Data Consulting Services | Data Modeling Consulting Services

Data modeling consulting services

Bring structure and clarity to your data landscape. A well-designed data model is the blueprint for how your organization's data is organized and related, forming the critical link between your raw data and actionable business insights. Our expert data modeling consulting services help you design, implement, and optimize conceptual, logical, and physical data models that improve data integrity, accelerate query performance, and provide a scalable foundation for your analytics platforms.

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Pythian has the top data modeling consultants

Our data modeling consulting services are delivered by a team of certified experts using proven methodologies to design a logical and performant structure for your data assets. We help you implement the right modeling techniques to improve data accessibility, ensure consistency, and drive powerful analytics.

25+

Years of experience

For over two decades, we have been helping the world's leading organizations solve their most complex data challenges.

45+

Data technologies supported

Pythian supports over 45 technologies—from cloud platforms and data warehouses to data lakes and databases—ensuring we can design a data model that fits your unique technology stack.

500+

Global customers

As a global service provider, we serve customers across the world, many who have been with us for decades, trusting us to manage the critical data that runs their business.

What's included in Pythian's data modeling consulting services?

A strategic blueprint for your data's structure

Our data model consulting is a comprehensive service that addresses every layer of your data's architecture. We create a strategic, forward-looking plan to ensure your data models are not only effective today but also scalable for the future.

Data model assessment and discovery

We analyze your existing data models, business requirements, and source systems to identify gaps, performance bottlenecks, and opportunities for improvement.

Conceptual and logical data modeling

We collaborate with your business stakeholders to create a technology-agnostic conceptual and logical model that defines key business entities, attributes, and relationships, ensuring alignment with your strategic goals.

Physical data model design and implementation

We translate the logical model into a physical design optimized for your specific database or data platform technology (e.g., SQL, NoSQL). This includes defining tables, columns, data types, and indexes for maximum performance.

Data warehouse and analytics modeling

We specialize in designing schemas for analytical workloads, including star schemas, snowflake schemas, and data vault models, to enable efficient and intuitive business intelligence and reporting.

Fashion retailer creates a unified customer view with a new data model

Stand Out For Good, Inc. (SOFG) had unstructured customer data distributed across numerous sources like TikTok, Facebook, and Google Analytics, making it impossible to create personalized messaging. The company engaged Pythian to build an enterprise data platform on Google Cloud, centered on a new data model designed to provide holistic and integrated customer insights. The new model empowers the marketing team with a complete "story" of each customer, enabling data-driven decisions that reduce costs and improve marketing efficiency.

Read the customer story ->
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Business outcomes you can expect from our data modeling consulting services

Build a foundation for clear, consistent insights

A well-designed data model is the foundational step for achieving data clarity and reliability. We help you structure your data in a way that improves performance, builds trust, and simplifies the path from raw data to valuable insight.

Improve data consistency and integrity

Establish a single source of truth by standardizing data definitions and relationships, reducing data redundancy and inconsistencies across your organization.

Accelerate query and report performance

Design physical models that are optimized for your specific analytics platform, leading to faster query response times and a better experience for business users.

Simplify data integration and development

Provide a clear, well-documented blueprint for your data, making it easier and faster for developers to build new data pipelines, applications, and reports.

Increase business user adoption and trust

Create intuitive and easy-to-understand data structures that empower business users to perform self-serve analytics with confidence in the underlying data.

How Pythian's data modeling consulting service works

A proven process for an optimal data structure

As an experienced data partner, our consultants use a proven, multi-layered process to design and implement data models that are scalable, performant, and aligned with your business.

Business requirements gathering

We begin by collaborating with your business teams to understand their key processes, entities, and analytical questions.

Logical model design

We translate these requirements into a technology-agnostic logical data model that serves as the blueprint for your data's structure.

Physical model implementation

We implement the logical design onto your target platform, optimizing the physical structure for performance, scalability, and cost-effectiveness.

Validation and governance

We validate the model against business use cases and help establish governance processes to manage and evolve the model over time.

Bring clarity and performance to your data

Ready to design a scalable and high-performance data model?

Related resources

Learn how our customers succeed with data modeling consulting

Explore how our customers have built powerful analytics platforms on a foundation of solid data modeling.

Stand Out For Good unifies its marketing data
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Stand Out For Good unifies its marketing data

Learn how this fashion retailer designed a new data model to consolidate unstructured data from disparate sources like social media and Salesforce. The new model provides a holistic view of the customer journey, resulting in more efficient marketing and annual cloud savings of about $100,000.

Read the story ->
Morse Hydraulics slashes SAP reporting time
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Morse Hydraulics slashes SAP reporting time

Discover how a global manufacturer migrated its legacy SAP ECC system to S/4HANA, a project centered on a significant data model transformation. This data model change was a key factor in reducing SAP report execution time from nearly two hours to less than five minutes.

Read the story ->
University of St Andrews enables data-driven decisions
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University of St Andrews enables data-driven decisions

The University of St Andrews needed to overcome challenges with siloed data held in numerous separate systems. By building a centralized data warehouse on Google Cloud, they designed a new, unified data model that provides a single source of truth to support effective analysis and decision-making.

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Frequently asked questions (FAQ) about data modeling consulting

What is data modeling consulting?

Data model consulting is a service where experts help businesses design, implement, and optimize the structure of their data. This involves creating a blueprint (the data model) that defines how data is organized and related, ensuring it is a reliable and performant foundation for applications and analytics.

What are the different types of data models?

The three main types are conceptual, logical, and physical. A conceptual model provides a high-level view of business concepts. A logical model adds more detail about entities and relationships, independent of technology. A physical model is the actual implementation of the model in a specific database system.

What is the difference between a data model for a transactional vs. an analytical system?

A data model for a transactional system (OLTP) is typically normalized and designed to optimize fast, frequent, small transactions (like placing an order). A model for an analytical system (OLAP) is often denormalized (e.g., a star schema) and designed to optimize fast queries across large volumes of historical data.

 

How does a good data model improve analytics performance?

A good data model improves performance by organizing data in a way that minimizes the amount of work the database has to do to answer a query. For analytics, this often means pre-aggregating data, reducing the number of table joins, and aligning the structure with common business questions, which results in faster reports and dashboards.

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