Data Without Drama:
A Manufactured Overhaul
Background: "We were drowning in reports—hundreds of them outdated, redundant, and inconsistent across departments. It was chaos."
A national PE-backed snack manufacturer faced significant challenges with data management and reporting. Their system consisted of 100s of reports, the majority of which were outdated and still required maintenance. Reporting was highly manual, involving redundant processing steps and dependencies on key individuals. With 10 departments and 5 disparate data sources, the company struggled with inconsistent definitions and an overwhelming backlog of reporting requests. These inefficiencies hampered decision-making at both the Fund and the Company.
Challenge: "Every department had its own version of the truth. No one knew which numbers to trust."
The company faced a large set of challenges, including:
Lack of a Centralized Data Source
Key business systems were disconnected, requiring teams to extract reports manually, leading to inconsistencies.
Data Fragmentation
Different functional groups relied on separate data sources, leading to discrepancies.
Manual Reporting & Human Error
Critical reports were manually compiled, increasing the risk of errors and consuming valuable employee time.
Backlog & Bottlenecks
Only 2 people were responsible for generating reports, creating a significant bottleneck.
Lack of Scalability
Reports were often specific to individuals or teams, making it difficult to scale insights across the organization.
Solutions: "They didn't just build a data warehouse, they built a foundation for us to grow on."
Stratus implemented a modern data warehouse solution with the following key components:
Training & Knowledge Transfer
Ensured post-engagement continuity by conducting extensive training with the Client.
Automated Reporting with Power BI
Eliminated manual reporting by providing real-time dashboards accessible across departments.
Centralized Data Warehouse
Developed a single source of truth by integrating disparate data sources within a Snowflake database.
Impact: "I used to wait days for reports, and now I get answers in seconds. Every morning, I log in and have everything I need. No emails, no delays—just data, exactly how I want it."
Hours Saved per Year
The fund and company now save ~1,000 hours across its IT team, executives, and fund sponsors and making and fulfilling requests.
Reduction in Wait Time
The fund used to send up to 5-10 requests per week, each taking days or even weeks to fulfill. Now the fund sponsors log into Power BI and get answers in seconds.
Key Man Risk
Whereas reports used to depend on key members of IT, all data requests are now self serve.
Cultural Shift
Power BI adoption spread beyond the company, influencing data strategy discussions at the Fund and other portfolio companies. Employees who traditionally relied on manual reporting started proactively requesting Power BI implementations for their workflows.
Sustained Data Governance
Post-project, the company’s IT team continued developing the warehouse while adhering to best practices, demonstrating the project's long-term sustainability.
Takeaways
Why Stratus: "Stratus just got it... The questions they asked were so detailed, it was clear they understood our pain points at a level no other vendor did."
The Fund conducted a thorough vendor selection process, engaging 3–4 firms, and ultimately choosing Stratus for several key reasons:
Depth of Inquiry & Understanding
The Fund and Company were able to understand their operational processes and technical infrastructure with greater depth due to Stratus's inquisitive approach towards the problem.
Technical & Business Expertise
Both technical and non-technical stakeholders were able to get on the same page, because the Stratus team bridged the gap with their knowledge in both business and technology.
Chemistry with the Team
The in-house IT team felt comfortable with Stratus due to their collaborative and listening-oriented approach.
Commitment to Partnership
The team felt like their best interests were being served, because of Stratus's emphasis on long-term partnership rather than a transactional engagement.
About Stratus Data
Stratus creates unreasonable competitive advantage for mid-market companies through analytics and AI solutions that drive measurable ROI.
Projects
Clients
Data Science Staff
Years in Operation
Philosophy
Listen Carefully Listen Carefully. Organizations don't come out of a box, neither should their solutions. Listen for what each person actually needs. Partner Longterm Think about the client's longterm goals. Place what you build into their longterm vision. Drive ROI Focus on solutions that drive measurable value by targeting key actions and decisions. Focus on Quick Wins Build momentum by creating solutions that prove value quickly, then go big. Start Simple Start simple then add complexity. 80% of value can be harvested with 20% of the effort.
The Team
Charles Pensig, Co-Founder Analytics and Business Intelligence Data Scientist and business translator generating results such as immediate 5% revenue lift and 2% gross profit lift through deep dive analyses finding key levers to big outcomes. Built data science functions at early- and mid-stage Silicon Valley IoT, Wearables, and SaaS companies, with 2 acquisitions; and the Wharton School's Center for Analytics & AI. Derek Chang, PhD, Co-Founder Machine Learning and Optimization Data Scientist and engineer developing algorithms and data automation systems saving clients 1,000 hours per year, reducing SG&A by 15-40%, and reducing CoGS by 5-7%. PhD Stanford EE, BS Caltech EE, former nuclear fusion researcher Speaker at Caltech, Stanford, HBS, and Wharton. Applied Experience from Silicon Valley and Beyond Tech: Google, Amazon, Meta, IoT, Wearables Consulting: McKinsey & Co. Finance: Goldman Sachs, Maveron, Fintech Government: NASA, Livermore National Labs Research Degrees from Harvard, Stanford, Caltech, MIT Machine Learning Artificial Intelligence Statistics Physics Electrical Engineering Computational Biology Applied Degrees from Wharton, Caltech, Stanford, Georgetown Statistics Finance Financial Engineering Operations Research
Services that Drive Growth, Margin, and Efficiency
Analytics Strategy In the world of big data and AI, there are many ways to take on the future. Our team helps you find the right path into longterm success. AI-Assessment and AI-Powered Automation AI is estimated to grow in use by 10x between no and 2030. How should you be using it? Using out of box tools or custom built ones, with training for your team. AI Prediction and Pattern Detection Machine learning models can see the future and find hidden patterns that go well beyond Excel and the human eye. What would you do if you had a crystal ball? Deep Dive Quantitative Analysis Modern data science methods and tooling enable root cause analysis in a way and at scale, that traditional tools like Excel don't. Automated Reporting Tableau and Power BI dashboards enabling realtime monitoring of key aspects of your business
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