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Modernizing Retail Data Platforms

Faster time to retail data insight

Retail is about matching products to customers at the right place, time, and price.
Patterson Consulting delivers platforms that minimize time to retail operational insight.

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Retail Platform Engineering Services

Retail runs on accurate forecasting of demand and sales—and then allocating product to match, so the right inventory reaches the right channel at the right time. Execution hinges on three linked domains:

  • merchandising (what to offer and price)
  • operations & fulfillment (how it moves)
  • and sales & customer experience (how it’s bought and supported)
Your tech stack should unify all three.

Retail marketing is moving towards "Customer 360" as a single, real-time view of each shopper—linking POS, e-commerce, loyalty, service, and returns—so operations can match inventory, labor, and promotions to actual demand. This requires a data platform with efficient data change updates and data tranformation workflows that can quickly update the latest information.

Patterson Consulting will help your retail organization consolidate data fragmentation, reduce event lag to insight, and better align demand and supply. We do this by building on the most advanced data warehouses today, the Databricks Data Lakehouse.

Data platform technologies:

  • Databricks
    • Unity Catalog
    • Serverless SQL
    • Lakeflow Connect
    • Lakeflow Jobs
  • Amazon Web Servers
  • Azure
  • GCP

Databricks Databricks

Data Integration

Retail data is scattered—POS, e-com, loyalty, vendor EDI, logistics—so it’s hard to answer: “what’s selling where, at what margin, and what do we order next?” Patterson Consulting uses Databricks Lakeflow Connect to unify these sources into a governed, near-real-time layer with reliable pipelines, standard schemas, and lineage—giving COO/CTO teams one operating picture with clear item/location margin & sell-through, timely allocation/replenishment signals, and faster decisions.

Faster Insights with Databricks

Close the gap between events and decisions --- If forecasts and actions come from weekly or monthly reports, you’re reacting after margin is already lost—via stockouts or markdowns. The pain: you can’t quickly align products, customers, and supply chains because data is scattered, late, and siloed. Patterson Consulting fixes this with Databricks Serverless SQL, Lakeflow Jobs, and Unity Catalog—streaming and governing POS, e-com, loyalty, vendor, and logistics into a real-time information layer. Result: KPIs in minutes (not weeks), a single operating picture, tighter allocation and pricing, and fewer stockouts and markdowns.

Case Study:

Learn how Quantatec integrated generative AI into their Movias logistics platform.

Quantatec wanted a large language model (LLM) to answer customer’s questions about their own fleet data and Patterson Consulting helped us to integrate the LLM properly and provide consistent quality answers to the user.

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Our latest blog resources

Articles on platform engineering and generative AI from the Patterson Consulting team

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Building a Property Insurance Claims Data Lakehouse with Airflow and Databricks

In this article we build a data lakehouse for property insurance claims management. This allows us to build out a complete claims analysis platform complete with standardized data modeling.

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Building a Standard Claims Data Model With the Cube Semantic Layer and Databricks

Creating standard claims data models for claims knowledge work in property insurance companies.

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Data Modeling Healthcare Data with DBT

In this post we’ll build our first DBT pipeline to summarize patient history data for downstream data engineers.

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Common Questions About our Engagement Process

Here are some of the commons questions asked.

The Patterson Consulting "Architecture Review" offering is the best way to get started. The deliverable is a report explaining the current state of your data strategy and data platfrom (from an retail industry point of view), and how they currently perform. We also measure how the data platform is affecting divisions such as operations, merchandising, and sales. We then work with your engineering team to understand the skills of your existing team, and preferred cloud technologies, before recommending a specific new 'evolved' architecture that will improve the key business indictors of the current platform. This offering costs between $55k to 75k, depending on the scope of the engagement.

Yes, we look for platform architectures that fit the existing skill investments of organizations, and then make sure we get them on technologies that will scale to the speed that does not hold back the business.

We work with retail data teams to come up with a plan to communicate the work they are doing and what issues currently exist between two systems. We then create joint plans between different parts of the organization that helps them work together to get the data integration to a place where everyone trusts the process and the data.

Let's talk about your data platform.

Patterson Consutling platform engineering teams help you architect and deploy data platforms that drive the business and support generative AI capabilities. We specializing in constructing modern data platforms that focus on ensure data correctness and generating analytical results as quickly as possible.

Talk to our experts