We measure our success by the real-world impact we deliver. Here are a couple of examples of how Clearcut has helped clients transform their analytics:
Client: A mid-sized financial services company specializing in whole loan and RMBS (Residential Mortgage-Backed Securities) investments. A division within a larger bank, this team managed billions in loan assets but relied on outdated data marts and Excel-based reporting. Their legacy system struggled with scalability – data loads were slow, and complex risk analyses had to be run overnight. The organization’s small IT team (just a few developers) found it difficult to keep up with business demands for new analytics, and vendor-locked BI tools made integration with newer cloud services nearly impossible.
Clearcut undertook a full-stack modernization. We implemented a hybrid data architecture where on-premises SQL servers (holding sensitive loan records) were connected to a cloud data warehouse for scalable processing. Using our proprietary scaffolding tools, we quickly spun up an Azure Databricks environment and a Snowflake data warehouse (for staging and analytics) – providing the client with virtually unlimited storage and compute on demand. We re-engineered their ETL, moving to an ELT model: raw loan data is now replicated to the cloud warehouse in near real-time, then transformed using Spark jobs and SQL modeling to calculate risk metrics, aggregations, and valuation models. All pipelines are orchestrated with data quality checkpoints. We also built a custom low-code interface for the client’s analysts to adjust certain parameters (e.g. scenario assumptions for loan default rates) without coding. For visualization, we replaced their static reports with interactive dashboards in Tableau, showing portfolio metrics, trends, and drill-downs by loan type or region. Crucially, our design ensured vendor independence – all core transformation logic is in open-source Spark and portable SQL, and data is stored in an open format, giving the client flexibility to adapt in the future.
The impact was dramatic. Reporting that previously took hours of manual effort is now automated and runs on a consistent schedule. The new pipelines are far more reliable and efficient – the firm experienced a 3× faster pipeline recovery and processing speed, thanks to resilient architecture and checkpointing, and saw a 2× increase in the volume of data (and number of scenarios) they could generate and analyze within the same time window. In practice, this meant risk analysts could iterate on models multiple times a day instead of waiting overnight. Data quality improved with automated validation, boosting trust in the numbers. Business users praised the new dashboards, which allowed them to slice loan portfolios by various dimensions and get instant insights (query performance improved significantly, even on large data sets). Importantly, by leveraging a modern stack, the small data team can now manage the system with minimal firefighting – no more brittle SSIS packages or Excel macros to fix constantly. They have also saved on costs: moving away from an expensive legacy data warehouse appliance and heavy manual processes has reduced maintenance expenses. This modernization positioned the client to be more agile in a fast-moving financial market, armed with up-to-date, accurate analytics to inform investment decisions.
Client: An e-commerce retail brand’s marketing department (approximately 50 employees) that relies on data to drive customer acquisition and retention. The company had data coming from multiple sources – web analytics, advertising platforms (Google Ads, Facebook), CRM, email marketing, and sales transactions. However, these were siloed in different tools. The marketing analysts spent a lot of time exporting data into spreadsheets and manually combining them to evaluate campaign performance and customer lifetime value. Their existing data pipeline was a patchwork of scripts that frequently broke (especially when APIs changed), and their on-premise database struggled with the growing data volume. There was no single source of truth, and reports from different team members often conflicted, undermining confidence in the data. With only one data engineer supporting them, the team needed a more automated and robust solution – essentially, a modern data stack that just works so they could focus on insights rather than plumbing.
Clearcut implemented a modern marketing analytics platform from scratch. We used a cloud-based approach: setting up a centralized data warehouse (BigQuery) to store all marketing and customer data. For data ingestion, we deployed a set of fully-managed connectors (leveraging Fivetran and some custom pipelines) to automatically pull data from the client’s sources (Google Analytics, Facebook/Google Ads, Shopify sales, email platform, etc.) on a continuous basis. This immediately eliminated their manual data fetching and greatly reduced pipeline maintenance issues. We designed a robust transformation layer using dbt (data build tool) to merge and clean this data – creating unified tables such as a 360° customer view and campaign performance metrics. Our low-code platform allowed the marketing ops lead to adjust business logic (for example, defining how to attribute sales across channels) via a UI, with the transformations updating accordingly in the backend. On top of this, we built interactive dashboards in Looker, providing real-time views of key marketing KPIs (ROAS, CAC, retention rates, cohort analyses) with the ability to drill down by campaign, channel, or customer segment. The entire stack was set up with monitoring and alerting – if any data load fails or a transformation produces an anomaly, the data engineer and our team are notified immediately.
The marketing team quickly felt the benefits. Their data pipelines became highly reliable – in fact, after the changes, pipeline failures virtually disappeared. One case study showed a 95% reduction in data pipeline issues after moving to the modern stack. This reliability restored trust in the data; marketers no longer felt the need to double-check numbers in disparate systems. Query performance and overall responsiveness of analytics improved drastically – dashboard queries that used to take minutes (or sometimes time out) now run in seconds, representing about a 75% reduction in query times. This meant analysts and even executives could get answers on the fly during meetings, rather than waiting for overnight reports. Moreover, the improved data infrastructure boosted the productivity of the data/analytics team – one measurement was a 3× increase in data team velocity, as they spent far less time fixing pipelines or wrestling with data prep and more time on actual analysis and new initiatives. For the business, these technical gains translated into tangible outcomes: the company saw an improvement in customer retention and lifetime value, aided by the richer insights and timely analytics now available. For example, they could identify and quickly act on trends in customer behavior (like detecting when a cohort of customers was likely to churn and deploying targeted retention campaigns), something that was previously not feasible with slow, unreliable data. In summary, by bringing in Clearcut’s modern data stack solution, the marketing division transformed into a truly data-driven operation – making decisions backed by real-time data and achieving better campaign ROI with the insights uncovered.
