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Enabling Digital Finance to Predict an Uncertain Future

  • By AFP Staff
  • Published: 8/24/2021

Digital Finance_article
“Digital finance is about a mindset and awareness of data and how we use it to make better business decisions.” At the AFP FinNext Asia session, “Enabling Digital Finance,” Lance Rubin, CEO of Model Citizn, and Giles Male, co-founder of Full Stack Modeller, presented a paradigm for harnessing your technology stack in service of finance goals.

“Digital finance is not about getting rid of the spreadsheet,” said Rubin. “It’s about using technology that has evolved, including the technology that sits inside Excel. Power Query sits inside Excel; it’s under everyone's nose. It's a low-code, no-code technology. You'll hear a lot about low-code, no-code in the digital world. It's absolutely part of that, yet it's been around for a decade and most teams are not using it. So there's absolutely an opportunity for change.”

It is important to think about and be aware of your finance tech stack, and to understand the difference between financial modeling and predictive analytics. Evaluate where you are today, and set goals based on your finance and organizational strategy. That is critical. “If you don't know where you’re going, whatever you build is going to be wrong,” said Rubin. “Stop, go slower, assess where you are, assess the technology under sound principles.”

Phase 1: Scoping

Where does an overwhelmed FP&A professional even start the process? “You start off in the scoping phase,” said Male. The scoping phase is where you answer the critical question of why: Why are we doing this? You also want to know what questions it will be answering, and what it is not going to be doing. Increasingly sophisticated tools help you to work faster, but also help you to make mistakes faster (and a greater expense).

“One of the biggest mistakes we see in financial modeling is rushing that why phase,” said Male. “You want to really hone in on whoever you’ll be dealing with, listen to and engage with your stakeholders. The more time you can spend in that why bucket initially, I promise you, it pays dividends later on when you start building to a plan.”

“Start the conversation with your purpose,” said Rubin. Understand why you are doing this and what your current data challenges are. Be able to articulate what success looks like. “Budgets aside, people aside, skills aside, what does success look like? What do you want to have as an end point?” said Rubin.

Phase 2: Planning

“A lot of people build really complex models so that others are dependent on them,” said Rubin. “To be honest, I did that myself. I saw myself as the guru of the Excel model in the workplace and therefore felt that I was secure, but the reality is that I wasn't. Through multiple restructures in the model, the model got replaced and you realize that it is not that important. What is important is that other people can use your model.

“When I left corporate and started Model Citizn, and we built models for clients, suddenly I'm not the most important user of their model; my client is, and the people who I'm coaching and training. There is power in simplicity.”

When you start thinking about dipping your toe outside of Excel to try the latest tool or technology, Rubin and Male suggest taking a diagnostic approach. Start with one key question: What is it that I am going to be getting? “Too often, people buy some fancy tech, and it doesn't actually get used, or it doesn't get used to drive business decisions,” said Rubin.

Phase 3: Building

“You have to think far more about futureproofing and the users of that model, and making sure it's robust time after time, month after month, year after year,” said Male. Futureproofing means establishing a process that allows current and future data flows to enter the model by creating consistency, allowing for changes, and responding to validating output. If there are no rules, there is chaos. “If you haven't got a teamwide approach to what you're doing, if you don't have a set of principles that you're sticking to, then you're just going to end up in this Wild Wild West of spreadsheet nightmares,” said Male.

The well-known acronym FAST – flexible, appropriate, structured and transparent – is a valuable reference for the characteristics your model should have. For a financial model to be flexible means you are building something in which the drivers and inputs can be changed without the underlying calculations falling apart.

Appropriateness is about picking the right level of detail to go to. The right answer here is not always most or least, but rather a modest step or two up to a level that gives you the right insights.

“Structure is probably the most important fundamental that often gets missed,” said Rubin. It goes beyond the spreadsheet into knowledge management, sharing and understanding. If you do not have the fundamentals of the model in place from the beginning, no one else can look at it or learn from you; it will be as if they're reading a different language. Without structure, there is no context – no start, no end, no middle.

Last, but not least, is transparency. Transparency is about simplicity and making things as clear as you possibly can, not just for you, but for anyone else who needs to use the model.

Phase 4: Considerations for Implementation

One of the most important considerations is integration. The ability to integrate your automation with your analytics and financial modeling is critical to maximizing the value of your digital finance stack. If you do not have that, then you are just going to be resorting to Excel middleware, copying and pasting, and then your people become your integration. Data wrangling is critical, and being able to use tools that perform that task is incredibly powerful.

Another key consideration is the total cost of ownership. Technology costs go far beyond the license fee. While the upfront build costs are known and well-established — from experts that build the infrastructure, build the model, build the data model, build the automation layers, and build the workflows — you also need to factor in the cost of ongoing maintenance and the cost of change.

One of the biggest challenges in the digital finance world is the virtual nature of work. Models and decision-making tools need to be built at warp speed by colleagues who are now physically separated. We still need to make sure these basic aspects are maintained in a structured way. Implementations and ongoing modifications require communication across teams to understand changes and how to utilize the systems.

To ensure your systems are used, remember to build in time and budget to train users on the system! What training is available for the system you’re going to purchase or the technology you’re going to buy? How much training is actually available? Is it going to be left to you to learn on the job? If that is the case, you are taking a risk.

It is also important to consider administration and security, specifically, how much control is available in terms of users, in terms of information? Can you control what people see and limit the access to appropriate and relevant information.

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