The Analytics Power Hour #286: Metrics Layers. Data Dictionaries. Maybe It's All Semantic (Layers)? With Cindi Howson

Release Date: December 9, 2025
Host(s): Michael Helbling, Moe Kiss, Tim Wilson
Guest: Cindi Howson (Chief Data & AI Strategy Officer, ThoughtSpot)

Overview

This episode dives deep into the persistent and evolving challenge of semantic layers in analytics—what they are, why definitions of even simple business metrics (like “revenue” or “active users”) create confusion, and how semantic layers play into the future of generative AI, data mesh, and modern data architectures. Featuring Cindi Howson, a long-time thought leader in business intelligence and analytics, the panel debates whether the dream of a single “semantic layer to rule them all” is visionary—or a fallacy.

Key Discussion Points & Insights

1. What is a Semantic Layer?

• Definition: Cindi defines a semantic layer as a bridge that maps physical data structures to business terms, giving non-technical users a way to interface with databases using familiar concepts.

  “In the simplest terms, a semantic layer provides a representation of the business model in business terms to the physical structures in your...data warehouse, data lake, cloud data platform, whatever you want to call it. And it is important that it is in business terms.” — Cindi Howson (04:31)

• The importance of context: The meaning of a metric like “revenue” changes based on business domain (finance, sales, supply chain, etc.), illustrating why shared definitions are so contested and critical.
  • Example:

    “A finance person is always going to assume I am talking about when it was invoiced. A salesperson is going to come from the context of when is my commission going to get paid?” — Cindi Howson (07:26)

2. The “Semantics” Problem in Analytics

• Different teams create differing definitions for the same metric, leading to confusion and lack of trust.
  • Moe describes how every department’s unique version of “MAU” (Monthly Active User) cannot be summed for a company total, and definitions fail to roll up for organizational insight. (09:00)
• Semantic layers arose decades ago to make report writing accessible and more accurate, replacing cryptic coding with pick-and-click business logic.
  • Historical perspective:

    "Prior to semantic layers...you had to code your own SQL...The semantic layer gave report writers a way to click on business terminology to generate the SQL." — Cindi Howson (11:24)

3. Why Are Semantic Layers Hot Again (and Why Now)?

• Move away from in-memory tools that allowed individual, in-silo definitions revived the need for central, trusted definitions—especially as businesses leverage bigger, more unified datasets in the cloud.
• The rise of generative/agentic AI (“agentic” referring to systems that can autonomously act and reason) makes explicit semantic context crucial for accurate results (and to avoid “hallucination”).

  “The more context you give the LLM, the more accurate your answers will be. And that is why...semantic layers have become more important because of agentic AI.” — Cindi Howson (13:00)

4. Can There Ever Be “One Semantic Layer to Rule Them All?”

• The desire for a single semantic layer is strong but unrealistic; practical limitations and downstream tools’ idiosyncrasies make it impossible to achieve a unified layer.

  “What people want is one semantic layer to rule them all. And I just think that's a fallacy.” — Cindi Howson (15:08)

• Standards efforts (e.g., Snowflake’s Open Semantic Interchange) may help, but fragmentation is persistent. Each tool or platform interprets things differently, leading to separate implementations.
• Business value should drive semantic design, not technical convenience.

  "You want to avoid bringing in absolutely everything in the physical storage and exposing that to mere mortals, because that'll be overwhelming." — Cindi Howson (43:25)

5. The Role of Business Users, Domains, and Data Literacy

• There’s tension between technical design (schemas/domains) and real business questions; semantic layers should serve business usage patterns.

  “If you build a semantic layer that doesn't work that way [reflecting how business users ask questions], what is the point? Go home.” — Cindi Howson (28:15)

• The panel laments that many analysts have little training in data modeling, leading to underappreciated complexities and inconsistent models.

  “Our industry has also now raised a generation of data analysts who never learned proper data modeling.” — Cindy quoting her own writing (40:00)

• Both data literacy and business literacy are essential.

  “We talk about data literacy. We also need to bring in business literacy. To me, it's not just about where is the data coming from. It is also how is it used and that there really might be two different definitions.” — Cindi Howson (42:16)

6. Standards, Vendor Lock-in, and Industry Adoption

• There’s real risk that attempts to standardize semantic layers result in even more fragmentation (the “now we have 14 standards” XKCD effect).

  “We have the XKCD cartoon...There are 13 standards, we need one more—and now we have 14 competing standards.” — Tim Wilson (30:18)

• True adoption will depend on both vendor coalitions and customer demand. Snowflake, ThoughtSpot, DBT, and others are moving, but ecosystem buy-in is uncertain.

7. Lessons Learned: Building a Semantic Layer

Cindi’s tips for success:

1. Don’t expose everything: Don’t dump all physical tables into the layer—start from actual business questions. (43:25)
2. Build for reusability, not for technical completeness.
3. Ensure semantic layers use business-friendly (not technical) language.

• The time to mature: Metrics layers could take 5-10 years to fully mature and integrate seamlessly (Gartner’s research cited at 36:09).

Noteworthy Quotes

“The more context you give the LLM, the more accurate your answers will be. That is why semantic layers have become more important because of agentic AI.”
— Cindi Howson (13:00)

"What people want is one semantic layer to rule them all. And I just think that's a fallacy."
— Cindi Howson (15:08)

“If you build a semantic layer that doesn't work that way, what is the point? Go home.”
— Cindi Howson (28:15)

“Our industry has...raised a generation of data analysts who never learned proper data modeling.”
— Cindi Howson (40:00)

“…You want to avoid bringing in absolutely everything in the physical storage and exposing that to mere mortals, because that'll be overwhelming. So I always start with who is going to use this and what are the top questions they’re going to want to be able to ask of it.”
— Cindi Howson (43:25)

Memorable Moments & Humor

• Tim’s Humorous Summary: “Part of the challenge, it’s like we’re trying to find a technology or a tool or a process to solve for something where the business…thinks revenue in their own way…and then they both complain that the other department is wrong.” (07:02)
• History Lesson: Cindi recounting the legal origins of semantic layers—originally patented by BusinessObjects, with Cognos having to pay royalties! (11:24)
• XKCD Reference for Standards: Tim analogizing data standards to the famous “Now There Are Fourteen Competing Standards” comic (30:18)
• Podcast Host Confessions: The hosts candidly admit they don’t finish each other’s books or listen to many business podcasts—much laughter (47:24).

Timestamps for Key Segments

• Definition and importance of semantic layers (04:31‒06:23)
• Metric ambiguity in real businesses (06:27‒08:23)
• Why context & business meaning is hard (08:23‒11:24)
• History and resurgence of semantic layers (11:24‒14:09)
• Why “one semantic layer” will never happen (15:08‒18:38)
• Data mesh and the semantic “cake” (18:38‒21:09)
• Are semantic layers really new? (21:09‒24:03)
• Do analysts really know modeling anymore? (40:00‒42:16)
• What not to do — pitfalls in building a semantic layer (43:09‒43:56)

Final Takeaways & Recommendations

• Semantic layers are not new, but more important than ever. They enable both business clarity and the next generation of AI-powered analytics.
• No silver bullet: There won’t be a universal semantic layer—organizations must architect with business context in mind, and be prepared for multiple layers and representations.
• Business and data literacy need to advance together. Both technical and domain expertise are essential for the future of analytics.
• Be intentional: Start with user needs and prioritize clear business language over technical completeness. Expect evolution—not perfection—from tools and standards.

For more:

• Listen to the full episode via Analytics Power Hour
• Explore Cindi Howson’s podcast, The Data Chief, for more on the future of BI and analytics leadership.