In most companies, highly qualified (and highly paid!) finance professionals spend more than half their time combing through and compiling data from a variety of spreadsheets, notes and internal systems. A majority of that effort includes checking, cleansing, and reusing old data with very little time to correlate it against outside factors or information from external sources. In fact, recently one Head of FP&A told me about a challenge she still struggles with; having enough time to meet her operating departments’ demands for situational analysis, while also fostering useful discourse around the data.
Trying to plan or forecast in today’s dynamic business climate can quickly point out these limitations of traditional FP&A processes and systems. Overcoming this has become so important that we are now seeing a secular shift in the way finance looks at new technology. While historically the adoption curve for new technology in corporate finance is often the slowest, today’s climate is forcing the Office of the CFO to expand outside of its traditional accounting and back-office role and become more proactive and strategic. New technologies designed specifically for the sector are combining big data, predictive analytics, data visualization, mobility, and machine-learning to work iteratively with finance experts to reimagine consolidation, reporting, planning and forecasting – the foundational processes for understanding and driving business performance.
Senior Analyst Krishna Roy from 451 Research just last week dove into the topic. Her new research on machine-learning notes that while we’ve seen its adoption by CIOs in other technologies, for CFOs a number of factors had to occur first before machine-learning could start hitting prime time. Roy points out considerations like the ability to store and aggregate massive amounts of data and, “…the ready availability of economically priced processing power because machine-learning algorithms need a great deal of horsepower.”
For CFOs seeking true transformation of financial processes, this is arguably the most important point to understand. Within the CFO technology landscape of EPM, Roy is describing the difference between legacy companies who utilize the cloud as a hosting platform, and modern companies like Tidemark who believe the cloud is more. Today, organizations of all sizes can tap into the cloud’s explosive potential to package and make innovation available to everyone. For Tidemark, this means utilizing the cloud not just as a hosting platform to make our system available anywhere at anytime, rather we see the cloud as a computational platform to continuously innovate, build, and deliver new services to our customers. Roy, who had a chance to spend time in our platform the last couple of months, noted in her report that:
“Performance management in the cloud' pure play Tidemark Systems has also brought machine learning into the FP&A mix for predictive analytics use cases. Tidemark is using Apache Spark for processing and Spark's MLlib machine-learning library for predictive forecasting. The aim is to enable companies to understand customer churn, market share or some other forward-looking analysis related to performance management – without having to go to a statistician or analytics expert to get this type of analysis.”
Modern CFOs need modern technology to help solve today’s challenges. A true cloud inclusive of machine-learning enables the analysis of nearly unlimited numbers of attributes or sets of data, supporting financial leaders with the power to help their departments uncover revenue and operational insights, plan better and work smarter. If you’re interested in more in-depth analysis on machine learning’s impact on finance, take a look at our complimentary research, “Forecasting the Impossible and the Improbable.”