The Significance of Data Modeling

Data Modeling has become a second-class citizen at modern tech companies. Without data architects and data engineers in the loop, the data model is all but ignored in favor of 'quick insights' with infrastructure never designed for scale. When this lack of modeling is paired with an inherent desire for speed- tooling which the Modern Data Stack (MDS) is all but too happy to provide - the result is an endless Data Swamp. Data consumers are stuck leveraging tables they don't trust and ownership of critical data sets is tenuous at best. Insights, machine learning models, and reports are built on a house of cards that could come crumbling down at any moment. Too often teams confuse this chaos as either 'normal' or the fault of a data infrastructure team that hasn't been doing their jobs. In reality, it is a result of years (sometimes decades) of data debt and decay. While it is possible to establish some semblance of a structure by throwing bodies at the problem until people stop complaining, for all but the most well-capitalized businesses in the world this solution is hopeless and unreachable. "We can just hire more data engineers" is a pipe dream unless your company is named Google or Apple. Philosophically, the reason data modeling is useful is that it is an abstraction that bridges the gap between our data and the real world. Semantic concepts - entities, their behavior, the relationships between them, and their properties- modeled effectively in the Warehouse provide an environment for data consumers to explore the edges and nodes in this semantic graph and build agreed-upon derived concepts/metrics. Without this layer, it is up to individuals and teams to make decisions about how to best represent these semantic concepts in SQL leveraging an output never designed for analytics. Complex business rules which should be captured semantically, are instead inferred post-hoc through hundreds of lines of code. Context is lost over time as thousands of decisions are made, queries are tweaked, and your average data consumer is left in the dark. I'm sure to many people reading this post what I am saying is as obvious as saying the sky is blue. However, in engineering-first organizations, the likelihood that a well-modeled semantic layer exists, is actively maintained, and actually represents the business is becoming increasingly rare over time. Modeling is one of the core principles of Good Data UX and it is stunningly rare in 'top-tier' tech companies. Data Scientists and Analysts, it is your responsibility to advocate for a strong semantic layer! Data Engineers and Platform Engineers, it is your responsibility to ensure that data producers understand when and where data modeling should happen, and how to communicate those needs programmatically to data producers. Service Engineers, it is your job to take accountability for the data your service produces and treat it as a product. Better data is on all of us! Good luck! #dataengineering

Sanjeev Mohan dives into why the success of AI in enterprise applications hinges on the quality of data and the robustness of data modeling. Accuracy Matters: Accurate, clean data ensures AI algorithms make correct predictions and decisions. Consistency is Key: Consistent data formats allow for smoother integration and processing, enhancing AI efficiency. Timeliness: Current, up-to-date data keeps AI-driven insights relevant, supporting timely business decisions. Just as a building needs a blueprint, AI systems require robust data models to guide their learning and output. Data modeling is crucial because: Structures Data for Understanding: It organizes data in a way that machines can interpret and learn from efficiently. Tailors AI to Business Needs: Customized data models align AI outputs with specific enterprise objectives. Enables Scalability: Well-designed models adapt to increasing data volumes and evolving business requirements. As businesses continue to invest in AI, integrating high standards for data quality and strategic data modeling is non-negotiable.

Why Data Modeling is Essential for Business Intelligence (BI)? If you’re working in BI or analytics, you already know how important it is to have clean, well-organized data. That’s where data modeling comes in, it’s the backbone of any database, helping to structure information so it’s easier to work with and draw insights from. Here are some key concepts that make data modeling so critical: Entity: Think of this as any real-world object or concept you want to track in your data, like a customer or a product. Relationships: These define how different entities are connected and interact with each other. Foreign Key: This helps link different tables together while keeping your data organized. Primary Key: Every record needs a unique identifier—that’s what the primary key is for. Normalization: The process of breaking data into smaller, related tables to reduce redundancy and make it more efficient. ERD (Entity-Relationship Diagram): A simple visual tool to map out your data structure. Schema: This is the blueprint for your database, organizing tables, views, and more. Cardinality: Tells you how entities in one table relate to entities in another. Surrogate Key: An artificial key used when there’s no natural identifier available. Metadata: Data about your data—it helps describe the structure and relationships in your database. Data Types: Defines what kind of data you can store, whether it’s numbers, text, or dates. Data modeling is key to delivering meaningful insights and helping businesses make smarter decisions. It's one of those skills that unlocks doors to new opportunities in BI and beyond. Have you been working with data models? What are your go-to tools for building them? #dataanalyst #datamodeling

Data modeling for AI For data leaders, the message is clear: without a robust data modeling practice, your organization is flying blind. Data models offer a structured approach to capturing the essence of what the business needs and translating that into actionable data strategies. In today’s competitive landscape, where businesses are increasingly looking to leverage Artificial Intelligence and Machine Learning, having a solid foundation in these models is imperative. AI and ML thrive on clean, well-structured data, and without a well-constructed data model, the data fed into these systems may not truly represent the business’s needs, leading to suboptimal outcomes. Article in CIO Influence by Keith Belanger