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Elevating the student experience with AI-powered search

  • Education

Elevating the student experience with AI-powered search

OneSix enhanced the student experience for a global study abroad organization by developing an AI chatbot powered by Snowflake Cortex Search. The solution ingests unstructured documents and enables natural language search.
AI & Machine Learning
AI Agents & Chatbots
Snowflake

Overview

Facing information overload for students

Supporting thousands of students across hundreds of global programs, a nonprofit study abroad and intercultural exchange organization faced a major challenge: critical information—such as travel logistics, visa requirements, lodging info, and cultural context—was scattered across numerous unstructured sources.

Students struggled to locate critical information, often leading to confusion and repeated questions directed to support teams. The organization needed a centralized, intelligent solution to streamline access to program-specific information and relieve the burden on internal staff.

Our Solution

An AI chatbot powered by Snowflake Cortex

To enhance the student experience and increase operational efficiency, OneSix developed a robust conversational AI chatbot, seamlessly integrated into the organization’s website and powered by Snowflake Cortex Search.

Key Capabilities
  • Document Ingestion & Indexing: The chatbot ingests thousands of unstructured documents, including:
  • Program-specific reading materials (PDFs)
  • Location-based “Know Before You Go” guides (PDFs)
  • Content collected via web scraping
  • Natural Language Search: Students can ask intuitive, conversational questions such as:
  • “Do I need a visa for Seville?”
  • “Will I receive college credit?”
  • “What’s included in the housing package?”
  • “How do I get around?”
  • Real-Time, Reliable Responses: Answers are grounded in the organization’s official documentation, ensuring accuracy and consistency.
Technical Implementation
  • Advanced Chunking & Preprocessing: Custom pipelines split content into meaningful, searchable segments.
  • LLM Prompt Engineering: Carefully crafted prompts optimize answer accuracy, context relevance, and tone.
  • Custom Search Tuning: Retrieval and ranking strategies were tailored using content type and user behavior.
  • Backend Integration: Python-enhanced logic is fully embedded in Snowflake SQL stored procedures and UDFs.
  • Optimized for Performance: Fine-tuned Snowflake Cortex implementation balances cost, speed, and accuracy for enterprise-grade performance.

Results

Transforming the student support experience

  • Reducing support load: Internal teams now rely on centralized, AI-powered search to answer student questions more efficiently.
  • Enhancing the student experience: Students benefit from 24/7 access to trusted information, increasing their confidence and satisfaction.
  • Earning internal praise: Staff report fewer repetitive queries and a noticeable improvement in operational efficiency.

Ready to unlock the full potential of data and AI?

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Modernizing healthcare data management for security and efficiency

  • Healthcare & Life Sciences

Modernizing healthcare data management for security and efficiency

OneSix partnered with a healthcare data company to build a secure, scalable data platform using Snowflake, Python, and Sigma—enabling centralized data management, automated reporting, and robust governance.
Data & App Engineering
Data Analytics
Snowflake
AWS
Sigma

Overview

Scaling healthcare data with security and efficiency

A healthcare data company managing a complex network of data sources needed a scalable platform to support growing reporting needs while maintaining strict data privacy standards. With a lean team, they required a solution that balanced performance, security, and ease of maintenance. Their goals included:

  • Centralizing diverse data sources into a unified registry
  • Implementing robust role- and row-level security for end users
  • Developing a cost-effective, low-maintenance solution
  • Enabling audit capabilities, including user-level reporting activity tracking

They selected OneSix for its expertise in HIPAA-compliant environments, deep technical knowledge across Snowflake, Python, and data visualization, and its ability to provide strategic, personalized support aligned with business goals.

Our Solution

A secure, scalable, and intelligent data ecosystem

OneSix designed and implemented a modern data architecture using Snowflake, Python, and Sigma. Snowflake served as the core data infrastructure, enabling scalability, security, and governance. Cloud-based Python pipelines were used to extract, stage, and transform data efficiently.

