Data & Analytics
Consulting
Your business generates data every day. We make sure it actually tells you something useful.
Most companies have data spread across dozens of systems, and none of it connects into one clear picture. We build the data architecture and analytics layer that gives your leadership team real answers, not more spreadsheets to argue about.
Companies we've worked with
Every engagement ends with a single, authoritative data layer. No more competing spreadsheets, no more conflicting reports, no more meetings spent arguing about whose numbers are right.
Dashboards and reports that reflect what is happening now, not what happened last month when someone finally had time to compile the spreadsheet.
We build analytics around decisions, not around dashboards. Every metric we put in place answers a question your business actually needs answered.
Data Without Architecture Is Just Noise
The most common data problem we see is not a lack of data. It is a lack of structure. Your business generates huge amounts of information across Salesforce, your ERP, Microsoft Dynamics 365, spreadsheets, and a handful of other tools. None of them talk to each other, and none of them give you a complete picture on their own.
So leadership ends up making decisions based on partial information, delayed reports, or gut feeling. When someone finally builds the report that shows what is actually happening, the response is always the same: "Why did we not know this sooner?"
The answer is almost always architecture. Not more tools. Not more dashboards. A data architecture that is designed around the decisions your business needs to make.
Data Silos Everywhere
Customer data lives in your CRM. Financial data sits in your ERP. Operations tracks things in spreadsheets. Getting a complete picture means someone has to pull from all of them manually, and by the time they do, the numbers are already stale.
Reports That Arrive Too Late
If your month-end reports take a week to compile, they are not helping you make decisions. They are telling you what already happened. By the time you see the numbers, the window to act on them has closed.
Conflicting Numbers Across Teams
Sales says revenue is up. Finance says margins are down. Operations says everything is on track. Different teams pull different numbers from different sources, and leadership spends meetings debating data instead of deciding what to do.
No Way to See What Is Coming
You can see what happened last quarter. But can you see what is likely to happen next quarter? Most businesses are still running on rearview mirror analytics when they could be forecasting trends and catching problems early.
From Raw Data to Real Answers
We work across your entire data stack, from where the data is stored to how it reaches the people who need it. Here is what that looks like in practice.
Data Architecture Design
We evaluate how your data is structured and whether it can support where your business is headed. That includes data warehouses like Snowflake and Amazon Redshift, data lakes on Azure Data Lake Storage or Amazon S3, and hybrid setups using patterns like Data Lakehouse with Medallion Architecture.
Data Pipelines and Integration
We look at how data moves between your systems. If your team is still running manual exports, we design automated pipelines using tools like dbt, Fivetran, Apache Airflow, or AWS Glue so your reports stay current without someone babysitting the process.
Business Intelligence Setup
We help you choose and implement the right BI tool for your team, whether that is Power BI, Tableau, Looker, or Qlik Sense. More importantly, we build the reports around the questions your business needs answered, not just the data that is easy to display.
Data Warehousing and Storage
We design and build the central data layer where all your information comes together. Whether you are on Google BigQuery, Azure Synapse Analytics, or Snowflake, we set it up so every team pulls from the same source of truth.
Data Quality and Governance
Bad data in means bad decisions out. We audit your data for accuracy and consistency, then put governance processes in place using frameworks like DMBOK and tools like Collibra or Atlan so the quality stays high over time.
Predictive Analytics and Modeling
When your data foundation is solid, we help you move beyond "what happened" to "what is likely to happen next." That might mean building models in Python with scikit-learn, running them on Amazon SageMaker, or using Databricks for larger-scale analysis.
How It Works
Three steps over two weeks. You get a prioritized roadmap with real numbers attached to every opportunity we find.
Week 1
Discovery and Process Analysis
Discovery Interviews
We interview your leadership team and the people on the ground to find the gap between how the business is supposed to run and how it actually runs. That gap is where the money is. We are not asking about goals or visions. We are looking for broken processes, friction, and inefficiencies.
Map the Process and Find Opportunities
We map your entire operation across Acquisition, Delivery, and Support on a single canvas. Then we score every opportunity we found against effort and impact. Quick Wins go to the top. Before we finalize anything, we validate the plan with you so you have ownership of the priorities.
Value Stream Map
Full process map
Value vs. Effort Matrix
Effort vs. impact scoring
Validation
Co-created with you
Week 2
Presentation and Next Steps
The ROI Summary
Every recommendation comes with the math to back it up. The ROI Summary shows the savings per process, the estimated implementation cost, and the projected Year 1 ROI. We include a revenue uplift section showing what happens when you redirect freed-up employee hours to higher-value work. The presentation ends with clear next steps.
The Deliverables
You get systems and documentation you can act on right away. Every deliverable includes specific next steps with effort estimates and expected outcomes.
Data Architecture Blueprint
A complete design for your data infrastructure. Warehouse schema, pipeline layout, source system connections, and data model documentation, all mapped to your business requirements with clear technology recommendations.
Automated Data Pipelines
ETL and ELT pipelines that move data from your source systems into your analytics layer on schedule, with error handling and monitoring built in. No more manual exports or broken spreadsheet links.
BI Platform Implementation
Your business intelligence tool set up, connected to your data, and configured with the dashboards and reports your team actually needs. Built for the people who use them, not the people who approved the budget.
Operational Dashboards
Real-time visibility into the metrics that drive day-to-day operations. Designed so your team can check the numbers themselves without waiting for someone to pull a report.
Executive Reporting Suite
High-level summaries built for leadership decision-making, with drill-down capability when the numbers need more context. Designed around the questions your board and investors actually ask.
Data Model Documentation
Complete documentation of your data model, transformation logic, and metric definitions. Built so the system is maintainable and auditable long after our engagement ends, even if team members change roles.
When Your Data Needs a Real Foundation
You do not need to be in crisis to get value from this work. But if any of these sound familiar, it is probably time to bring in someone who has built these systems before and can tell you exactly what is missing.
We have built data infrastructure inside Fortune 500 environments at Southern California Edison, Accenture, and Teck Cominco. We have also worked with growing mid-market companies where the data problems look the same, just at a different scale.
Your leadership team does not trust the reports, so they make decisions based on gut feeling instead
Your team spends hours every week pulling data from multiple systems just to build one report
You bought Tableau, Power BI, or another analytics tool but nobody is using it consistently
Different departments report different numbers for the same metric and nobody knows which is correct
Board members or investors ask data questions your team cannot answer with confidence
You cannot clearly tell which products, customers, or channels are actually profitable
You are paying for a data warehouse like Snowflake or Redshift but are not sure it is set up correctly
A previous analytics project did not deliver, and now your team is hesitant to invest again
23 Years. Real Clients. Real Stakes.
Your Data Should Answer Questions, Not Create Them
Book a discovery call and we will talk through what your current data setup looks like, where the gaps are, and whether working together makes sense for your situation.
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