Case Studies Better Infrastructure Built on a Faster, Smarter Financial Close
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Better Infrastructure Built on a Faster, Smarter Financial Close

Analytics & Modeling - Predictive Analytics
Platform as a Service (PaaS) - Data Management Platforms
Business Operation
Software Design & Engineering Services
System Integration
The month-end close process at Capstone Infrastructure was plagued by manual data collection and reconciliation against printed spreadsheets. This manual data entry and consolidation were becoming increasingly time-consuming and error-prone. The overall process lacked version control, an audit trail, and data accuracy and integrity, making it difficult for the company to ensure reliable financial reporting. The inefficiencies in the process were causing delays and increasing the risk of errors, which could have significant financial implications for the company.
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Capstone Infrastructure is an independent power producer focused on clean, renewable energy. The company generates revenue of $138.4 million and is committed to providing sustainable energy solutions. Capstone Infrastructure operates in the energy industry and has a significant focus on financial processes, including financial close, reporting, and account reconciliation. The company was using Microsoft Great Plains (MS GP) as its existing software for managing these processes. However, the manual nature of their month-end close process was becoming a bottleneck, leading to inefficiencies and potential inaccuracies in their financial reporting.
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Capstone Infrastructure implemented the Vena solution to address the challenges in their month-end close process. The entire process was run through an OLAP database, providing a single source of truth for all financial data. This centralized data approach eliminated issues related to version control, data accuracy, and integrity. With real-time visibility into the data, teams across the business could ensure they were always looking at the correct information. The Vena solution streamlined the month-end close process, making it faster, more accurate, and efficient. By automating data collection and reconciliation, the company significantly reduced the time and effort required for these tasks.
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The month-end close process was completed 6 days earlier than before.
Data review time was cut by 72 hours per entity.
Significant cost reduction was achieved by eliminating manual processes and data reconciliation against printed spreadsheets.
6 days reduction in close time.
72 hours cut in data review time per entity.
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