To streamline operations and reduce data movement, Snowflake data shares were used to transition data between environments. Automated data transformations were powered by Snowflake-hosted Python scripts, tasks, and stored procedures. Row-level security and custom roles ensured sensitive data was protected at all times.

For reporting, Sigma was embedded into the client’s front-end application, delivering seamless, self-serve analytics. OneSix also supported external data-sharing needs via Snowflake and provided hands-on support for customer-specific reporting, training, and dashboard development.

Additionally, OneSix supported AI and machine learning initiatives by assisting with clinical pilot programs, leveraging EMR data, and refreshing predictive models for clinical outcomes. Snowflake’s governance tools, including built-in query history, ensured ongoing compliance and audit readiness.

Results

Driving efficiency, growth, and innovation

The implementation significantly improved operational efficiency and data governance. Snowflake-based architecture simplified data pipelines, reduced maintenance, and supported the development of new data products and services.

Key outcomes included:

  • Reduced support ticket volume through enhanced end-user training and documentation
  • Streamlined operations with lower maintenance overhead
  • New revenue opportunities through enhanced service offerings and data-sharing capabilities
  • Improved compliance and auditing via built-in Snowflake governance tools

The client reported high satisfaction with OneSix’s strategic partnership, ongoing support, and ability to deliver scalable, secure, and forward-thinking solutions tailored to their evolving business needs.

Ready to unlock the full potential of data and AI?

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Modernizing data architecture to unlock business insights for a financial services firm

  • Financial Services

Modernizing data architecture to unlock business insights for a financial services firm

OneSix partnered with a financial services firm to modernize its data architecture, implementing a scalable platform using Snowflake, dbt, Matillion, and Sigma to unify over 50 data sources, automate reporting, and deliver real-time insights.
Data & App Engineering
Data Analytics
Snowflake
AWS
Matillion
dbt Labs
Sigma
Fivetran

Overview

Transforming data management to drive operational excellence

A financial services firm specializing in ETF investment and technology platforms faced challenges in managing its data across multiple sources. The absence of a unified and scalable data platform hindered efficient reporting, data quality, and visibility into performance metrics, limiting the ability to meet both internal and external demands. The client sought to establish a robust data platform to support diverse use cases, streamline operations, and improve decision-making.

The goals of the project included:

  • Creating a centralized and scalable data platform for real-time reporting and insights.
  • Enabling seamless integration of data from internal and external sources.
  • Improving operational efficiency through automated reporting and analytics.

The client chose OneSix for its expertise in data architecture, cloud technologies, and demonstrated success in delivering similar solutions within the financial services industry.

Our Solution

Modern architecture and automation for seamless data management

OneSix implemented a comprehensive solution leveraging modern data architecture principles and cutting-edge technologies—including Snowflake, dbt Core, Fivetran, Matillion, and Sigma—to address the client’s data management challenges.

The project unfolded in three distinct phases:

Phase 1: Data Ingestion

  • Conducted discovery and strategy sessions to assess requirements and define the architecture.
  • Designed a secure and scalable data platform architecture using Snowflake.
  • Built a data lake to support ingestion from over 50 data sources.
  • Implemented dbt Core with Matillion for automated ETL pipelines and robust data quality tests.

Phase 2: Portal Build

  • Selected and implemented advanced ETL tools for efficient data transformation.
  • Developed a data warehouse for AUM (Assets Under Management) reporting and analytics.
  • Created an internal portal to provide teams with real-time insights into ETF performance through consolidated dashboards.

Phase 3: Additional Reporting

  • Automated 13F filings for compliance with SEC reporting rules, reducing manual effort and improving accuracy.
  • Built automated reporting for fund administration, legal registration statements, and board reports.
  • Integrated the data lake with external systems to facilitate seamless data sharing with third parties.

