The Digitization of the Finance Function
Excerpt from Deep Finance: Corporate Finance in the Information Age
Finance transformation involves the expansion of the finance department into a strategic, forward- looking organization that becomes the central figure in an organization’s strategic initiatives by integrating new technology (APIs, databases, and data science) to provide greater management insights. CFOs maintain their role as a key member of the strategic leadership team. Finance becomes faster, more efficient with precision accuracy. By defining the transformation ahead, you can give senior leaders and your entire team a peek into the future.
But before peering into the future, let’s look back at the past to see how we got here.
Accounting, like all areas of business, began a transition to the cloud in the early twenty-first century. Financial records were moved from filing cabinets and storage boxes to digital files on local machines, then to the cloud, which has proven safer and more scalable for most businesses than managing and storing files on an internal computer network. Accessible from everywhere and available to be backed up at any moment, the cloud has tightened security while allowing the democratization of available data within a company. This doesn’t just make an accountant’s job easier; it elevates everyone’s tasks and time spent within the company.
No More Gatekeepers
Communications networks and cloud computing led to a paradigm shift in accounting processes. Accounting professionals began to understand and adopt the usage of these new tools to improve accounting operations. They suddenly had wider access to data, tighter security, and better data retention. Before cloud access, accounting software was built for individual PCs or computer servers. But with the cloud, data of all types became available to more people.
In this new paradigm, CFOs or controllers can’t act as guardians of the data now because everybody in a modern company expects to have access to data in real time. The financial metrics that are tied to performance are key to improving that performance; the quicker people can see them, the quicker they can act on them. In a digitally transformed company, even those operating on the same roles they had throughout the last decade, expect to have ready access to data.
Cleaning Up and Moving Up
Easy access to data isn’t just about who can turn on a computer and look at data. The time it takes to organize, locate, and retrieve data also maximizes the efficiency of an employee’s time. Anyone who has worked in both an “old school” and “new school” office knows what I mean. Cloud computing has allowed companies to keep a cleaner virtual office and make room for more data and the innovative technologies that make use of that data, like Artificial Intelligence (AI) and Machine Learning (ML).
Businesses are built on the belief that they can provide goods or services in a better, cheaper, or faster mode than their competitors. For this reason, the primary focus is typically on the customer-facing side of the organization. A common problem for any business (but especially for startups) is that not enough attention is paid to the back-office operations. There’s nothing inherently sexy about finance or accounting software, and it is generally hard to show how those functions provide value to a company’s bottom line. As a result, it is not uncommon for back-office information to be disorganized and not used to its greatest extent.
I like to think of a company’s data warehouse in terms of old-school filing cabinets. In a well-organized file system, company contracts, agreements, and other information were easy to find. But if there was no system in place to file the data, it could be impossible to find what you were looking for. This was important when paper files were the only way to track data, but it is especially important in the digital age.
The amount of information available today compared to the pre-digital era is greater by several orders of magnitude. But if the data can’t be identified, labeled, and harnessed, it is of no greater use than those cabinets filled with paper documents. Cloud computing changed the way that we see and sort large amounts of data. If your company wants to take advantage of ML algorithms in the process of your digital transformation, you will need to get a handle on your data
Access Leads to Automation
Access to accounting and operational data is only the beginning for an aspiring data-driven organization. Once a company has established a methodology for data collection, the next step is to put in place ways to retrieve, organize, and analyze that data to improve operations, budgeting, sales, service delivery, and other areas of your company
Data can come from many sources. Accounting information is just part of the puzzle. The real power of data comes when organizations can combine information from myriad sources like customer relationship management (CRM) tools, project management software, marketing lists, and inventory systems to find correlations and linkages that better describe everything from business processes to customer behaviors.
Once a company starts collecting information, the managers will inevitably strive to gather more. That is often one of the first hurdles for small or mid-sized businesses looking to improve operations through the efficient use of data
Truthfully, most companies are not going to have access to massive troves of data that will illuminate everything about their consumers and operations. Data limitations can prevent companies from being able to make accurate predictions. The world’s largest technology companies like Google, Facebook, and Amazon have gathered vast amounts of data on all of their users; that’s how their analytics can get so predictive.
For a peak into how analytic intelligence is advancing read the series From Average to World Class – Next Generation Finance by Jesper Sorensen and Robert Zwerling
In the case of data, bigger is definitely better.
What does that mean for small companies who are trying to increase their data capabilities? Fortunately, smaller companies now have the ability to use the smaller amounts of data that they have to start using predictive algorithms and elevate the roles of everyone in the company. This can be done initially through adoption of Robotic Process Automation (RPA) to connect and automate systems. As the company grows and collects more data, they can actually start training ML algorithms to increase automation and continue the shift to becoming data-driven. Cloud computing, RPA, ML, and other innovative technologies are all steps toward a full, effective digital transformation.
