Graph-Centered AppDev Series [Replay], Episode 3: Master Data Management (MDM)
Will EvansVP Consulting
- Why does the advent of graph database like Neo4j make Master Data Management more effective than it ever has been
- Examples of how to get started with your graph for your Master Data Management project
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In this episode replay we cover how effective Master Data Management (MDM) must extend beyond the basics of entity resolution to include the entire data lifecycle. To stay truly in control of your data you need to understand not only that two entities may represent the same thing, but also what is the source of these two entities? Have they changed over time? Who or what changed them? Graphs enable you to answer all of these questions and more. Join us in the third episode of our year long series as we highlight the best practices for using graphs to truly master your data.
Format: Presentation/discussion with some show and tell. This particular event is not a hands-on session, but if you think that would be a good idea for future events, please reach out to firstname.lastname@example.org.
Audience: The session should be interesting to anyone who has experienced the shortcomings of rdbms-based applications, and is in a position to impact their organization’s direction for new and re-platformed apps. Graph database enthusiasts, IT managers, architects, DBAs and AppDev technical people will benefit uniquely from this content.
All episodes in the series are the 1st week of every month on Tuesdays @ 12:00PM ET.
No specific technical skills required, but to derive the most value from this session, you should at least understand appdev and database concepts and their implications.
Graphable delivers insightful graph database (e.g. Neo4j) / machine learning (ml) / natural language processing (nlp) projects as well as graph and Domo analytics with measurable impact. We are known for operating ethically, communicating well, and delivering on-time. With hundreds of successful projects across most industries, we thrive in the most challenging data integration and data science contexts, driving analytics success.
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