Solution Brief

The Power of the Semantic Layer in Retail

The retail industry is undergoing a rapid evolution, driven by factors such as the rise of e-commerce, changing consumer behavior, and the impacts of economic change. These shifts have pressured retailers to adapt and become more agile. In response, retailers are turning to semantic models, a technology that simplifies access to complex data and empowers decision-making at all levels of the organization to adapt to this rapid evolution.

Choosing the Right Semantic Layer Solution

When selecting a semantic layer solution, retailers should consider:

  • Universal Compatibility: Ensure compatibility with various BI and data science tools.
  • Data Abstraction: Prioritize solutions that abstract data complexities and prevent vendor lock-in.
  • Performance at Scale: Demand fast query performance, especially for the largest datasets and complex queries.
  • Business-User Focus: Provide an intuitive experience for non-technical users to access and analyze data with the tool of their choice using common business terminology.
  • Robust Security and Governance: Integrate with existing security infrastructure and provide data governance at the row and column levels of data.
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A semantic layer is a business representation of data that helps executives, business stakeholders, and analysts get trusted results from their data using commonly understood terms like “product”, “customer,” and “revenue”. The result is a unified and consolidated view of data across an organization.

Retailers leverage a semantic layer to instill trust in Generative AI and analytics-driven KPIs. A semantic layer can yield:

  • $2+ million in analytics project cost savings
  • 3x increase in ROI of IT investments

How retailers use a semantic layer:

  • Real-time Customer 360: Combine data from POS, CRM, e-commerce, and marketing systems to create a comprehensive view of each customer.
  • Inventory Optimization: Integrate inventory data with sales trends to accurately forecast demand and optimize stock levels.
  • Sales Performance Deep Dive: Analyze sales data by region, product, store, and period to track KPIs and identify growth opportunities.
  • Marketing Campaign ROI: Connect marketing data with sales and customer data to measure campaign effectiveness and optimize spend.

Data Challenges in Retail Solved with a Semantic Layer

Retailers face several challenges in managing their data to make data-driven decisions successfully:

  • Data Silos: Data often resides in separate systems, making it difficult to gain a holistic view of customers, inventory, sales, and marketing efforts.
  • Complex Data Preparation: Transforming raw data into insights requires significant time and technical expertise.
  • Inconsistent Reporting: Analyzing data from disparate sources can lead to conflicting conclusions.

The ROI of a Semantic Layer

A semantic layer offers several benefits to retailers, including:

  • Optimized Cloud Costs: Semantic layers reduce cloud analytics costs by over 3x by optimizing compute usage, improving query performance, eliminating redundant data copies, and streamlining data preparation.
  • Optimized Human Capital Costs: Using a semantic layer reduces the effort for a typical 1,000-hour analytics project by nearly half. An average organization has 25 such projects annually, estimating savings of $2.3 million annually.
  • Trusted Results through Data Governance: AtScale’s semantic layer enables natural language prompting, delivers result consistency, and improves text-to-SQL performance, achieving nearly 100% accuracy by providing analysts and LLMs with a common business language by mapping diverse data into familiar terms like “product,” “customer,” and “revenue.”
  • Accelerated Time to Insights: Improve query performance by over 4x. Semantic layers simplify and automate data preparation, empowering BI and Generative AI initiatives to uncover insights faster.

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