Strategies And Tactics For A Successful Master Data Management Implementation
Data Engineering Podcast
00:00
The Hard Part of M D M
M d m is both a way to manage data, but it's a technology. The process of merging records and having one version of the truth can be very hard if you don't have strong executive support. If you talk to a lot of elder statesmen like me who've been in data management a while, chances are pretty good they will have om some experience in a failed m d m programm.
Transcript
Play full episode
Transcript
Episode notes
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
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
- Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Tonic.ai does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try!
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
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
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
