The Client: Genomenon
Genomenon has positioned itself as a leading B2B software-as-a-service (SaaS) provider, aiming to revolutionize the field of genomics through their comprehensive and cutting-edge solutions. With an unwavering commitment to advancing genomic research and healthcare, the company is dedicated to equipping medical professionals, researchers, and institutions with the tools necessary to unlock the vast potential of genomic information.
Genomenon’s primary objective is to bridge the gap between research and patient care, harnessing the power of genomic data to facilitate the diagnosis and treatment of individuals affected by rare genetic diseases and cancer. Through their state-of-the-art technology, Genomenon aims to consolidate and organize genomic data from diverse sources, enabling their clients to gain valuable insights, identify patterns, and make informed decisions.
The Challenge: Error-prone Models and Cumbersome Manual Processes
Before implementing Jirav, Genomenon relied heavily on Excel for their forecasting and modeling processes. One major issue this caused was the lack of version integrity, as the team struggled to keep track of the modifications and changes made to the models over time. This resulted in difficulties in maintaining the history of the data and often led to errors in the spreadsheet that affected the accuracy of their models.
The team also faced systemic modeling errors that would occasionally arise and disrupt the overall functionality of the model. These errors were difficult to identify and resolve, further hampering the effectiveness of their financial forecasting. As a small team and startup, Genomenon encountered additional challenges in establishing control measures and implementing comprehensive review processes for complex financial modeling.
The data inputs for their Excel models primarily consisted of hiring data and pipeline data from their sales team, and as such, they relied on a Professional Employer Organization (PEO) for their HR information system to provide them with labor and payroll data. However, integrating this data into their Excel models was a cumbersome process, involving manual data dumps into CSV files and extracting relevant information through Pivot tables.
These obstacles hindered their ability to effectively forecast and model their financial performance, necessitating the search for a more streamlined and efficient solution.
The Solution: Jirav
Genomenon recognized the need for efficient software to enhance their forecasting capabilities and improve financial reporting across departments. After testing multiple products, the finance team decided to evaluate two options: Jirav and another firm's software. Their critical requirements and metrics included direct integration with their systems, particularly their financial systems.
During the four-week trial period, the finance team gained confidence in Jirav’s flexibility and ability to handle complex forecasting and modeling. The team found the driver-based forecasting system particularly user-friendly, which played a significant role in their decision to choose Jirav as their preferred solution.
Simultaneously, the finance team replicated their three-year forecast from an Excel spreadsheet into Jirav. Over the following two to four weeks, they continued to iterate and fine-tune their models and driver capabilities within Jirav. This parallel approach ensured a smooth transition while maintaining core forecasting capabilities. Within approximately six weeks of switching to Jirav, the company had achieved their basic forecasting needs and had full reporting capabilities.
The Results: Swift Implementation and Streamlined Productivity
The implementation of Jirav had a significant impact on Genomenon, particularly within the finance team. The ease of use, transparency, and reporting functionality greatly improved the efficiency of conversations and reduced strain within the team.
One of the notable benefits of utilizing Jirav was the ability to easily integrate information while maintaining data integrity through its robust reporting features. This feature streamlined budget discussions, forecasting, and reporting processes for the finance team. As a result, the software significantly reduced tension that would otherwise arise from data disorientation.
The finance team experienced the most substantial impact, as Jirav streamlined budget discussions, forecasting, and reporting in their Management Discussion and Analysis (MDNA). The ease of access to accurate and organized data facilitated more productive discussions and decision-making.
Jirav’s reporting capabilities and data integrity features enhance interdepartmental collaboration and improve overall operational efficiency. In terms of implementation, given the fast-paced nature of Genomenon’s industry, time was of the essence. Jirav’s swift integration and the process of flipping the switch to see actuals and experience the software's full functionality was seamless. The implementation took a maximum of a few minutes, with no significant time-related issues or delays encountered.
