The question we hear most often from senior finance executives around Digital Finance is: “Where do I start?”
The simple answer – start from where you are. Finance organisations have lots of experience managing change. But digital represents opportunities we haven’t seen before. Opportunities to explore massive amounts of information, to do it quickly, and to distribute knowledge wherever it needs to go. These shifts are not only driving operational improvements, they’re also changing expectations for adding insight.
The key is to define what the business expects from the finance function, and then work backwards to figure out the quickest, most efficient and accurate way to use digital to deliver on those expectations. Still stumped? Below we make sense of some of the technologies available, and explore how they could be used to transform your finance function.
According to Gartner, at least 25 percent of new core financial application deployments in large enterprises will be cloud software-as-a-service (SaaS) by 2018. Cloud services come with legitimate cyber and security concerns that must be taken seriously. But there are also numerous finance applications where cloud simply makes too much sense to ignore.
Where to start? Top applications for early adoption include planning, budgeting, forecasting, procurement, expenses, reporting, and payroll.
Advanced analytics solutions have already made their way into the toolsets of finance teams around the world. As finance organisations work to meet growing expectations for value-added insights, the trend will likely continue, with talent increasingly focused on analysis and interpretation including application of sophisticated algorithms used by data scientists.
Where to start? Advanced analytics can amplify the strategist and catalyst roles of finance. Invest in tools specifically designed to improve forecasting. CFOs tell us it’s the place their business colleagues expect the most support.
Cognitive computing is a general term that covers machine learning, natural language generation, speech recognition, computer vision, and artificial intelligence. Taken together, these tools simulate human cognitive skills, grinding through mountains of data to automate insights and reporting in real time.
Where to start? Natural language science (NLS) gives companies the power to tackle processes around contracts and purchase orders at high volumes without intervention. In addition, natural language generation can supplement routine reports with narrative commentary using personalised text.
Process robotics uses software programs to perform repetitive tasks and automate processes, such as procure-to-pay and order-to-cash. These processes often involve large volumes of manual activities, including data entry and reports. Companies already using the technology have familiar motives: more speed, less cost, and higher accuracy. In addition, automation gives finance an opportunity to move people into functions where they can help the business make better decisions. That’s a good thing.
Where to start? There’s no need to reinvent the wheel. Many finance organisations have discovered good opportunities to reduce costs and improve productivity through process robotics. Avoid analysis paralysis by choosing a proven application and diving in.
Visualisation tools can bring analytical solutions to the enterprise faster, enabling rapid prototyping that reduces development time. These tools also allow companies to ‘see’ developing stories that directly address decisions that matter. Visual metrics are easily understood by more people, enabling analytics to expand beyond the domain of data scientists and quants.
Where to start? It’s often assumed that visual analytics tools themselves will provide insights out of the box. They generally don’t. Like anything else, finding effective solutions requires sifting through options, experimenting, and then settling on an approach that works for your unique needs.
In-memory computing involves storing data in main memory to get faster response times. And because the data is compressed, storage requirements are reduced. The result? Speed and access to quantities of data that were previously unimaginable. Not many CFOs are currently using in-memory technologies, but look for that to change a lot over the next few years. The explosion of information streaming in from the Internet of Things alone could make in-memory a critical capability for companies undergoing digital transformation.
Where to start? Get your geek on. Where do you need fast access to analyse a high volume of concurrent transactions? Where would automated notifications in real-time enable better decision-making? Where do you need dynamic big data calculations in milliseconds? You can’t address these questions without diving deep into data—and in-memory technology enables that to happen.
While the technology is gaining momentum, there are still unresolved issues, including risks associated with regulation, control, and security.
Where to start? Wait and see. Developments in blockchain are moving forward steadily, especially in financial services, where companies could see $20 billion in annual savings by 2022. Even if you’re not in banking, keep an eye on what financial institutions are doing. Current obstacles to blockchain adoption should eventually be resolved, and it will likely be coming your way.
About the Author
Paul Zanker is a Partner in Consulting and is the Asia Pacific Finance Transformation Leader at Deloitte. He is recognised as a subject matter specialist with a strong focus on finance transformation across strategy, transactions, treasury management, shared services and business process reengineering. Paul has deep outsourcing and shared service experience and has worked with clients in Europe, North America and Asia on multiple strategic sourcing initiatives.