Results

Improved efficiency, compliance, and operational visibility

The implementation of the scalable data platform delivered measurable impact across key areas of the business:

  • Streamlined Operations: Ingested data from over 53 sources into a centralized data lake and warehouse, supporting consistent daily reporting.
  • Increased Efficiency: Automated AUM reporting and SEC filings, significantly reducing manual effort and ensuring regulatory compliance.
  • Improved Data Quality: Applied rigorous data validation using dbt Core and Matillion to maintain accuracy and consistency.
  • Real-Time Insights: Deployed internal portals and dashboards for up-to-date visibility into ETF performance.
  • Time & Cost Savings: Automated 13F filings, reducing errors and freeing up team resources.
  • Expanded Capabilities: Integrated with external systems to enable secure, scalable data sharing.

With a modern, automated data foundation in place, the client now benefits from faster decision-making, improved compliance, and a platform built to support ongoing innovation and growth.

Ready to unlock the full potential of data and AI?

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Reducing data processing time from 10+ hours to minutes for a pricing consultancy

  • Consulting

Reducing data processing time from 10+ hours to minutes for a pricing consultancy

OneSix helped a pricing consultancy reduce data processing time from over 10 hours to minutes by implementing a modern ELT pipeline using Azure Data Factory and Snowflake. The solution enhanced agility, scalability, and confidence in pricing insights.
Data & App Engineering
Data Analytics
Snowflake
Microsoft Azure

Overview

Unlocking faster, scalable pricing insights

A strategic pricing consultancy faced challenges in processing large-scale customer pricing data. Their existing solution relied on Python scripts and Excel, which struggled to handle the increased data volume from a new customer. Processing times ballooned to over 10 hours, making iterative analysis impractical and hindering their ability to provide timely pricing insights.

The consultancy needed a flexible, scalable, cloud-based solution that could process data rapidly, support iterative experimentation, and lay the groundwork for a pricing platform that could accommodate additional customers while maintaining data security and isolation.

Our Solution

A modern ELT pipeline to optimize data processing

OneSix designed and implemented a modern ELT (Extract, Load, Transform) data processing pipeline leveraging Azure Data Factory and Snowflake. The architecture streamlined data ingestion, transformation, and analytics, delivering rapid performance and scalability. Key elements of the solution included:

  • Automated Data Ingestion: Azure Data Factory ingested raw customer data on a scheduled basis into the client’s Snowflake environment.
  • Medallion Architecture: The pipeline followed a medallion architecture (bronze, silver, gold layers) to clean, enhance, and transform raw data into actionable pricing insights and recommendations.
  • Data Processing: Processing was conducted with a combination of Snowflake SQL and Python for flexible and powerful transformations.
  • Interactive Analytics: A Snowflake Streamlit application allows customers to view insights, experiment with pricing configurations, and analyze results in real time.
  • Seamless Data Export: Azure Data Factory exported processed data products into the customer’s SQL database for further analysis and reporting.
  • CI/CD Integration: The entire Snowflake environment was managed using CI/CD pipelines via GitHub workflows and Snowflake’s Git integration tools for declarative schema management.
Technologies Implemented
  • Snowflake
  • Python
  • Azure Data Factory
  • Streamlit

Results

Achieving fast performance, enhanced agility, and seamless scalability

The solution drastically improved data processing efficiency, reducing execution time from over 10 hours to just minutes or seconds. This rapid performance enabled:

  • Faster Iterative Analysis: Clients could quickly experiment with changes to the pricing engine and configurations, facilitating a more iterative and agile approach to pricing strategy.
  • Enhanced Confidence: The ability to rapidly analyze data and refine pricing models helped build confidence in the pricing recommendations and insights provided.
  • Scalability and Flexibility: The architecture laid the foundation for onboarding additional customers seamlessly, with reusable components and strict data isolation for security.

The consultancy praised the performance and adaptability of the new solution, highlighting how the architecture enabled rapid, iterative development and delivered consistent, high-quality pricing insights.

"OneSix’s solution transformed our data processing capabilities. What used to take hours now takes seconds, allowing us to experiment and deliver pricing insights with unprecedented speed and accuracy."

Ready to unlock the full potential of data and AI?

