Editor’s Note: This blog features guest blogger, Gary Simon, Chief Executive of FSN & Leader of the Modern Finance Forum on LinkedIn.
Data is widely regarded as the fuel of the digital economy, yet for most organisations it remains, ungoverned, incomplete and under-exploited. “Data Mastery”, which FSN broadly defines as the ability to use data as a corporate asset remains stubbornly elusive. According to FSN’s 2020 research, “The Future of Automation in the Finance Function”, a mere 19% of modern finance functions claim to have mastered their data.
Traditional approaches to information management, driven from the bottom up from ERP and other operational systems through to corporate performance management (CPM) and Business Intelligence are struggling to keep pace with the demands of the digital economy. At the same time, newer technologies such as Machine Learning, Artificial Intelligence, predictive analytics, and data science, remain largely experimental within the finance world, often because they have been starved of the abundant and dependable data resources needed to make sense of a rapidly changing business landscape.
However, the coalescence of three pivotal technologies, namely: data blending, machine learning, and robotic process automation (RPA), is about to set a new paradigm for the way we think about data and digitally transform our core financial processes.
Data Blending
Modern, data blending technologies are rapidly displacing the ETL (Extract, Transform, Load) technologies of yesteryear and not before time. According to FSN’s “Global Innovation in Reporting research 2018”, 55% of finance functions say they spend too much time on data collection from multiple data sources and 58% say they spend too much time on cleaning and manipulating data. 82% agree or strongly agree that innovation is needed to bring all data into one single unified reporting environment and 86% say it is needed to drive better insights about the business or to automate and accelerate the reporting process.
However, with modern data blending tools, finance functions can prepare and blend complex data pipelines including spreadsheets, documents, cloud sources, and unlock more insights by enriching data with non-financial data harvested from operational systems or external sources such as demographic, firmographic, and geospatial intelligence.
Machine Learning
When it comes to machine learning (ML), the same research finds that 55% of finance functions believe it will have a powerful role in predictive analytics and 57% are optimistic it will drive deeper insights that could not be achieved by traditional means. The tide is turning, and ML is becoming more accessible to finance functions, with newer technologies allowing ML pipelines to be built and operated in fully automated, assisted, or expert modes. Furthermore, ML can even be used to spot and fix the data quality issues that beset the finance function before they harm results, with automated data health checks replacing historic methods of data validation.
Robotic Process Automation
As for automation, the opportunity is vast. For example, FSN’s “The Future of Automation in the Finance Function 2020” found that only 12% of finance functions claim to have fully exploited opportunities for automation in the record to report’ process. In addition, 40% say they “could do more” to automate the planning, budgeting and forecasting process with a further 36% saying they “could do a lot more”.
Robotic Process Automation (RPA) is set to make a profound impact on the limited process automation to date. Modern RPA can automate repetitive, data-driven processes and more importantly, trigger automated actions and drive fast analytic outcomes into dashboards, applications, or other RPA processes further downstream.
But what if these three technologies could be weaved into a continuous process from data capture and blending, through to ML driven analytics – all supported by dedicated workflows and RPA. That is the game changer that is set to digitally transform the office of the CFO. It’s a new paradigm, for sure, but after three decades of CPM the office of the CFO may be ripe for disruption.
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