Strategies And Tactics For A Successful Master Data Management Implementation
Data Engineering Podcast
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Graft Based Entity Resolution
Most emdiampla processes are running these days, and most emdian platforms utilize that form of matching. But where i see things going is there could potentially be a kind of a waterfall type approach to antity resolution in the future. Where you use graft for a first pass to support more marketing centre ces cases were close enough as good enough, right? So i could easily see a world where graft gets integrated into this space.
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Summary
The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics. In this episode Malcolm Hawker shares his years of experience working in this domain to explore the combination of technical and social skills that are necessary to make an MDM project successful both at the outset and over the long term.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Malcolm Hawker about master data management strategies for the enterprise
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving your definition of what MDM is and the scope of activities/functions that it includes?
- How have evolutions in the data landscape shifted the conversation around MDM?
- Can you describe what Profisee is and the story behind it?
- What was your path to joining Profisee and what is your role in the business?
- Who are the target customers for Profisee?
- What are the challenges that they typically experience that leads them to MDM as a solution for their problems?
- How does the narrative around data observability/data quality from tools such as Great Expectations, Monte Carlo, etc. differ from the data quality benefits of a MDM strategy?
- How do recent conversations around semantic/metrics layers compare to the way that MDM approaches the problem of domain modeling?
- What are the steps to defining an MDM strategy for an organization or business unit?
- Once there is a strategy, what are the tactical elements of the implementation?
- What is the role of the toolchain in that implementation? (e.g. Spark, dbt, Airflow, etc.)
- Can you describe how Profisee is implemented?
- How does the customer base inform the architectural approach that Profisee has taken?
- Can you describe the adoption process for an organization that is using Profisee for their MDM?
- Once an organization has defined and adopted an MDM strategy, what are the ongoing maintenance tasks related to the domain models?
- What are the most interesting, innovative, or unexpected ways that you have seen MDM used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working in MDM?
- When is Profisee the wrong choice?
- What do you have planned for the future of Profisee?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Profisee
- MDM == Master Data Management
- CRM == Customer Relationship Management
- ERP == Enterprise Resource Planning
- Levenshtein Distance Algorithm
- Soundex
- CDP == Customer Data Platform
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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