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Crafting an AI-powered recommendation engine for a private equity service provider

  • Financial Services

Crafting an AI-powered recommendation engine for a private equity service provider

OneSix developed a recommendation engine for a private equity concierge service, using a large language model to match the best service providers from a large network, integrated into Salesforce with real-time chatbot functionality.
AI & Machine Learning
AI Agents & Chatbots
Snowflake
Fivetran

Overview

Streamlining service provider selection for PE concierge service

Our client connects private equity (PE) firms with service providers (SPs) for tasks such as diligence, value creation, and prep-for-sale, offers a “white-glove” concierge service. Their challenge was speeding up the process of selecting the best SPs from their large network to match new client needs, ensuring high-quality, timely recommendations for every new concierge.

Our Solution

AI-driven recommendation engine for efficient provider matching

OneSix built a recommendation engine using a domain-general large language model (LLM) to extract, distill, and embed unstructured data about past concierges and SP qualifications into a searchable vector database. This system allows agents to quickly search for and find the most relevant past concierges and SPs.

To enhance the relevance of the search results, a proprietary re-ranking model was included, ensuring that the recommendations are hyper-relevant and aligned with the client’s core sales metric. The engine was integrated into their Salesforce frontend via an API, and also exposed a natural language “chatbot” interface for real-time recommendations based on user input data.

Additionally, the system leverages existing data from Salesforce, Fivetran, and Snowflake, creating a specialized data-science schema to feed the recommendation engine. The Python app, connected to Snowflake via the Snowflake Python connector, powers the entire solution.

Results

Enhanced speed and quality in service provider selection

The recommendation engine is now used for all incoming concierge requests. Within Salesforce, agents receive a top-10 list of recommended SPs for each new concierge, allowing them to quickly choose the best-fit providers.

Success is measured by the percentage of engaged SPs that originated from the system’s recommendations, typically ranging between 60-80% each week. The solution has significantly streamlined the company’s SP selection process, delivering fast, high-quality matches to clients.

Ready to unlock the full potential of data and AI?

Book a free consultation to learn how OneSix can help drive meaningful business outcomes.

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Optimizing data pipeline efficiency to reduce cloud costs for an amusement park

  • Travel & Hospitality

Optimizing data pipeline efficiency to reduce cloud costs for an amusement park

By analyzing and adjusting data processing schedules, OneSix enabled a seaside amusement park to significantly reduce unnecessary cloud expenses while maintaining scalability during peak demand. This optimization improved cost efficiency and ensured the pipeline aligned with real-time business needs.
Data & AI Strategy
Data & App Engineering
Snowflake

Overview

Optimizing Snowflake costs for smarter data spend

Many organizations invest in modern cloud data platforms like Snowflake to support data-driven decision-making and innovation. However, without proper cost management, these investments can grow disproportionately to their value. A seaside amusement park client approached us with a significant challenge: their Snowflake costs were spiraling beyond expectations despite what they believed should be a relatively modest data workload. They needed clarity on cost drivers and actionable recommendations to align spending with business outcomes.

Our Solution

Executing in-depth cost analysis and actionable strategies

We performed a comprehensive analysis of the organization’s Snowflake environment, focusing on spend trends, workload efficiency, and resource allocation. The engagement included:

  • Macro-level spend assessment: Identified trends and major cost contributors within the organization’s Snowflake environment.
  • Detailed workload profiling: Drilled into high-cost applications and workloads, uncovering inefficiencies driving excess expenditure.
  • Actionable cost-saving recommendations: Delivered a set of tailored strategies to reduce costs while maintaining operational effectiveness.

Two primary inefficiencies emerged during our analysis:

  • High-cost applications with minimal value: One application alone consumed over 30% of total Snowflake costs, but didn’t represent significant business value.
  • Overly frequent processing schedules: Frequent data loads without real-time needs can inflate costs. Aligning processing frequency with business value reduces waste and maintains efficiency.

