Integrate modern analytics tooling with insurance policy systems.
Cut administrative costs and save over $300K/year.
“When we engaged Gazer, I knew we could streamline our data analytics better. I had no idea things could be this good. Our whole company now works together, and smarter, in a way I never thought possible, all while decreasing our spend. It's incredible.”
–Sam Melamed, CEO of NCD
Trusted by:
Handle the evolving needs of a data-heavy, highly regulated insurance industry.
Boost operational efficiency by 80% at every touchpoint.
Connect Your Company
Unify all claims, demographic, agent performance, billing, member retention, provider utilization, clinical, SFTP, and unstructured data in a centralized data warehouse.
Real-Time Monitoring
Automate real-time monitoring alerts and dashboards to send CSV, PDFs, or external reports generated via email. Alert team members to problems and wins without any manually data pulling.
Embedded Data Privacy & Compliance
Build HIPAA, SOC-2, and HI-Trust level security and compliance, protecting and encrypting you and your customers’ PHI data.
Data Pipelines Built To Last
Built with end-to-end data testing and best-in-class, iterative data pipeline and transformation tools like SQL-based dbt, Dagster, and AWS Lambda/Fargate, and more to enable teams to build faster and smarter.
Accelerate Claims Throughput
Iterate on company-wide data models and metrics. Go from request to dashboard deployment in minutes, saving thousands across data resources and boosting team productivity.
Unlock Personalized Member Data
Allow stakeholders to ingest, analyze, and self-serve data from any vertical in real time, increasing MRR and saving dozens of collective hours per week.
Technical FAQs:
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A data warehouse is a centralized storage location (e.g. Google BigQuery, Amazon Redshift, or Snowflake) for all your claims, demographic, agent performance, billing, member retention, provider utilization, clinical, unstructured data and beyond. It provides a unified place to clean and transform your data to map various sources to unlock cross-vertical analysis e.g. mapping customer data to transaction data.
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Data transformation is the process of converting data from its raw format into analyzable metrics and KPIs e.g. Quickbooks data that’s converted into financial KPI visualizations. A commonly used transformation tool is dbt.
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A data pipeline, or ELT (Extract, Load, Transform), extracts all your data from various sources, then loads it into a centralized data warehouse. It’s set to load data on a schedule, as quickly as every 15 minutes. Transformation is typically done within the data warehouse after the data is loaded.
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Dashboards consolidate and visualize critical business metrics and KPIs from various data sources in a single view. A real-time understanding of your business unlocks trends, patterns, and areas that require attention. Good dashboards are simple, clean, intuitive, well-labeled and easily filterable.
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After initial scoping, a data pipeline is built, a central warehouse is setup, and all data sources are ingested. A metrics layer is built within a transformation tool e.g. dbt. Once final tables are produced, we connect BI/dashboarding to the final dbt tables. Testing is done after each step to ensure data quality.
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We sift through your existing primary data sources, assess pipeline/ETL integration compatibility, understand your KPIs, then put together and share with you a document detailing all steps how to setup a data stack specific to your needs.
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You’ll pay the monthly ongoing costs of loading, hosting, transforming, and visualizing your data. All accounts will be under your ownership. We’ll project costs for you based on your data volumes.
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Initial setup is for any company with minimal to no previous data architecture. A migration is for company that already has architecture or a warehouse, but plans to migrate to more efficient, modern, or cheaper data tools.
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After initial implementation or migration, you can subscribe to on-demand analytics maintenance for when new dashboards/analyses need to be built, new data sources need to be added to the warehouse, or data tables and metrics need to be redefined.
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Typical analytics tooling includes Airbyte/AWS Lambda (ELT/custom pipeline), Google BigQuery/AWS Redshift/Snowflake (central warehouse), dbt (transformation), and Metabase/Hashboard/Looker/Omni (BI/dashboarding).
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First-time implementation can range from 3 weeks to a few months for full-scale migration projects.
Atticus Grinder, CEO/Founder of Gazer
Atticus has 8 years prior experience helping companies setup data analytics and deploy pipelines at a variety of both enterprise and startup level companies. We’ve helped top-tier exited fintechs (General Catalyst, Spark Capital, Canaan Partners-backed) through the largest financial institutions in the US (American Express, Capital One) build next generation data products.
atticus@gazerlabs.com | gazerlabs.com