Our Work
Past Experiences
Exploring real-world challenges and delivering tangible results is at the heart of what we do. This section showcases a selection of projects where we’ve applied our data and analytical expertise to solve specific business problems, drive efficiency, and inform strategic decisions. Each entry provides a glimpse into the context, our approach, and the impact achieved.
Here are some examples of our past work:
Data Solutions
Scalable Data Platform Implementation
Context and Challenge
Working within a very large Fortune 500 tech company, the need was to build a robust, scalable data platform capable of handling massive datasets (starting with 4 billion rows and growing by 5 million daily) ingested from diverse sources and supporting reliable, high-performance analytics and reporting for business clients.
Role and Contributions
We played a key role in designing and implementing the data infrastructure on Google BigQuery. We are responsible for setting up project environments, developing ETL processes, ensuring data quality, optimizing query performance, and scheduling data loads. Technologies Used: Google BigQuery, Google Cloud Platform, SQL, Google Looker Studio, Power BI.
Approach and Solution
Implemented a structured approach using three distinct Google Cloud projects: one for raw data ingestion from multiple sources, a second for a development environment used for ETL, SQL testing, and dataset validation (focusing on accuracy, quality, consistency, and query cost optimization), and a third for the production environment. Developed processes to finalize tables, views, and materialized views in development before promoting them to production. Created and managed daily schedules for inserting data into production tables. Enabled business reporting by connecting Google Looker Studio and Power BI to the production datasets.
Key Outcomes and Impact
Successfully built and maintained a high-volume, petabyte-scale data platform on Google BigQuery capable of handling billions of rows and supporting daily ingests from diverse sources. Provided a reliable and performant foundation for business reporting and dashboards, empowering business clients with access to large-scale data for decision-making. Ensured data quality and query cost efficiency through dedicated development and validation processes.
Integrated Data and Analytics Platform Development
Context and Challenge
The need was to develop and manage a modern, integrated data and AI platform on Microsoft Fabric to improve data availability, enable self-service analytics, support business reporting, and facilitate complex data transformations.
Role and Contributions
We were involved in developing and implementing various components of the data and AI platform on Microsoft Fabric. Our contributions spanned data ingestion, processing, data lakehouse design, data warehousing, and integrating analytical tools. Microsoft Fabric (Data Factory, OneLake, SQL Data Warehouse, Data Engineering/Spark notebooks, Direct Lake), Power BI.
Approach and Solution
Developed and managed data pipelines using Microsoft Fabric Data Factory to ingest and process data from disparate sources. Designed and implemented a Data Lakehouse leveraging Microsoft Fabric OneLake for storing and managing large volumes of data with schema flexibility. Built a SQL Data Warehouse within Fabric, focusing on schema optimization and indexing strategies to improve query performance for business-critical reporting. Utilized Spark notebooks within the Fabric Data Engineering environment to perform complex data transformations and feature engineering. Integrated Power BI reports directly with the Fabric Lakehouse using Direct Lake connectivity to provide interactive, high-performance visualizations.
Key Outcomes and Impact
Created a more integrated and efficient data platform on Microsoft Fabric. Improved data availability for downstream analytics. Enabled self-service data access for data analysts through the Data Lakehouse. Enhanced query performance for reporting via the SQL Data Warehouse. Facilitated complex data preparation using Spark notebooks. Significantly improved report load times and interactivity by leveraging Direct Lake integration with Power BI. Empowered business users with user-friendly analytical solutions.
Analytics Solutions
Operational Performance Monitoring Dashboard
Context and Challenge
A large retail chain needed better visibility into the performance of their service delivery operations to identify areas for improvement and ensure key service level agreements (SLAs) were met. The challenge was to consolidate operational data and present key metrics in a clear, actionable format for monitoring and optimization.
Role and Contributions
We developed an operational performance monitoring and optimization dashboard to track key service delivery metrics and provide insights into performance trends across different regions. Data integration/processing tools (likely SQL, potentially others), Data Visualization/BI tool (e.g., Power BI, Looker Studio).
Approach and Solution
Collected and analyzed operational data related to job notifications, first-time on-site rates, job go-back rates, call center performance, alarm thresholds, and notification list updates. Designed and built a dashboard (likely in a tool like Power BI, given previous examples) that visualized key performance indicators (KPIs) against targets and showed month-over-month (MoM) and year-over-year (YoY) comparisons. Included regional breakdowns and specific metrics like average job go-back percentage and time to repair.
Key Outcomes and Impact
Delivered a comprehensive dashboard providing clear visibility into the operational performance of service delivery. Enabled stakeholders to monitor key metrics, identify underperforming areas (e.g., first-time on-site rates, call answer times), and track improvements (e.g., reduction in job go-back, improved time to repair). Supported data-driven decision-making to optimize field operations, improve service quality, and enhance customer satisfaction by focusing on critical performance indicators.
Patron Segment Analysis and Targeted Marketing Strategies
Conducted a comprehensive segmentation analysis of library users to uncover behavioral patterns and engagement trends across a diverse patron base. Leveraged K-means clustering and multi-year interaction data to identify distinct user groups and developed tailored marketing strategies to better align services with community needs. Delivered insights that empowered more personalized outreach and deeper patron engagement.
Context and Challenge
A library needed to better understand its diverse user base to tailor services and marketing efforts effectively in an evolving landscape. The challenge was to identify unique patterns of engagement, preferences, and behaviors within the library's user base.
Role and Contributions
We conducted a detailed analysis to create distinct patron segments and developed targeted marketing strategies based on the segmentation insights.
