Building Semantic Layers to Accelerate AI Adoption

Building Semantic Layers to Accelerate AI Adoption

AI promises transformative outcomes—but only if it’s built on trusted, consistent data. Too often, organisations rush into AI and analytics without aligning business definitions, leading to silent failures: inflated sales but collapsing margins, underpriced insurance premiums, or dashboards that don’t match reality.

This article explores how a Semantic Layer solves those challenges by acting as a business-friendly abstraction over complex data. By unifying metrics, dimensions, and facts, it ensures every dashboard, AI model, and decision shares the same trusted foundation.

You’ll discover:

Why semantic consistency is essential for successful AI adoption

Real-world examples of costly failures caused by inconsistent definitions

Practical steps to design and implement a semantic layer

How platforms like Snowflake and Cortex Analyst bring semantic models to life

Versent’s AI Readiness Assessment—a structured approach to measure maturity, identify gaps, and fast-track adoption

At Versent, we believe the semantic layer is the missing link between BI and AI. It doesn’t just power dashboards—it powers trust, explainability, and ROI across the enterprise.

If you’re serious about unlocking AI value while avoiding hidden risks, now is the time to invest in a semantic foundation.

2. Building Semantic Layers
to accelerate AI Adoption
Unlock trusted data for analytics & AI

3. 3
Agenda
What is a Semantic Layer
Whyis it important
Howto create it
Are you ready for AI
Summary/Q&A
01
02
03
04
05

5. What is a Semantic Layer?
•A business-friendly
abstraction over data
•Translates complex
sources into shared,
governed metrics
•Powers both BI
dashboards and AI/ML
models

6. Why does a Semantic Layer enable AI Adoption?
AI models need clean,
contextual, structured inputs
Provide structured, contextual inputs to models
Enforce consistent metrics across all use cases
Enable faster training & explainability
Bridge BI & AI through a single data foundation
Better ROIand adoption

7. The Retailer That Discounted Itself Into Trouble
Retailer launches AI for pricing & promotions
→ dashboards show sales up 12%
→ Leadership celebrates “success”
But… inconsistent definitions of margin(gross vs
contribution/net)
→ AI discounted products that looked profitable but
weren’t
Reality:Sales up, but profits down —millions lost
before finance caught it
SALES PROFIT
Inconsistent semantics = Silent AI failure

8. When AI Underestimates Risk
Health insurer used AI to optimise claims & risk
scoringfor pricing
→ Dashboards looked great -approvals faster,
costs down
Hidden issue: inconsistent definitions of “claim”
•System A = line item (blood test, X-ray)
•System B = episode (full hospital visit)
Model trained on ‘line items’
→ underestimated risk & payouts
Result:premiums underpriced, reserves short,
losses in the tens of millions
SALES PROFIT
Inconsistent semantics = Silent AI failure

9. What does a Semantic Layer Looks Like?
Every semantic layer requires
essential elements
FACTS: Events “How Many”
METRICS: KPIs “How Well”
DIMENSIONS: Context “Who/What/When/Where”

10. How do I Design A Semantic Layer?
Create your
Business Data Model
What business entities exist in your data (for
example, customers, products, orders, and so on)?
How do these entities relate to each other?
What metrics are important to your business?
What dimensions do you use to analyse these
metrics?

11. How do I Make it Real?
Map your business concepts to
your physical data
Which tables contain the data you need?
How will you join these tables?
What calculations are needed to derive your
metrics?

