PUBLISHED DATE: 2026-09-07 01:43:40
Publicis Sapient and Snowflake | Publicis Sapient
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Unlock, Unscramble And Unleash Your Data
Your data problems, solved.
Our approach to data considers all the functions of a data center of excellence (CoE)
Publicis Sapient and Snowflake work collaboratively to solve complex data problems. Together, we help organizations unite siloed data, while enabling data storage, processing and analytic solutions that are faster, easier to use and far more flexible than traditional offerings. Through this partnership, we can enable elasticity and scalability of the cloud without worrying about cost, performance or system complexity.
Real-world impact across every data CoE function
Together, Publicis Sapient and Snowflake strengthen all the core functions of a modern Data Center of Excellence—from ingestion and processing to governance, lineage, quality, and consumption. Our teams help enterprises transition from rigid, siloed data environments to flexible, scalable cloud-native ecosystems that support advanced analytics, AI, and enterprise-wide decision-making. Through thoughtful architecture design, engineering rigor, and embedded governance, we create platforms that evolve with the business. The case studies below illustrate how these capabilities translate into measurable outcomes across multiple data domains.
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Major investment company
Data mesh implementation for a major investment entity
Background
The company operates a wide variety of financial instruments offering personal and business investment, wealth management, portfolio construction and distribution through its offices located in Europe, North America and Asia
As part of a internal initiative company is bringing data culture and revamping how data products are perceived
Imperative for change
Publicis Sapient is engaging with the enterprise architecture and business technology teams to build data mesh architecture
Publicis engineering team is owning the both high level and low level detailed design
Providing technology capability with our experience on data engineering
The transformative solution
Introduction of Snowflake as Information delivery platform
Snowflake provides faster data integration via data sharing capability
Snowflake architecture separates compute and storage which provides strong scalability
Source/Consumer Domains
Source domains are built on Snowflake by leveraging Data Sharing capability
Enrichment and validation of data to be done in Snowflake
The impact
Detailed analysis on current state
Data mesh architecture
Integration and collaboration with data platform
Enabling data product team to onboard on data mesh
Data products design
Robust data consumption layer
Global investment management firm
Snowflake governance program
Snowflake governance is a core platform project managed by a dedicated small team that focuses on overseeing and optimizing the usage of Snowflake within the organization. It encompasses monitoring, access control, cost management, and security to ensure the platform’s efficient and secure operation while aligning with the firm’s data management and compliance goals.
Background
The client, a leading international investment management firm, aims to enhance their reference data management procedures and is interested in deploying a cloud-based data warehouse. This solution will empower the firm to perform robust analytics on extensive historical data, ultimately enabling more informed business decision-making.
Imperative for change
The governance framework is put in place to support critical aspects of Snowflake’s usage within the organization, including:
- Monitoring: The platform team is responsible for continuously monitoring Snowflake’s performance, health, and security. They track system usage, query performance, and resource utilization to promptly identify and address any issues
- Access Control: Snowflake governance involves defining and enforcing access policies and permissions to ensure that only authorized personnel can access and manipulate the data stored in Snowflake
- Cost Management: Controlling costs associated with Snowflake usage is crucial, especially in a cloud environment. The platform team sets up cost monitoring and optimization strategies to track expenses and recommend cost-efficient resource allocation
- Compliance and Security: Snowflake governance includes compliance with regulatory standards and security best practices. The platform team implements security measures, encryption, and auditing to safeguard data and ensure the organization complies with relevant regulations
The transformative solution
The solution involved the following components:
- Role-based Access Control (RBAC)
- Automation tools/utilities help establish a robust governance framework to manage and monitor Snowflake
- Lambda-based monitoring of Snowflake accounts to detect and notify of policy or security violations
- DBT models for data transformation and retaining data from Snowflake share
- Resource Monitors: Resource monitoring to track credit usage and receive alerts for potential credit consumption issues
The impact
We have a small 3-member team from PS helping clients to build and develop the tools around monitoring and managing the Snowflake platform
PS partnered with the client to build automation tools/utilities to help establish a robust governance framework to manage and monitor Snowflake
PS helped the client create a governance dashboard in Tableau that provides real- time insights into Snowflake usage, cost, and security compliance
As the Snowflake usage within the firm increases, PS is helping other projects to onboard onto the platform and creating Roles, Databases, and Schema with appropriate privileges for different user groups
Program to manage, monitor & govern the Snowflake service
Program to build a market reference data platform
Snowflake governance is a core platform project managed by a dedicated small team that focuses on overseeing and optimizing the usage of Snowflake within the organization. It encompasses monitoring, access control, cost management, and security to ensure the platform’s efficient and secure operation while aligning with the firm’s data management and compliance goals.