To sustain cost efficiency, we suggested leveraging Snowflake’s built-in tools, such as resource monitors, auto-suspend settings, and budgets with usage alerts, ensuring proactive cost management aligned with evolving business needs.

Results

50% cost reduction and a foundation for long-term efficiency

Our engagement delivered immediate and impactful results:

  • 50% reduction in Snowflake spend: Shutting down the underperforming application and adjusting the data pipeline frequency achieved nearly half of the total savings.
  • Improved cost visibility and control: Introducing workload-specific warehouses allowed precise tracking of resource usage, enabling better budget alignment.
  • Sustainable cost management: Implementing Snowflake’s monitoring tools created a feedback loop for ongoing optimization.

These changes not only aligned Snowflake costs with the organization’s data workloads but also created a foundation for sustainable growth. By reducing unnecessary expenses and enhancing efficiency, the organization could reinvest savings into high-value data initiatives, driving innovation and competitive advantage.

Are you looking to optimize your cloud data spend and unlock the full potential of your Snowflake investment? Contact us to learn how we can help you achieve impactful results like these.

Ready to unlock the full potential of data and AI?

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Unifying data for enhanced portfolio insights and reduced risk​ in private equity

  • Financial Services

Unifying data for enhanced portfolio insights and reduced risk​ in private equity

By creating a scalable, unified data infrastructure, OneSix enabled a private equity firm to gain a comprehensive view of portfolio performance, improve decision-making, and drive long-term profitability.
Data & App Engineering
Data Analytics
Snowflake
Matillion
Fivetran

Overview

Building a modern data infrastructure for strategic agility

A private equity firm managing a diverse portfolio of investments faced challenges in leveraging data for strategic decision-making. The firm recognized the need for a modern data infrastructure to consolidate disparate datasets, enhance analytical capabilities, and gain deeper insights into overall portfolio performance.
The existing data infrastructure was fragmented, leading to delays in reporting, inefficient data processing, and a lack of agility in adapting to evolving business requirements.

Our Solution

Data unification across portfolio companies

OneSix built a modern data infrastructure, which is essential for centralizing data and gaining deeper insights into overall portfolio performance. This involved creating a unified data repository to integrate and store data from various portfolio companies, providing a unified view for analysis and reporting.
As the firm increases the portfolio, the architecture was designed to allow seamless scalability, accommodating growing datasets without compromising performance. The solution leverages Snowflake’s data sharing features to securely share relevant data between portfolio companies and the PE firm, fostering collaboration and knowledge exchange.

Results

A holistic picture of portfolio performance

The implementation of a comprehensive data infrastructure provided the private equity firm with a holistic view of portfolio performance while ensuring data security and integrity.

This empowered the firm to make more informed decisions, enhance operational efficiency, and gain deeper insights into their investments, ultimately mitigating risks and fostering long-term profitability.

Ready to unlock the full potential of data and AI?

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Building a Customer 360 data solution for Leader Bank

  • Financial Services

Building a Customer 360 data solution for Leader Bank

By implementing a unified Customer 360 data solution, OneSix enabled Leader Bank to enhance customer service, drive data-driven marketing, improve data quality, and boost operational efficiency.
Data & App Engineering
Data Analytics
Snowflake
Matillion
Fivetran
Power BI

Overview

Overcoming data silos to gain a unified view of customers

To maintain a competitive edge, community banks need to have a deep understanding of customer behavior and preferences. Leader Bank, like many financial institutions, faced a common challenge: fragmented systems and data silos. They had separate platforms for commercial lending, residential lending, and core banking operations, with little interaction between them. This siloed approach made it incredibly difficult to establish a comprehensive view of their customers.

Back-office processes were cumbersome, and customer support teams found it challenging to provide efficient service due to a lack of unified customer data. To compound matters, data quality issues, including inconsistent naming conventions and formats, added complexity to the situation.

