Creating Master Data at Scale with AI

The Data Exchange Podcast: Sonal Goyal and Ben Lorica on master data, data preparation, entity resolution, data fusion, and more.

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In this episode of the Data Exchange, our special correspondent and managing editor Jenn Webb organized a mini-panel composed of myself and Sonal Goyal, founder of Aficx, a startup building machine learning based tools to help companies create holistic, trusted and consistent views of entities across data silos.

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[Image: Preparing Data for Analytics from Sonal Goyal, used with permission.]

Among the things we highlighted in our recent “2021 Trends Report: Data, Machine Learning, and AI” were foundational technologies including lakehouses and emerging metadata management systems. In this episode we discussed how companies address data silos to produce master data and trustworthy datasets that can be consumed by analysts and data science teams. This comes at a time when companies are using a multitude of software systems both on-premise and in the cloud. As Sonal explains:

    ❛ Master Data is essentially a unified view. What we do in data mastering, which is part of the data preparation is something like this: let’s say we have a customer who has bought multiple products from us. Now, typically, what would happen is that these interactions with the customer will be saved on the data management system residing in the department or product line. But when we want to build out a unified view, when we want to understand the entire customer journey, when we want to do personalization for them, when we want to do recommendations for them, maybe we want to do segmentation and understand how our customers behave and this is across the entire enterprise. We need to get these records of a customer together. We first build one single entity out of that, and that is the unified view of the customer, this entity can then actually be tied to behavioral data, and then your facts and dimensions come into play.

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[Image: Photo by Edu Grande on Unsplash.]