Aligning Business and Data: The Essential Role of Data Modeling
Summary
In this episode of the Data Engineering Podcast Serge Gershkovich, head of product at SQL DBM, talks about the socio-technical aspects of data modeling. Serge shares his background in data modeling and highlights its importance as a collaborative process between business stakeholders and data teams. He debunks common misconceptions that data modeling is optional or secondary, emphasizing its crucial role in ensuring alignment between business requirements and data structures. The conversation covers challenges in complex environments, the impact of technical decisions on data strategy, and the evolving role of AI in data management. Serge stresses the need for business stakeholders' involvement in data initiatives and a systematic approach to data modeling, warning against relying solely on technical expertise without considering business alignment.
Announcements
Parting Question
In this episode of the Data Engineering Podcast Serge Gershkovich, head of product at SQL DBM, talks about the socio-technical aspects of data modeling. Serge shares his background in data modeling and highlights its importance as a collaborative process between business stakeholders and data teams. He debunks common misconceptions that data modeling is optional or secondary, emphasizing its crucial role in ensuring alignment between business requirements and data structures. The conversation covers challenges in complex environments, the impact of technical decisions on data strategy, and the evolving role of AI in data management. Serge stresses the need for business stakeholders' involvement in data initiatives and a systematic approach to data modeling, warning against relying solely on technical expertise without considering business alignment.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Enterprises today face an enormous challenge: they’re investing billions into Snowflake and Databricks, but without strong foundations, those investments risk becoming fragmented, expensive, and hard to govern. And that’s especially evident in large, complex enterprise data environments. That’s why companies like DirecTV and Pfizer rely on SqlDBM. Data modeling may be one of the most traditional practices in IT, but it remains the backbone of enterprise data strategy. In today’s cloud era, that backbone needs a modern approach built natively for the cloud, with direct connections to the very platforms driving your business forward. Without strong modeling, data management becomes chaotic, analytics lose trust, and AI initiatives fail to scale. SqlDBM ensures enterprises don’t just move to the cloud—they maximize their ROI by creating governed, scalable, and business-aligned data environments. If global enterprises are using SqlDBM to tackle the biggest challenges in data management, analytics, and AI, isn’t it worth exploring what it can do for yours? Visit dataengineeringpodcast.com/sqldbm to learn more.
- Your host is Tobias Macey and today I'm interviewing Serge Gershkovich about how and why data modeling is a sociotechnical endeavor
- Introduction
- How did you get involved in the area of data management?
- Can you start by describing the activities that you think of when someone says the term "data modeling"?
- What are the main groupings of incomplete or inaccurate definitions that you typically encounter in conversation on the topic?
- How do those conceptions of the problem lead to challenges and bottlenecks in execution?
- Data modeling is often associated with data warehouse design, but it also extends to source systems and unstructured/semi-structured assets. How does the inclusion of other data localities help in the overall success of a data/domain modeling effort?
- Another aspect of data modeling that often consumes a substantial amount of debate is which pattern to adhere to (star/snowflake, data vault, one big table, anchor modeling, etc.). What are some of the ways that you have found effective to remove that as a stumbling block when first developing an organizational domain representation?
- While the overall purpose of data modeling is to provide a digital representation of the business processes, there are inevitable technical decisions to be made. What are the most significant ways that the underlying technical systems can help or hinder the goals of building a digital twin of the business?
- What impact (positive and negative) are you seeing from the introduction of LLMs into the workflow of data modeling?
- How does tool use (e.g. MCP connection to warehouse/lakehouse) help when developing the transformation logic for achieving a given domain representation?
- What are the most interesting, innovative, or unexpected ways that you have seen organizations address the data modeling lifecycle?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with organizations implementing a data modeling effort?
- What are the overall trends in the ecosystem that you are monitoring related to data modeling practices?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- sqlDBM
- SAP
- Joe Reis
- ERD == Entity Relation Diagram
- Master Data Management
- dbt
- Data Contracts
- Data Modeling With Snowflake book by Serge (affiliate link)
- Type 2 Dimension
- Data Vault
- Star Schema
- Anchor Modeling
- Ralph Kimball
- Bill Inmon
- Sixth Normal Form
- MCP == Model Context Protocol
Activity