Our Solution

Implementing a Customer 360 platform with a cloud-based infrastructure

OneSix partnered with Leader Bank to tackle these issues head-on, devising a comprehensive solution that aimed to provide a holistic view of each customer. The key components of this solution included:

  • Master Data Management (MDM) Strategy: OneSix developed a robust MDM strategy to standardize data across the organization, ensuring consistent naming conventions and formats. This laid the foundation for accurate data integration.
  • Cloud-Based Data Infrastructure: To create a unified platform for customer data, OneSix implemented a cloud-based data lake and data warehouse. This allowed for the storage and processing of vast amounts of data from different systems in a scalable and cost-effective manner.
  • Rules Engine: A rules engine was introduced to automate data validation, cleansing, and transformation. This ensured that data was accurate, up-to-date, and adhered to the defined standards.

The goal of these components was to establish a common data platform where information from various systems could seamlessly integrate. This would enable Leader Bank to tie together customer interactions, track their history, and gain valuable insights for personalized marketing and improved customer service.

Technologies Implemented
  • Snowflake served as the host to collect data into the data lake across various systems. A C360 data warehouse served the BI and Customer information needs of this bank.
  • Fivetran was used to capture data across a variety of data sources to move data into the data lake. In addition, Snowflake Data Share provided access to raw data for a Snowflake-based system.
  • Matillion was used as the rules engine to cleanse data and provide match/merge rules for the customer record and transform that into the data warehouse.
  • PowerBI was used to drive analytics and display Customer 360 information across the institution.

Results

Enhanced customer service, data-driven marketing, and operational efficiency

The implementation of the Customer 360 solution brought about significant positive changes:

  • Improved Customer Service: With a unified view of customer data, the bank’s support teams could quickly identify callers and access their interaction history. This streamlined the support process, leading to improved service and reduced call handling time.
  • Data-Driven Marketing: To create a unified platform for customer data, OneSix implemented a cloud-based data lake and data warehouse. This allowed for the storage and processing of vast amounts of data from different systems in a scalable and cost-effective manner.
  • Enhanced Data Quality: The MDM strategy and rules engine led to improved data quality. Data inconsistencies were eliminated, resulting in more reliable and accurate information for decision-making.
  • Increased Efficiency by 2X: Back-office processes were streamlined, reducing operational inefficiencies. This led to cost savings and increased productivity across the organization.
  • Increased Customer Satisfaction: With improved service and personalized experiences, customers were more satisfied, leading to stronger customer loyalty and potential for business growth.

Ready to unlock the full potential of data and AI?

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Maximizing casino player profitability with a one-to-one marketing engine

  • Travel & Hospitality

Maximizing casino player profitability with a one-to-one marketing engine

By implementing a one-to-one marketing engine with real-time personalization, OneSix helped a casino gaming provider enhance player experience and boost profitability through tailored offers.
AI & Machine Learning
AI-Driven Marketing
Snowflake

Overview

Revolutionizing loyalty marketing in casino gaming

Casino gaming is a high-energy industry that continually seeks to use state-of-the-art technology to improve the player experience. However, most loyalty marketing programs in the industry still follow traditional, tier-based direct marketing approaches. Using this model, casino patrons are evaluated based on monthly spending data, then categorized into low- to high-value tiers. Promotions like free play, restaurant discounts, and hotel perks are allocated by tier, with players receiving updates via direct mail or email.

This legacy system has significant drawbacks. Promotions are distributed only once a month, resulting in outdated information by the time offers reach players. Additionally, the process is labor-intensive and static, failing to respond to real-time changes in player behavior. Since players are grouped into broad value categories, the traditional approach also lacks personalization, preventing casinos from crafting customized offers that reflect individual preferences and engagement patterns.

In response to these limitations, our client, a leading casino gaming provider, partnered with OneSix to develop a solution capable of engaging players individually. The goal was to build a dynamic marketing engine that would automatically learn from players’ behaviors both online and in-casino, creating a personalized, real-time experience for each player.