12. Snowflake: Implementation Example
--Create or replace the semantic view named TPCDS_SEMANTIC_VIEW_SM
CREATEORREPLACE SEMANTIC VIEWTPCDS_SEMANTIC_VIEW_SM
tables (
CUSTOMER primarykey (C_CUSTOMER_SK),
DATEasDATE_DIM primarykey (D_DATE_SK),
DEMO asCUSTOMER_DEMOGRAPHICS primarykey (CD_DEMO_SK),
ITEM primarykey (I_ITEM_SK),
STORE primarykey (S_STORE_SK),
STORESALES asSTORE_SALES
primarykey (SS_SOLD_DATE_SK,SS_CDEMO_SK,SS_ITEM_SK,SS_STORE_SK,SS_CUSTOMER_SK)
)
relationships (
SALESTOCUSTOMER asSTORESALES(SS_CUSTOMER_SK) referencesCUSTOMER(C_CUSTOMER_SK),
SALESTODATE asSTORESALES(SS_SOLD_DATE_SK) referencesDATE(D_DATE_SK),
SALESTODEMO asSTORESALES(SS_CDEMO_SK) referencesDEMO(CD_DEMO_SK),
SALESTOITEM asSTORESALES(SS_ITEM_SK) referencesITEM(I_ITEM_SK),
SALETOSTORE asSTORESALES(SS_STORE_SK) referencesSTORE(S_STORE_SK)
)
facts (
ITEM.COST asi_wholesale_cost,
ITEM.PRICE asi_current_price,
STORE.TAX_RATE asS_TAX_PRECENTAGE
)
dimensions (
CUSTOMER.BIRTHYEAR asC_BIRTH_YEAR,
CUSTOMER.COUNTRY asC_BIRTH_COUNTRY,
CUSTOMER.C_CUSTOMER_SK asc_customer_sk,
DATE.DATE asD_DATE,
DATE.D_DATE_SK asd_date_sk,
DATE.MONTH asD_MOY,
DATE.WEEK asD_WEEK_SEQ,
DATE.YEAR asD_YEAR,
DEMO.CD_DEMO_SK ascd_demo_sk,
DEMO.CREDIT_RATING asCD_CREDIT_RATING,
DEMO.MARITAL_STATUS asCD_MARITAL_STATUS,
ITEM.BRAND asI_BRAND,
ITEM.CATEGORY asI_CATEGORY,
ITEM.CLASS asI_CLASS,
ITEM.I_ITEM_SK asi_item_sk,
STORE.MARKET asS_MARKET_ID,
STORE.SQUAREFOOTAGE asS_FLOOR_SPACE,
STORE.STATE asS_STATE,
STORE.STORECOUNTRY asS_COUNTRY,
STORE.S_STORE_SK ass_store_sk,
STORESALES.SS_CDEMO_SK asss_cdemo_sk,
STORESALES.SS_CUSTOMER_SK asss_customer_sk,
STORESALES.SS_ITEM_SK asss_item_sk,
STORESALES.SS_SOLD_DATE_SK asss_sold_date_sk,
STORESALES.SS_STORE_SK asss_store_sk
)
metrics (
STORESALES.TOTALCOST asSUM(item.cost),
STORESALES.TOTALSALESPRICE asSUM(SS_SALES_PRICE),
STORESALES.TOTALSALESQUANTITY asSUM(SS_QUANTITY)
WITHSYNONYMS = ( 'total sales quantity', 'total sales amount')
)
;

13. Snowflake: Semantic Layer Usage via Cortex Analyst

14. What Makes a Semantic Layer Work?
Clean, Well-Formatted Data
Unified Access to Data
Clear Business Definitions
Governance & Security
Scalable Architecture
Integration into AI/BI/Analytics

15. AIReadiness
Assessment
ASSESSMENT & AI PROOF OF CONCEPT
FOCUSED 4 WEEK
Ready to unlock the value tied up in your traditional
data stores? With this engagement we’ll assess your
organisation’s AI readiness with you using a
comprehensive range of activities:
Versentunderstands AI is challenging
and has developed a structured
approach to measure readiness, identify
uplift and gauge maturity using our
readiness framework and accelerators
to unlock value and capability in AI.
AI Readiness Gap Assessment
AI Proof of Concept
Readiness Assessment Scoring
Accelerated deployment roadmap

16. DATA AI
Semantic
Layer