Integrate analytics into budgeting and finance
Thursday, March 01, 2018
By Steve Cavolick
“Health care reimbursement is incredibly complicated. Sometimes we’re not paid for months and months so we have to make a lot of assumptions about how much we will be paid. Accounts receivable reflect hundreds of assumptions.”
– CEO, Major Health Care System
Imagine believing your organization was well-run, respected in the industry, and the previous fiscal year had you in the black, but waking up one day to discover that assumptions and procedural errors now have you squarely in the red. That’s what happened to the health care system referenced in the quote above.
No one is going to argue that the health care industry faces huge challenges when it comes to managing finances: medical procedure coding errors can cost a hospital millions of dollars, and more patients are uninsured or rely on Medicare and Medicaid, which don’t always reimburse the full amount of patient care. But assumptions have no place in running a business.
Using analytics takes assumptions out of managing an organization (or at least tell you the probability your assumption will occur). Modern budgeting, planning, and forecasting applications are well integrated into traditional business intelligence applications so that more people can view and contribute to the budgeting process, and have predictive or cognitive capabilities that take the guesswork out of finance.
The evolution to include predictive components in planning analytics is a logical one: it can help improve forecast and budget accuracy, while providing additional flexibility to planners. Some of the additional flexibility includes the capability to add outside information such as seasonality or customer/patient demographics, and the inclusion of unstructured data such as emails, survey data, or social media commentary.
In general, the financial planning and operational analytics platform you use should not only allow you to create budgets and financial plans, but will contain the following modern characteristics:
- Access To Internal and External Data
Users can access and explore information from a wide variety of application sources, including ERP, General Ledger (GL), and business intelligence. Unstructured data options include weather, sensor, social media and econometric data.
- Design Led By Line Of Business
Previously, financial plan models were created by a few power users and deployed to the line of business. This approach is still valid today, but the trend in financial planning is toward a paradigm where business users create financial models in whichever department has a need. This would allow a marketing department to connect a campaign plan to a promotion plan, for example.
- Insight With Advanced Analytics
When tightly integrated with cognitive or predictive capabilities, statistical and predictive analysis will tell you which factors influence your key metrics and desired outcomes. You will understand what is likely to happen and what you should do about it. You can then apply the insights from the predictive models into plans, analyses, and reports and dashboards. Data refinement and visualization capabilities have been simplified, which supports analysis and planning activities being distributed across lines of business.
Adding these capabilities to a budgeting, planning, and forecasting application gives you a competitive advantage over companies that limit themselves to Excel-based or legacy financial planning applications.
The LRS Big Data and Analytics team has 20 years of experience in analytics and data warehousing, including budgeting, planning, and forecasting applications. If you are interested in understanding how we can help your financial management environment optimize its allocation of resources and create a competitive advantage, please fill out the form below and let us know how we can help.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.