While cloud computing was about access, ML is about automation. A great deal of time was saved through the advent of computer technology and moving accounting from pen and paper to digital. In a truly automated modern accounting department, all manual efforts of entering invoices, reconciling bank accounts, journal entries, and accruals disappear. All of the work required to get the data into the system is done for you. ML tools can be trained to read and interpret printed documents, emails, or data that comes directly from an application programming interface (API) to a customer or vendor’s network.
Machine Learning is the next step for many companies and one that many have already started to take and use in their transformation. The possibilities for further elevating your company may sound just as futuristic as the printing press once sounded many centuries ago, but it’s those possibilities that will bring us into the next phase of accounting and deep finance.
Like cloud computing, ML algorithms may seem a jump too far in the future for many in the world of finance. But technology like this is crucial for an effective digital transformation. Accountants do not need to retrain in computer science to implement these tools and create a better and more cost-efficient workplace. Simply understanding the possibilities of ML will get the job done.
I have explained this to many people in finance who are hesitant to embrace the future of accounting. I tell them that failing to embrace ML is like being a race car driver and not understanding the new braking system in your car. Do you have to be able to build the braking system in order to be a good race car driver? Of course not, but if you understand the system’s capabilities and limitations, you’ll be able to drive more confidently and will be able to use the system to its greatest impact.
You must know the base technology the industry is using (and trust me, the industry is moving to make cloud computing and ML the “basics” of their process). Whatever role you’re in, technology is a part of it and it’s moving fast. If you go into a digital transformation thinking, “I’m a finance guy, and I need to stick to finance,” you are missing out. If you’re not keeping up with technology and you’re not keeping up with the latest tools out there, you’re doing a disservice to your company. Even if you’re the best finance guy or gal in the world, if you’re using the wrong tools, it’s going to show. The future of accounting is here, and as we dive deeper into the base technology that the industry is using, you will find that you can provide more value to your team and your company.
Dave Sackett is right in step with the message in his CFO Talk, Leading Edge Technology Update Designed for CFOs
How Does This Work?
When employees understand the true potential of a digital transformation, they know how to make their lives easier even outside of simple data entry tasks. Instead of scouring data for trends and correlations, software that automates complex processes and monitors anomalies in real time can give employees a “heads up” on what to look for.
Let’s look through the lens of a typical accounting question: why did our cost of goods sold remain the same during a period when our revenue went down? These variable costs are normally tied to revenue, but anomalies do take place. Instead of an analyst going through every invoice themself, they can spend less time working while ML algorithms spot (or even prevent) problems.
In a non-automated environment, an analyst would log in to the accounting system and pull customer invoices or sales and maybe break them down between product types. Customers may be invoiced for some bundle of software and services. The analyst would know that software products typically have a higher margin than service products, so based on her years of experience, she might start by looking to see if the product mix had changed. Maybe in the month being analyzed, the company sold a higher percentage of services than software, and the cost of goods sold remained higher because services are more expensive to offer.
In a rule-based automation system, you could program a computer to flag such an anomaly. In this example, you might program in a range that says if services make up less than 25 percent of any billing cycle, set a flag or a warning. In a rule-based system, you have to program the exceptions you want to monitor. But in a full ML-driven environment, you could run training sets based on the data you have and ask the computer to find correlations between the sets. The computer may find more examples than human eyes would have considered .
In the previous example, the answer may not have been solely based on the fact that the product mix shifted. Based on how you’ve trained your ML system, it may be able to detect something buried deeper in the numbers. For example, a vendor might have changed their billing rate, or maybe one of the costs was higher than usual because a single job took way more resources than a standard job. A human may be able to find this, but it would be very time consuming. Training algorithms with a complete view of vendor billing would allow the machines to identify variances more quickly than humans. This can only happen when analysts and financial teams embrace the digital transformation and understand ML systems beyond how they grab data from simple invoices.
Finance professionals around the world are facing the same question: Should they step into a new age of AI, ML, and big data, or will they stay in the shadows? For many, the choice to step forward feels like exposing themselves and the members of their field to an inevitable replacement by robots, but this isn’t correct. These concepts may have already influenced your processes at work—they are a part of every smart technology in our homes, cars, and offices. They aren’t going to replace accountants; they will actually give them better, more fulfilling jobs. By understanding the basics of what these technologies can do, we can see the opportunities available within finance automation and beyond.
To learn more about corporate finance in the information age, check out Deep Finance on Amazon.
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