What do you like best?
My company relies heavily on data analytics to provide our clients with comprehensive evaluations of their financial performance. In the past, we were restricted to using canned report templates built from our workdriver software or any existing software reporting our clients may have already had. However, once implementing Spotfire analytics into our workdriver tool, my team has been able to pull large amounts of financial data and present it in a plethora of beneficial ways for our clients. Spotfire analytics offers a user-friendly platform for analyzing, presenting, and researching hundreds of thousands of client accounts in mere seconds, and provides data visuals that are easy-to-read and meaningful for our client to better manage their organization's revenue and costs. My favorite feature is the sheer amount of data this software can handle - Spotfire can load and calculate a year's worth of my client's patient records (on average, 900,000 records) without a problem. Any other tools I've used (e.g., Microsoft Excel, Access) would instantly freeze or shut down when trying to process that much data.
What do you dislike?
Calculating custom fields in Spotfire is definitely not as intuitive as it is in more common programs, such as Microsoft Excel. Often times my team and I need to manually calculate the data in our Spotfire tool and writing expressions in Spotfire in order to plug those formulas is much harder than typical formula-writing. Spotfire uses a different set of indicators for it's formula language that is more in line with SQL writing, which can be difficult for beginners or those not familiar with coding. Additionally, Spotfire data sheets require each column of data to be categorized into a certain "type" of data so that the program can read the field values correctly and thus, calculate them correctly when needed. For example, a number field value must be listed as an "integer" data type, an alpha field value must be listed as a "string" data type, etc. The tool will initially "guess" what type of data each column has in it, but it is up to the user to manually change any that are not categorized correctly. If you do not change them, they may not calculate correctly in any of the subsequent analyses you do with that data in the tool.
Recommendations to others considering the product:
I highly recommend familiarizing yourself with case writing before you dive into this tool. When you understand the nuances of Spotfire's formulaic language, the data testing benefits of this tool are endless. You can customize data testing templates in Spotfire and configure them to automatically update every time you feed a new data file into it, so that you can test multiple iterations of files against the same template with ease. For example, my team built a Spotfire template to identify which departments in my client's organization were writing off revenue to the company's debt vendor (i.e., writing off money that will never get collected or paid) by setting the formulas up to identify a certain transaction code, and tie it back to which department in the organization had used that code. We were then able to load multiple files into the same template (e.g., financial reports from April, May, June) and analyze which departments wrote off the most debt for each month. We immediately saw a trend for a specific department, identified that risk, and were able to help our client save hundred of thousands of dollars by preventing those write-offs in the future.
What problems are you solving with the product? What benefits have you realized?
Our primary focus is to identify financial risks across my clients' organizations and use data analytics to troubleshoot process inefficiencies and realize benefit through improved revenue streams. As a result, my team must look at historical data records from huge, multi-facility organizations, which is often difficult given that these databases can easily exceed one million records. Spotfire analytics has changed the game for my team and my clients. It allows quick, comprehensive analyses of massive amounts of data, and as a result allows our team to solve key financial problems for our clients (e.g., "What is causing a spike in our AR days? Why has our debt population increased over the last year? What facilities/departments are bringing in the most/least revenue, and why?) much quicker. It allows my team to look at more data faster, and as a result we're able to uncover huge populations of financial risk that our client was otherwise oblivious to. We're then able to bring these numbers to light and work with our clients to implement functional/technical process improvements to prevent further financial risk.