Approach and Solution
The approach involved collecting and integrating three years of comprehensive patron interaction data from multiple library systems using Microsoft SQL Server. K-means Clustering was applied using Python programming to identify four distinct patron segments: Established Patrons, Community Connectors, Digital Natives, and Selective Borrowers. Power BI was used for data visualization to illustrate findings and inform strategies.
Key Outcomes and Impact
Delivered a detailed understanding of patron segments, highlighting their characteristics, engagement levels, and preferences. Developed tailored marketing strategies designed to enhance patron engagement within each specific segment, aiming to foster a deeper connection between the library and its community.
Polaris Collection Optimization and Management Guidelines
Context and Challenge
A library needed to optimize its collection based on user demand and data-driven insights to maximize resource efficiency and improve user satisfaction. The challenge was to analyze the popularity, usage patterns, and cost efficiency of various collection items.
Role and Contributions
We conducted an in-depth analysis of collection data to provide insights and outline strategic recommendations for improving collection quality, relevance, and engagement.
Approach and Solution
Collected and analyzed operational data related to job notifications, first-time on-site rates, job go-back rates, call center performance, alarm thresholds, and notification list updates. Designed and built a dashboard (likely in a tool like Power BI, given previous examples) that visualized key performance indicators (KPIs) against targets and showed month-over-month (MoM) and year-over-year (YoY) comparisons. Included regional breakdowns and specific metrics like average job go-back percentage and time to repair.
Key Outcomes and Impact
Delivered a comprehensive dashboard providing clear visibility into the operational performance of service delivery. Enabled stakeholders to monitor key metrics, identify underperforming areas (e.g., first-time on-site rates, call answer times), and track improvements (e.g., reduction in job go-back, improved time to repair). Supported data-driven decision-making to optimize field operations, improve service quality, and enhance customer satisfaction by focusing on critical performance indicators.
Digital Marketing Channel Optimization
Context and Challenge
A business needed to analyze the performance of different marketing channels to optimize lead generation and conversion efficiency. The challenge was to understand metrics like cost per lead, conversion rates, and ROI across various channels. My Role & Contributions: I developed a Lead Performance Dashboard and conducted analysis to evaluate the effectiveness of different marketing channels.
Role and Contributions
We developed a Lead Performance Dashboard and conducted analysis to evaluate the effectiveness of different marketing channels.
Approach and Solution
Analyzed lead volume, Cost per Lead, and Conversion Rate by Channels such as Channel 1, Channel 2, CPCMVP, CPCPrime, and Direct Mail. Evaluated Loan Conversion Rate and Cost per Loan by channel. Calculated Gross Margin Return On Investment (ROI) for each channel. Assessed loan distribution by channel and risk level (Fraudulent, Bad, Good Loan). Examined Risk Level distribution (Very Low to Very High) across channels.
Key Outcomes and Impact
Provided a clear view of channel performance based on key metrics like cost, conversion, and ROI, enabling data-driven decisions for marketing spend allocation. Analyzed lead quality by channel based on loan outcomes and risk levels.
Vertical Market Analysis for Growth Opportunities
Context and Challenge
A company needed to identify potential growth opportunities within different vertical markets. The challenge was to analyze market data and customer distribution to pinpoint promising sectors.
Role and Contributions
We conducted a vertical market analysis to assess potential growth opportunities and market penetration.
Approach and Solution
Analyzed customer distribution and market share across various vertical markets for different companies. Calculated and presented "Index-Band Over A" and "Index-Sales Over Base by Vertical Market" to compare sales performance relative to market presence. Visualized potential growth markets on a map based on business counts per CMA.
Key Outcomes and Impact
Provided a data-backed analysis identifying vertical markets with higher growth potential or areas where sales performance exceeded market presence. Delivered insights to inform strategic decisions regarding market focus and resource allocation for growth.
Growth Teams Performance Analysis
Context and Challenge
A business needed to evaluate the performance of its growth teams and understand key metrics related to lead management and conversion over time.
Role and Contributions
We analyzed growth team performance data, focusing on lead volume, cost, verification, and conversion rates across different channels and lead priorities.
Approach and Solution
Tracked and visualized lead volume and Cost per Lead over time (weekly trends). Analyzed Verified leads and Conversion Rate (Lead to Verified Ratio). Examined Leads Priority Distribution (High, Medium, Low Priority) and their respective conversion rates. Analyzed channels performance trend over time for Cost per Lead, Cost per Verified, Lead volume, Verified leads, and Spend across channels like AdWords, Display, LinkedIn, and Organic.
Key Outcomes and Impact
Provided detailed performance insights for growth teams and marketing channels over time. Identified the conversion rates associated with different lead priorities. Delivered data to optimize lead handling processes and channel investment based on performance metrics.
Lead Life Cycle Analysis
Context and Challenge
A business needed to understand the journey of a lead from initial contact through to conversion and installation, analyzing conversion rates and timings at each stage.
Role and Contributions
I analyzed the lead conversion funnel and the average time taken for leads to progress through different stages of the lifecycle.
Approach and Solution
Mapped out the Lead Conversion Funnel (Total Leads -> Leads To PA (Appts & PhSales) -> Leads To Sales -> Leads To Installs). Analyzed Appointment and Phone Sales conversion funnels separately. Examined Lead conversion steps and shrinkage, detailing disposition codes and their frequency. Calculated Average Days of Leads Conversion for stages like Lead To PA, PA To Sales, and Sales To Install. Analyzed average days to conversion broken down by different day groups (e.g., 0-3 Days, 4-6 Days, 7-10 Days).
Key Outcomes and Impact
Provided clear visibility into the performance and bottlenecks within the lead conversion process. Identified typical timings for leads to move through the lifecycle stages. Delivered insights to optimize sales processes and follow-up strategies based on conversion rates and duration at each step.
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