable control over access, usage, and quality standards.
  • Reliability and Stability: Known for stability and reliability, improving data quality.
  • Historical Data: Enables trend analysis and longitudinal studies.

Key Features of a Data Lakehouse

  • Schema Flexibility: Lakehouses offer schema-on-read capabilities, accommodating diverse data types.
  • Cost-Effective Scalability: Built on distributed storage systems, ensuring cost-effective scalability.
  • Real-Time Analytics: Support for near-real-time analytics.
  • Support for Unstructured Data: Handling various data forms, enhancing analytical potential.
  • Integrated Data Processing: Combining batch and stream processing.
  • Advanced Analytics and Machine Learning: Facilitating integration with advanced analytics and AI.

Traditional versus modern Master Data Management (MDM)

In the same way that data lakehouses offer the flexibility of ingesting and storing data in its raw state alongside advanced scalability, flexibility, and integration capabilities, the MDM market has reached a similar inflection point.

Traditional MDM approaches typically involve centralized data governance, focusing on creating a single source of truth for core data entities like customers, products, and locations. This often entails lengthy implementation cycles, rigid data models, and top-down governance structures. In contrast, modern MDM embraces agility and flexibility by leveraging advanced technologies like Graph, Artificial Intelligence (AI) and Machine Learning (ML). It emphasizes a more holistic and distributed approach to data management, enabling real-time data synchronization, self-service data management, and adaptability to evolving data types.

Key Features of Traditional MDM

  • Data Governance: MDM systems need to include provision for the establishment and enforcement of data governance policies, and they must allow you to define data ownership roles and responsibilities. Ideally, they should also offer assignment of data stewardship roles and responsibilities, workflow management, and collaboration tools for data stewards to resolve data issues.
  • Data Quality: MDM systems must also provide robust data quality features in the form of data profiling to identify data anomalies and issues, data cleansing, validation, and enrichment to ensure accuracy, and monitoring and reporting on data quality metrics. In addition, they should be able to identify and resolve duplicate or redundant data records and include advanced algorithms for fuzzy matching and record linkage.
  • Data Integration: Every MDM system should be able to integrate with various source systems in order to collect, transform and map data. In traditional systems, however, this is often limited to structured data.
  • Data Modeling: When using a traditional MDM system, you will be expected to create and maintain your own data models, hierarchies, and taxonomies. You will also need to define and manage the relationships between different master data entities yourself.
  • Data privacy and protection: Understanding the journey your data has taken, how and when it changed, and who was responsible for those changes is vital for data protection and compliance purposes. All MDM systems need to be able to capture and store historical changes to master data, and audit and track data modifications as well as implementing role-based access to master data, and providing encryption, masking, and access control features.

Key Features of Modern Master Data Management

In addition to the above features, which should be seen as core to any MDM offering, modern – or augmented MDM – offers a whole lot more besides. Modern MDM systems are Cloud-native, can consume vast amounts of data from innumerable sources, and do a lot of the hard, manual work for you by taking advantage of new technologies like Graph databases, Artificial Intelligence (AI) and Machine Learning.

  • Empowering the business user: In the past, MDM systems could only be deployed and managed by IT and technical experts. This is odd, considering that data is a business asset that should be leveraged by every department in order to solve business problems. Modern systems like CluedIn were designed as low/no-code platforms from day one, but have taken this further by integrating generative AI in order to allow non-technical users to manage and use data as part of their everyday work life using natural language processing.
  • Data Modeling: One of the most frustrating parts of any MDM program has to be the time and effort taken to model and map data upfront. This process can take months, in some cases up to a year, and the chances are that once it is “ready” for use by the MDM system it will already be out of date. Platforms like CluedIn completely remove this headache by using Graph to surface the relationships between the data and model it for you. Only CluedIn accepts raw data in its purest, most unconditioned form.
  • Cloud-native: There are lots of MDM systems that claim to be Cloud-native. Most of them however were created prior to the advent of the Cloud and have been retrofitted to work in a Cloud environment. Does this really matter? Well, yes, it does. Unless your MDM platform is genuinely Cloud-native, you won’t be able to take advantage of genuine integrations with other Cloud services, and will continue to be hit with hidden costs, endless software updates, security patches, integration requirements, and scalability issues. CluedIn integrates with 27 Microsoft Azure services out of the box and is designed to work with the economics of the Cloud from day one.

There are many parallels to be drawn from the relationship between Data Warehouses and Data Lakehouses, and the relationship between traditional and modern MDM systems. In the end, all data management systems should exist to make it easier and more cost-effective for your data to work with the business and for the business. There is also one key difference – while Warehouses and Lakehouses were designed for users with specific technical skills like SQL and Python, the same is not true of MDM. MDM systems were always intended to be used by business and domain experts. However, to this day, traditional MDM systems have almost exclusively needed technical specialists to implement and manage. This is partly why it has been so difficult for MDM projects to successfully demonstrate business value. Modern MDM counters this issue by making it easy for almost anyone with basic computer literacy to interact directly with data. The closer the relationship between computer and data literacy becomes, the higher the likelihood of a truly data-driven business becomes.

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