How to Improve FP&A Performance on the Cloud

The field of Financial Planning and Analysis (FP&A) currently faces the challenge of managing a massive influx of data from numerous financial transactions, market trends, and operational metrics. The sheer volume, diversity, and complexity of this information put a strain on FP&A’s performance and analytical capabilities amid the increasing demand for data-driven decision-making.

Surprisingly, 61% of FP&A leaders attribute their biggest challenge to insufficient tools and systems, which hinder their ability to glean valuable insights from the vast amount of data available. With the increasing demand for data-driven insights, organizations are actively seeking cloud-based solutions to handle this data deluge. This move offers scalability, cost optimization, and faster query performance.

However, transitioning FP&A teams to the cloud brings its own challenges, such as accurately integrating data, overcoming performance issues, and ensuring data accuracy. These limitations arise due to the high volumes of data being processed, the need for seamless collaboration across departments, and the requirement for timely and precise insights to inform strategic decision-making. This article will explore five best practices that enhance financial planning and analysis performance on the cloud. By adopting these practices, businesses can make smarter decisions and thrive in today’s dynamic market landscape.

Deploy the Right Cloud-Based Architecture
Cloud-based solutions allow for scale-in and out analytics capabilities based on data warehouse size. These solutions are also free from the limitations of on-premise systems. Additionally, the cloud platform enables regular data updates, ensuring teams always have the most current information at their disposal.

This leads to more precise insights for strategic planning and predictive analysis. Adopting cloud-based solutions also brings considerable cost savings by reducing reliance on expensive physical servers and manual maintenance.

Enable Self-Serve Analytics

Self-serve analytics can empower all members to access independently and analyze data. This eliminates the reliance on specialized data teams and creates a more agile and self-reliant decision-making environment. By implementing a universal semantic layer, financial planning and analysis teams can consolidate all business logic in one central location. This layer acts as a bridge between analytics tools and data platforms, guaranteeing standardized financial terminology and metrics.

Consequently, queries yield consistent results across different user groups. Individuals with varying technical expertise can extract insights autonomously by simplifying the complexities of raw data. Ultimately, this cultivates data democratization throughout the organization, enhancing agility and reducing reliance on data teams.

Modernize the OLAP Approach

Traditional online analytical processing (OLAP) systems, such as TM1, Essbase, and SSAS, encounter challenges when dealing with large data volumes. This often leads to slow query performance and processing obstacles. The complex and extensive nature of the data overwhelms these systems, delaying the generation of insights. However, by modernizing OLAP approaches, financial planning and analysis teams can effectively manage and analyze massive datasets without any hindrance.

A great example of this is utilizing AI-based smart aggregation technology, which enables the creation of scalable data models by processing multiple combinations relevant to business users. Consequently, this ensures faster responses to complex queries across various dimensions and measures. In just a matter of seconds, the sea of raw data transforms into valuable insights that can be acted upon promptly.

Simplify Complex Calculations

Financial planning and analysis teams often face the need to conduct complex calculations using extensive historical data, such as performing Year-on-Year (YoY) analysis to uncover changes in profits over multiple years. This entails comparing financial data from one year to another to identify patterns and gain insights.

To simplify these calculations, teams can utilize advanced data hierarchies within datasets, which provide a structured organization of information. By organizing different measures hierarchically and aggregating them, teams can efficiently analyze data at a detailed level, saving both time and computational resources. This approach facilitates more accurate forecasting and trend analysis.

Adopt a Decentralized Data Management Approach

Traditional centralized data management often leads to bottlenecks, backlogs in analytics, and scalability issues. However, a decentralized data mesh architecture can offer an effective solution. This approach treats data as a product and assigns ownership to individual departments or teams to manage their datasets.

By doing so, it promotes collaboration and enables the sharing and combination of these data products for comprehensive analysis. This not only reduces duplication but also enhances efficiency. Importantly, the data mesh approach ensures data security and access control by allowing federated governance for each data product individually.

Conclusion

By adopting these strategies, organizations can achieve their FP&A performance goals and effectively utilize financial data. This allows for quicker and more accurate decision-making, scalability, cost optimization, and higher efficiency through cloud technology. Leveraging the capabilities of cloud platforms enables businesses to stay competitive and make informed choices in today’s dynamic business environment.