On the surface, digital transformation and the finance function seem like strange bedfellows. Even CFOs may be wondering if the company’s digital transformation efforts will make their lives better. From more agility to better alignment, digital transformation initiatives can only mean better and more accurate forecasting, decision making and planning – the evolution of digital financial forecasting.
What is Digital Transformation?
To understand the impact of digital transformation on forecasting, it’s important to first define what digital transformation is.
Research organizations like Forrester council that digital transformation has come to mean different things to different parts of the organization. The broadest definition, however, has the most meaning to the finance function – the use and adoption of the latest technology to support a new business model. Through the adoption and use of technology and digital tools, organizations are experiencing a cultural change of openness and a revolution in operational efficiency.
One important piece of this for finance teams is the availability of data. New tools democratize organizational data, increasing information that finance has access to, but also making finance data available across the organization.
Also key is the adoption of tools that makes complex analysis of large amounts of data possible, while other tools make that data easier to understand.
Combine the availability of data with analytical tools and what-if scenarios, and forecasting becomes both highly accurate and a powerful agent for change.
Digital Financial Forecasting and Decision Making
With the proliferation of technical tools, machine learning, and automation that are available today, the finance team’s job has expanded and become a more critical part of the organization’s strategic planning. Three particular technical areas are contributing heavily to the evolution to digital financial forecasting:
Data transparency – A keystone of many digital transformation initiatives, data integration across the organization provides visibility to data previously siloed within various departments. Sales, production, supply chain data, and more is available for inclusion in forecasting analysis. With a broader understanding of operations that impact financial forecasts, and the speed at which that data is now available, forecasts after data integration are more comprehensive and therefore more accurate.
Processing large data sets – When big data initiatives are included in a company’s transformation efforts, finance teams have a goldmine of data that can be used for forecasting. With machine learning and predictive analytics, data from across the company can be married with other data — like social media, environmental, historical industry data and the like — to create forecasts that are like looking into a crystal ball.
Data Visualization – Some team members are highly skilled at data analysis. Unfortunately, those resources are in high demand, and not everyone who needs to review data and contribute to financial forecasts can extrapolate decision-critical information from a spreadsheet of numbers. Data visualization tools and financial dashboards, however, simplify the meaning behind the numbers so that data driven decisions can be made across the organization. It improves forecasting, as everyone involved can have a clear understanding of the critical data involved in the forecasting process.
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