Financial Analysis Tools for Data-Driven Insights
Today’s finance teams are using data analysis to deliver more value to their organizations than ever before. The availability of relevant data, combined with modern technologies capable of processing this data in massive quantities, has given finance teams the opportunity to apply data-driven insights not only to big-picture business decisions but also to the day-to-day questions that add up to shape the success of an organization. Let’s explore how today’s companies are using financial analysis tools to reduce risk and take advantage of opportunities.
Benefits of Using Financial Analysis Tools
Modern tools to analyze big data offer finance departments the ability to access the massive amounts of data available to today’s organizations. Mining this data unlocks otherwise-hidden insights that can be used to improve business functions across the company. Here are five specific ways finance departments are using data analysis tools.
Better-informed R&D investment decisions
Research and development initiatives require a significant investment of capital that often requires years to reach break-even. When drawing from a centralized platform that consolidates all relevant data, financial analysis tools are capable of identifying when a significant R&D expenditure will pay off as well as calculating probable rates of return and outcomes based on the investment. Data analysis can also help teams determine whether acquiring the desired asset through acquisition is more cost effective than developing a similar asset in-house.
Improved revenue and cost forecasting
Forecasting future revenue and budgeting based on those numbers is one of the core functions of finance departments. It’s now possible to track revenue in near real time, allowing for more frequent adjustments based on how revenue is tracking. Additionally, modern data platforms have the robust compute power needed to process massive amounts of relevant data, so forecasts are more accurate.
More robust risk assessment and fraud detection capabilities
Finance departments are tasked with processing payments to vendors and overseeing the expenditures of an organization. Billing mistakes and fraud, including duplicate or inaccurate invoices and prices or quantities that exceed contract parameters, represent a risk to the cash flow of an organization. Tools to analyze data can spot inconsistencies and track unusual patterns that indicate fraud, flagging them for follow-up.
Accurately project future growth and expansion costs
Historical data from various internal and external sources can be used to model future growth scenarios, allowing decision-makers to more accurately understand how micro and macro trends may influence growth. Large capital investments can be modeled based on key metrics such as sales, where current customers are located, trends influencing product consumption, and location-specific tax advantages to provide data-enhanced insight into when and where new investments should be made.
Challenges to Data-Driven Financial Analysis
Introducing modern financial analysis tools into practice can come with challenges, specifically for organizations with on-premises infrastructure. Here are some of the barriers that finance departments face in integrating new data analysis tools into daily practice and how to overcome them.
To benefit from data analytics, you’ll need to include all data relevant to the analysis. This data typically comes from a wide variety of sources, including applications, systems, and databases. With data stored in multiple independent locations, it’s nearly impossible to bring all relevant data to bear on the business question at hand. Modern cloud-native data platforms provide organizations with a secure single source of truth.
Varying data formats
Due to the variety of sources data comes from, organizations must deal with various data formats. Modern cloud data platforms are designed to accommodate all data formats, processing the data appropriately for analysis. As you’re implementing an analytics program, you’ll want to be sure that your data platform is capable of easily handling a wide range of data sources and types without requiring significant IT support that will create bottlenecks that slow time to insight.
Data security and governance risks
It goes without saying that most data involved in financial analysis is sensitive and must be protected from both external cyberattacks and unauthorized access from internal sources. Data governance provides a framework for managing data, ensuring quality and minimizing risk. Look for cloud data solutions that have robust security features and practices, offer advanced user access controls, and support data governance.
Lack of scalability
As the volume of data that organizations use continues to grow, legacy data solutions struggle to keep pace. Limited storage and compute resources prevent many organizations from fully benefiting from the data available to them. Modern cloud data platforms solve this problem by being precisely scalable. They offer near-unlimited data storage and are capable of providing task-optimized, dedicated compute power for each query.
Getting Started with Financial Analysis Tools
Adopting a modern, data-driven approach isn’t an initiative that happens overnight. It’s a process that requires careful planning and execution. Here’s how to get started.
Map out a plan
Creating a comprehensive data strategy is the starting point for any new data analytics initiative. Begin by defining the main objectives your approach is expected to accomplish as well as the personnel resources required to accomplish them. Identify the relevant data sets, where they’re currently stored, the format(s) the data is currently in, and whether or not additional data from third-party sources is needed to supplement what’s available in-house. If possible, bring in a dedicated data professional who can be integrated into the daily workings of the finance team.
Find the right data platform
The large volumes of data and dedicated compute power needed to run financial analyses efficiently require a modern data platform that’s easily scalable, with elastic data storage capacities and powerful compute resources. Additionally, as mentioned above, your platform should have robust security and governance features.
Begin with a small, easily definable objective and scale up as the team becomes more familiar with the data analysis process. Gradually integrating data analysis into the fabric of an organization takes time.
Involve teams from other areas of the business
The most effective data analysis initiatives involve stakeholders from other departments—capitalizing on their expertise and experience to solve business problems and improve the accuracy of insights with perspectives from outside of finance. For this reason, your data platform should support collaboration.
Snowflake Is the Ideal Engine for Financial Analysis
The Snowflake Financial Services Data Cloud is an ideal platform for financial analytics initiatives. With built-in performance optimizations, data management, and administration as a service, Snowflake makes it possible to elastically add resources on demand and pay only for what you use. Near-unlimited storage allows all data to be stored in a single, secure location, making it easily accessible to authorized users. Snowflake’s built-in tools enhance speed and resource performance, reducing time to insight and making it possible for teams to act quickly. Industry-leading security and data governance practices ensure that data stays protected.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.