About the Client
The client is a professional pest control company operating across the US. They follow a unique business model centered around acquiring and empowering local pest control companies. Their mission extends beyond pest management; they aim to foster a people-first culture, ethical practices, operational excellence, and reimagining how a modern pest control business can scale.
Business Challenges
The client’s existing data model was complex, disorganized, and lacked a coherent structure, leading to operational inefficiencies and challenges in maintaining accurate data. The client also highlighted multiple challenges, including:
Fragmented Data Sources
The client faced the major challenge of scattered business data across multiple platforms like HubSpot, Paylocity, D365, Invoca, Twilio, SQL Server, WorkWave, and more. The lack of a centralized data model resulted in inconsistent KPIs, duplicated logic, and siloed insights. This made it difficult for the client to fetch information from a single source of truth.
Lower Scalability and Performance
The client used legacy SQL Server infrastructure, which could not support growing data volumes and advanced analytics needs. This resulted in performance issues and slow query response times.
Inconsistent and Complex Reporting
The client generated reports from fragmented source systems. This approach was time-consuming, error-prone, and difficult to scale. It also led to business units often working with conflicting metrics, leading to misaligned strategies and decision-making delays.
Redundant Technology Costs
Multiple overlapping SaaS applications were used with no consolidation strategy. This redundancy inflated operational costs, diverted resources from innovation, and added complexity to IT governance.
Operational Dependencies
Critical processes like access provisioning, documentation, and UAT (User Acceptance Testing) relied heavily on internal client stakeholders. These dependencies resulted in delayed project timelines and increased execution risks.
Kasmo’s Solutions
Our experts understood these challenges and leveraged Snowflake and Data Load Accelerator to solve their data migration challenges.
Kasmo’s Data Load Accelerator
We implemented Kasmo’s Data Load Accelerator (DLA) Framework to ingest, transform, and reconcile data from SQL Server and third-party SaaS applications into Snowflake. This eliminated manual effort, improved efficiency, and ensured reliable, standardized, and secure data migration.
Unified Semantic Data Model
Our team developed and implemented a centralized semantic data model on Snowflake to standardize KPIs, hierarchies, and business logic across all sources. This ensured consistency in reporting and eliminated duplicate or conflicting metrics that previously existed across departments.
Secure Data Sharing
We enabled WorkWave data sharing within Snowflake and built a roadmap to retire redundant SaaS platforms post-migration. This helped the client lower operational costs and errors.
BI Modernization
Early, the client pointed SQL server data to Power BI, but after post-migration, we re-pointed existing dashboards in Power BI to Snowflake’s semantic model. This approach helps to achieve faster performance, real-time reporting, and consistent metrics across business units.
Data Quality Assurance
To guarantee data accuracy and build trust in the new system, we automated both row-level and summary-level validation checks across all data sources. These validations were completed prior to UAT and go-live, ensuring smooth adoption without data quality issues.
Future-Ready Data Platform
Finally, we implemented Snowflake features to improve scalability for advanced use cases, including AI/ML workloads, predictive analytics, and integrations with new SaaS applications.
Results Achieved
Our experts successfully implemented Data Load Accelerator and executed a seamless data migration, through which the client achieved:
Unified Data Platform
- We integrated 15+ disparate data sources into a single semantic model built on Snowflake.
- The client eliminated 100% of data silos and legacy inconsistencies.
Higher Scalability and Performance
- With Kasmo’s Data Load Accelerator, our client improved Ingestion speed by 2–3x.
- Query performance increased 3–5x, enabling high-volume data analysis with minimal latency.
- Data refresh time was reduced from several hours to just minutes, supporting near real-time insights.
Analytics and Reporting Speed
- After connecting Power BI dashboards to Snowflake, the client can load data 40–60% faster.
- By reducing data prep time by 30%, analysts gained more bandwidth to focus on delivering timely, high-value executive insights.
Improved Data Accuracy
- Validations after data migration to Snowflake resulted in nearly 99.9% data accuracy, which increased trust and provided accurate insights.
- Automated validation and reconciliation reduced manual reconciliation efforts by 70%.
Operational Efficiency and Cost Savings
- Kasmo’s Data Load Accelerator helped to automate pipelines and reduce ETL and maintenance efforts by 50–60%.
- Client saved over 20+ hours per week previously spent troubleshooting SQL scripts and reporting issues.
- Sunsetting redundant SaaS tools is projected to cut licensing and infrastructure costs by 15–20% annually.
Future-Readiness
For our client, Snowflake now serves as a central data hub—enabling AI/ML, advanced analytics, and faster onboarding of new SaaS sources in days instead of weeks.
