I’ll be the first to tell you, my background is non-traditional for a data scientist. I started my career in Bio-statistics, and then learned accounting and finance when I learned those jobs paid much better (at the time). Unsurprisingly, I found basic accounting to be very, very boring, so of course I stuck at it, earning my CMA and CPA, because, you know, that made sense.
But accounting did introduce me to databases and big data. A typical accounting or finance workflow involves requesting a bunch of data extracts from a data warehouse or lake, then spending a few days massaging the data into Excel and PowerPoint for a report.
For me, even early in my career, this was pants on head crazy. Why would you waste all that time repeating the same process manually? It’s like building a new car every time you want to drive somewhere. I quickly learned how to query databases, then got into data application development- luckily, the field of data science was emerging and I could use all that stats knowledge from bio-statistics. Now I am using stats to predict business outcomes instead of protein folding, and I’m using applications to automatically produce reports instead of copy-pasting into Excel.
I know what you’re saying “Dan, that’s a nice personal history, but I don’t actually know or care about you, and I’m quite bored already.” And understandably so, but there’s a method to my rambling. You see, I have now come full circle. With the Analytics Benefit Calculator, the ABCTool, I’ve used some financial decision making tools, like Net Present Value, to help you decide if wasting all that time dumping data into Excel really is worth it, or if it makes financial sense to invest some funds for a business intelligence tool up-front in exchange for long-term efficiencies and higher output from your resources.
Feel free to ask any questions about how to use the tool in the comments; I’ll keep an eye on the feed to help you out.
Calculate the ROI on your BI Investment!
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About Daniel: Using Business Intelligence platforms to bridge the gap between Advanced Data Analytics and the efficient effective principles of accounting, Daniel applies technology and mathematics to make business faster and smarter.
Daniel has managed solutions for diverse client sectors such as as advertising, military, insurance, and oil & gas. These solutions include Business Intelligence Platform management, online key performance indicator identification and tracking, to full predictive data model construction. Although the analytic solutions are often mathematically complex, Daniel’s presentation and academic background ensures any insights delivered by solutions are relevant and simple to understand.