Understanding Financial Analytics
The use of tools and procedures to aggregate and analyze information to learn more about your organization's financial performance is known as financial analytics. Bringing data from all diverse systems together provides you with a comprehensive view of your company and deeper insights that enable you to forecast and enhance performance.
Why is Financial Analytics Important?
Modern financial analytics can transform your business in numerous ways - at all organizational levels. The finance department can focus more on deeper discovery and performance analysis. Additionally, new tools & techniques enable you to extract accurate and actionable insights to manage risks efficiently, enhance profitability, reduce operating costs, and forecast future plans.
This assists in the transformation of the CFO's function from scorekeeper to being an important catalyst for improving overall organizational performance. Further, it helps in identifying and evaluating the value drivers within your organization, i.e., the factors that increase your business credibility.
What Challenges Businesses Face While Implementing Financial Analytics
Listed below are the two key challenges that organizations experience when they implement modern data financial analytics in their businesses:
Passive data: Businesses are evolving more rapidly than ever. But traditional data and methods are too passive to be relied upon to extract real-time information about market trends, customers, and operations. It has become crucial for businesses to understand what’s happening right now to take immediate action. Moreover, it has become a must to accurately forecast future outcomes that compel timely action.
The good news is that advancement in both the delivery and consumption of data now makes it possible to establish finance analytics pipelines that deliver real-time, high-quality data to take immediate action.
Massive investments & complex models: Optimizing predictive analytics and prescriptive analytics in the past required that you find and employ data scientists to create unique machine learning algorithms. Additionally, if you had to integrate, store, and manage your data, it required significant money to be spent on hardware and data experts.
With the advent of modern AutoML (Automated Machine Learning), one can easily build, train, and deploy personalized ML models. Moreover, there is no hassle in securing data storage, computing power, and required speed with a cloud data warehouse.