Many organizations are discovering that spreadsheets and financial reporting tools are inadequate to deliver timely insights into the underlying trends of their business operations. Perhaps this is why, according to Gartner, only 47% of decision-makers say that financial analysis adequately portrays the story of their business area and its performance. Slicing and dicing stale data from financial statements and reports doesn’t reveal the “why” behind the numbers that would help identify and resolve business issues before they happen and uncover hidden opportunities.
Here are 7 financial analytics strategies to help IT empower financial and line of business users to quickly get the data-driven insights they need to derive greater business value and foster innovation.
Financial Analytics Strategies for IT Leaders
Understand Strategic Goals
Your first step should be collaboration with business managers and financial staff to understand what they are trying to achieve and to determine what data, tools, and analytics techniques will help them reach those goals. You should also try to discover how they intend to measure their success.
Choose the Right Technology Infrastructure
Consider where your data needs to live, on-premises, in the cloud, or a combination of these, and choose a data platform that can support your deployment model(s). Further, you’ll need to test the data platform to ensure that it can handle the volumes of data, number of users, complex calculations, and advanced analytics to support your financial analytics use cases.
Define Data Integration Needs
Data silos can be a huge barrier to delivering financial analytics. Financial analytics often requires integrating data from diverse sources, including internal accounting and payroll systems, customer relationship management (CRM), and sales management platforms. Additionally, businesses may need access to external data sources to benchmark their company, understand changing market dynamics, and identify other factors that impact financial performance.
Ensure Data Quality
Data used for financial analytics must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. To provide quality data, IT needs to have processes in place to assess and resolve data quality issues on a continuous basis.
Implement Advanced Analytics
Advanced analytics will need to be a part of the analytics portfolio that you enable and support. Your business users will need advanced analytics such as machine learning, to gain deeper real-time insights into underlying business trends than spreadsheets and financial reporting can provide. In addition, advanced analytics provide predictive insights to help businesses be more proactive and better hone their future business strategy.
Secure and Govern Data
In addition to security controls to keep your data safe, including user authentication, access control, role separation, and encryption, you’ll need data governance to adhere to regulatory guidelines when collecting, storing, using, and sharing financial information. This requires fine-grained data governance techniques such as data masking to prevent inappropriate access while still allowing visibility to the data users need.
Address Skill and Expertise Gaps
IT organizations are facing challenges in finding and retaining talent with the technology and analytics, particularly machine learning, skills required for modern financial analytics. You will need to evaluate your in-house capabilities and determine where you fall short. Since recruiting externally can be challenging, you should consider training the staff you already have when possible.
The Actian Data Platform simplifies how you connect, manage, and analyze financial data. The Actian platform will allow you to run your analytics wherever your data lives and provides exceptional performance for large volumes of data and users and for running advanced analytics.