Our Solution

Building a one-to-one marketing engine for personalized engagement

OneSix designed and implemented a one-to-one marketing engine powered by a distributed reinforcement learning platform, tailored specifically for the gaming industry’s needs. Recognizing that each player’s engagement is driven by unique behaviors, we developed a platform capable of continuously learning and adapting to individual player activity. This multi-channel “next best action” engine integrates data from multiple sources to generate a comprehensive, real-time view of each player.

The system leverages this data to make intelligent, individualized decisions, including when and how to communicate with each player. The engine streams out optimal actions that maximize engagement, offering personalized incentives and communication across various channels, such as email, direct mail, online platforms, in-app messages, and SMS. This approach allows the casino to engage each player based on their current behavior, creating a highly responsive, individualized experience that feels more relevant and timely than traditional loyalty programs.

In addition to building this powerful engagement engine, OneSix ensured that the platform would continue learning over time. Our solution includes a feedback loop that adjusts strategies based on player response, optimizing interactions to align with evolving preferences. This adaptive approach allows the casino to nurture player relationships effectively, combining the best of real-time analytics with advanced AI-driven marketing capabilities.

Results

Enhanced player experience and increased profitability

The deployment of this one-to-one marketing engine has transformed the client’s approach to player engagement, providing a more immediate and personalized experience for casino patrons. Players now receive offers tailored to their individual habits and preferences, and these offers are refreshed in near-real-time as new player data is collected.

Experimental testing of the platform has demonstrated substantial improvements in player profitability, as well as enhanced engagement and loyalty among both new players and those migrated from traditional loyalty programs. By responding to shifts in player activity and delivering customized offers across multiple channels, the casino has seen a marked increase in player satisfaction and profitability.

$450M

Annual player reinvestment

10%

Increase in player visits

6%

Increase in player profitability

Ready to unlock the full potential of data and AI?

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Uncovering insights into member engagement for a digital health company

  • Healthcare & Life Sciences

Uncovering insights into member engagement for a digital health company

OneSix implemented a scalable data warehouse solution for a digital health company, enabling efficient, accurate reporting and data-driven insights into member engagement across texting programs.
Data & App Engineering
Data Analytics
Snowflake
Matillion
Fivetran
Power BI

Overview

Streamlining insights into member behavior

A digital health company was looking to better handle its customers’ needs by providing insights into member behaviors within its texting programs.

Our client provides their customers with various metrics with regard to participation and engagement in their many texting program offerings. As requests come in for new information and/or slight variations in existing reports, our client needed a better way to meet these requests.

The existing process was very manual and prone to errors, sometimes delaying turnaround time in responding to customers’ data requests. This process impeded the client’s ability to create new reports or aggregate reports across multiple programs for each customer. The current reporting could represent a point in time but did not easily allow for trends over time.

Our Solution

Building a flexible data warehouse with automated ETL

OneSix designed and built a data warehouse that could handle the client’s current and future needs in both reporting and data analytics. We created a data model that could handle today’s reporting needs while allowing the flexibility to add on later. 

Using Fivetran, we brought their source data, SQL Server and DynamoDB, into a Snowflake database. Using Matillion, we built a fully automated ETL process with fewer steps and a faster turnaround time to provide up-to-date data. The reporting and analytics are now available in Power BI.

Technologies Implemented
  • Snowflake was utilized as the data warehouse to store engagement and messaging data.
  • Fivetran was leveraged to fully replicate data from AWS DynamoDB and SQL Server on RDS to a data warehouse.
  • Matillion was used to efficiently and quickly transform source data into an appropriate schema for reporting needs.
  • Power BI provided the internal and customer facing reports for actionable insights and trends.

Results

A single source of truth for decision-making and trend analysis

With this solution and knowledge transfer, the organization is able to leverage the analytics output from our project as a template for building future reporting. A single source of truth from the cloud data warehouse allows the business to look at the numbers with confidence for decision-making.

Key financial metrics are available to view by snapshot, trends, as well as drill-down to more granular components in a visual, easy-to-digest method. The labor reporting has already yielded insight into better management of overtime for the workforce.

Ready to unlock the full potential of data and AI?

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