Before Querio: The Initial Hurdles

Mercury is a company focused on serving its customer, spending ample amount of time ensuring great satisfaction. Internal Operations grew more complex as the company has scaled, but the company was started before many of the data tools like Querio existed. This meant that their data was messy, and reporting on anything took weeks of multi—stakeholder effort to pull the correct data then analyze it.

The raw state of the data and its complex data structure, built for software, made it virtually inaccessible for meaningful analysis. Hundreds of tables, organized around the objects required by the software they are running rather than the questions that they are trying to answer to efficiently run their business. The primary challenge in this case was to refine this raw data into a structured, analyzable format without losing the nuances necessary. Then we could begin to transform analytics.

Transforming Mercury's Data Reporting

Phase 1: Pinpointing Business Nuance

Querio's first step in transforming Mercury Data's analytics capabilities was a deep dive into the company's operational lexicon and strategic questions. Through intensive discussions with Mercury Data's business leaders, Querio meticulously mapped out the landscape of operations, the specific terminologies used, and the key entities around which the business revolved. The heart of these conversations revolved around identifying the main questions that Mercury Data needed answers to for driving their business forward.

Querio and Mercury Data collaboratively defined the essential business queries, such as customer behavior patterns, sales effectiveness, and operational efficiencies. By understanding what Mercury Data referred to in their daily operations—be its 'customer journeys', 'sales cycles', or 'engagement rates'—Querio was able to tailor the analytics framework to align with Mercury Data's internal narrative, ensuring that the insights generated would be both relevant and immediately actionable.

Phase 2: Understanding the Existing Infrastructure

Working alongside Mercury's engineering team, Querio delved into the existing data storage solution to understand the current landscape and possibilities for leveraging this data for the business objective. This comprehensive analysis laid the groundwork for the transformative steps that would follow.

Phase 3: Architecting a New Framework

Recognizing the need for a robust data transformation process, Querio designed a comprehensive plan to redefine Mercury Data's approach to analytics. he aim was to create a structure that would not only streamline the data transformation process but also empower Mercury Data's business team with a dynamic, intuitive analytics platform. By envisioning a new analytics framework, Querio set the stage for a data ecosystem that could adapt and grow with Mercury Data's evolving business landscape. This forward—thinking approach was instrumental in ensuring that the subsequent phases of ETL development and BigQuery integration would be built on a solid, strategically planned foundation.

Phase 4: ETL Development and BigQuery Integration

After laying the groundwork for understanding Mercury Data's business needs and structuring a new framework for analytics, Querio embarked on the critical task of ensuring fresh data was consistently available in the proper analytical structure. Implementing an advanced Extract, Transform, Load (ETL) solution, Querio adeptly translated Mercury Data's raw, complex data into a meticulously structured format optimized for analytical querying. This transformation was pivotal in maintaining data integrity and relevance. To host this transformed data, a Google BigQuery warehouse instance was launched, establishing a dynamic environment tailored for advanced analytics. This strategic development guaranteed that Mercury Data could rely on up—to—date, accurately structured data for making informed decisions, thereby enhancing operational efficiencies and business intelligence capabilities.

Phase 5: Empowering Through Context — Semantic Layer and Data Catalog

The essence of Querio's transformative work with Mercury Data lies in the strategic deployment of a custom—built semantic layer and data catalog, integral components that serve as the connective tissue between Mercury Data's business operations and their newly set analytical infrastructure.

This semantic layer and data catalog are not mere technical features; they are the embodiment of Querio's innovative approach to making complex data structures comprehensible and accessible to business users. By mapping the business terminology and concepts directly to the data stored in the Google BigQuery warehouse, these tools allow non—technical personnel to navigate vast datasets leveraging the Querio webapp without needing to understand the intricacies of data schemas or query languages.

This innovation fundamentally changes how Mercury Data interacts with their data. Business users can now query and extract insights using the familiar terms and concepts of their daily operations, bridging the gap between the technical data structure and the operational needs of the business. The semantic layer translates these business terms into the data terms needed to queries, while the data catalog provides the baseline for accuracy. This seamless integration empowers business teams to make informed decisions quickly and efficiently, leveraging data insights without the bottleneck of technical mediation.

Achieving Unprecedented Speed and Accuracy

The implementation of Querio's solutions marked a new era for Mercury Data. The business team now accesses data directly through the Querio web application, without reliance on analysts, DB administrators, or engineers. Crafting dashboards for sales and customer usage with unprecedented speed and accuracy.

The results are staggering:

• 20 times faster than before. What used to take weeks, now takes minutes.
• The ability for decision—makers to build dashboards and answer questions themselves.
• A 95% accuracy rate in data—driven insights.
• Reducing the headcount necessary to empower decision—makers with data.

Reshaping Data Utilization

Querio's engagement with Mercury Data stands as a testament to the transformative power of expert data management paired with the most cutting—edge technology. By turning complex, raw data into a streamlined, accessible resource, Querio not only answered Mercury Data's immediate questions but also equipped them with the tools to continue uncovering insights swiftly and accurately. This case study not only highlights Querio's proficiency in data analytics but also showcases their role in enabling businesses to navigate the complexities of data with unparalleled efficiency and precision.