Background
The client is a prominent global investment management firm seeking to optimize their reference data management processes. Confronted with issues surrounding data quality, restricted data coverage, intricate data consumption approaches, and the need for thorough auditing, the company visioned to overhaul its reference data platform. The objective was to enhance overall efficiency and facilitate more informed decision-making.
Imperative for change
The project aims to easily enable data acquisition with a broad range of external vendor datasets to support firms’ market reference data needs, thereby ensuring steadfast adherence to vendor data consistency and quality principles. The primary focal points of the project encompassed the following central objectives:
- Enhanced understanding of Data Lineage and Utilization
- Remove vendor data processing from Oracle and use Snowflake to store the vendor data
- Improve vendor data quality validations and transparency back to the vendor file to ensure vendor data across the firm has been validated before use
- Expose all vendor data in query accessible source for all consumers across the firm
The transformative solution
Build a modern reference data platform using Snowflake’s cloud-based data warehousing solution combined with AWS services.
- Snowflake Data Warehouse
- AWS Services Integration
- Snowpipe for Real-time Ingestion
- Data Transformation and Quality Checks
- Data Access and Consumption
The impact
We have a 30-member team from Engineering, Experience design, QA research, and product working across five tracks to build the next-generation reference data platform
PS partnered with the customer to build an in-house, highly configurable vendor data onboarding application tool, which replaced their legacy third-party tools, saving on licensing fees and faster onboarding of any new vendor data
PS partnered with the customer to build a metadata-based data processing application that empowers analysis to write business logic to quickly process and transform vendor data without any developer involvement
PS partnered with the customer to create a new security setup service, significantly sped up the process of setting up a new instrument within the company. This service is now heavily leveraged by the entire firm
PS Quality Engineering team collaborated with the customer to automate testing and release processes. This collaboration led to a noteworthy enhancement in their ability to quickly introduce new features to the production
Large investment bank
Program to build a next-gen advisor platform
Detailed technical analysis and technical design to develop the advisor platform and big data engine powering the data needs
Background
Client is building a next-gen advisor platform solution that needs a solid pillar of data and abilities to migrate multiple data sources including hundreds of legacy OLTP systems to cloud computing systems. Advisor should be able to view and recommend insights to their clients based on latest asset trends and reports that are built out of the data platform.
Imperative for change
Detailed data and technical requirements needed to be driven out for the Extract, Transform, Load (ETL) design part of cloning the 40 applications running in the retained Bank
Requirements gathering, functional analysis, technical architecture and detailed data design / engineering of source data, data extraction, transformation and manual data processes
Provide valuable input for the courses of actions that the bank needs to take in order to succeed in the transformation programme including data governance
Baseline Data Architecture, and Baseline Application Architecture to inform decisions and subsequent work for cutover and migration activities
Provide functional and technical support during transformed target values, system testing, system integration, proving and user acceptance testing, data cuts and sign off
The transformative solution
During the onboarding phase, Publicis Sapient proactively performed on evaluation of two major Big Data platforms that the client was reviewing and made recommendations that were greatly received
Publicis Sapient’s team performed multiple POCs over the technology evaluation period and consistently enabled the client in making informed and right decisions
In the current development phase, the team is helping build capabilities across the depths of the data using technologies like Snowflake, DBT, Airflow and Power BI to name a few
Publicis Sapient is building solutions involving Java, Python and testing using automation frameworks
The impact
We currently have 24 team members from experience, engineering, research and product working across 6 different tracks of work on the next generation advisor platform
Publicis Sapient made quick turn arounds and enabled multiple Power BI reports which were previously not possible with legacy systems resulting in client confidence and trust
Even as the company is expanding its in-house development team, we continue to focus on designing their strategically important next generation data platform and contributing to their application development initiatives
How Publicis Sapient helps organizations undertake an AI-centric digital transformation
During the World Tour 2025 conference in New York City, "Data Cloud Now" Anchor Ryan Green had the chance to chat with Naveen Porumamilla, VP of Executive Client Partner at Publicis Sapient, to discuss how we help clients implement AI as part of an overall enterprise digital transformation. They also discuss strategies for identifying key applications for AI and evaluating the impact of AI implementations.
Meet our Snowflake leader
Orla Beirne
Business Development & Alliance Partner, NA
View Orla Beirne's LinkedIn Profile
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