Solution enables IT professionals to quickly deploy a centralized hub for financial analytics and empower Finance decision makers with timely, accurate insights
Incorta, the platform for real-time operational analytics on raw business data, announced the launch of its Analytics Data Hub for Finance to enable IT organizations supporting finance teams to deliver financial and operational analytics with unmatched flexibility and speed. The solution introduces new finance data apps, support for new data sources, and a component SDK built specifically for financial workstreams on the Incorta platform. Now available to all customers, the solution gives CFOs and finance teams a complete, end-to-end view of the latest business data in real time to drive faster, better, and more informed decisions.
“Today’s business environment creates an u***** need for real-time analysis of financial and operational data,” said Steve Walden, Incorta EVP of Business Development and Strategy. “The Incorta Analytics Data Hub for Finance simplifies and speeds up data sharing between sources and destinations, driving huge gains in productivity and efficiency for finance teams, and shifting the focus from backward-looking reports and dashboards to real-time analysis and insight discovery.”
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Companies today are operating in a business environment where urgency and uncertainty are both at all-time highs, raising the stakes for strategic financial decisions and accelerating reporting timelines in the office of finance. According to Gartner, 75% of finance functions are now providing reports to the C-Suite on at least a weekly basis, with over 40% doing so multiple times a week. At the same time, today’s finance teams routinely struggle to access, analyze, and generate meaningful insights from financial and operational data. A recent Gartner survey found that 72% of CFOs will focus on improving the flexibility of budgeting and forecasting in 2022.
The newly launched Analytics Data Hub for Finance brings unprecedented speed and flexibility to financial and operational analytics. It provides an end-to-end self-service platform for analysis of accounts payable, receivable, fixed assets and general ledger data, and provides visibility down to transaction-level details while applying common models and governance controls to data from different sources and destinations. New financial data apps accelerate operational analytics for Oracle ERP Cloud, SAP, Oracle EBS, and Netsuite. New integrations enable data delivery to core forecast, plan, and consolidation process finance systems, starting with direct API feeds to Blackline with planned solution expansion across a breadth of technologies in the space.
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With the Analytics Data Hub for Finance, Incorta customers can now collaborate and share critical business data in a unified environment that:
- Provides consistency for data models and governance across the enterprise by connecting data from multiple ERP solutions, business applications and financial and operational systems in a unified environment.
- Accelerates data feeds to, and analysis for, planning, forecasting, reconciliation, and close cycles with visibility and analytics directly on subledger details and external operational data.
- Delivers built-in Finance data apps to quickly and easily deploy new financial and operational analytics in days.
- Extracts more value while simultaneously future-proofing your financial technology investments by easily integrating with popular finance tools and reducing long-term operating costs.
Incorta is a unified data and analytics platform — spanning data acquisition, data management, and data visualization — which is purpose-built to accelerate time to insight from complex subledger and operational data sets. It directly maps to the source application data, eliminating the traditional transformation and aggregation steps to deliver 100% of operational data to the business. As a result, Incorta users can slash the time needed to prepare data, analyze all their usable data down to transaction-level detail, and enjoy unmatched performance even when analyzing billions of rows of data in near real time.
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