Development in new data technologies like AI, ML and Cloud has hardly touched financial reporting systems and processes. Most finance teams still spend a significant amount of their time manually consolidating long spreadsheets with numerous complexities. It is still a challenge to create accurate, scenario-based, financial forecasts and reports.
In the meanwhile, the job of a CFO is becoming even more complex. Many traditional corporations are moving to more and more multi-layered ways of interacting with their customers (for example to B2B2C models), which makes tracking revenues and costs laborious. At the same time, the evolution of customer demands has never been faster. Not only do consumers want their products quickly, but they also expect them to be highly personalized.
Speed, however, is a tricky thing for CFOs because it can backfire. It is hard to have a team of analysts crunching numbers quickly every day of the week. The combination of speed and manual consolidation work has repercussions on quality, accuracy, and detail, which impacts trust from GMs and other C-Suite executives.
According to a study by Workiva, the financial reporting process keeps 97 percent of CFOs awake at night. And the graph on the right is even more telling.
The Solution to Big Data in Finance that is not a Solution
The challenges with speed, manual work, and scenario-based forecasting in Finance are not new. The answer to them has traditionally been: “just roll out the same accounting system everywhere!” This has typically been done as part of Enterprise Resource Planning (ERP) rollouts (accounting being included in the ERP in these cases). This approach, however, presents many challenges.
Slowness. ERP implementations take years. What does a CFO do during all of those years? They continue doing manual work, of course. This goes directly against the need for the faster time to insight from Finance organizations.
Integrations. Achieving 100% coverage in all legal entities of big corporations of the same accounting system is close to impossible. But even if it is achieved, it is usually a moving target anyway. Big companies are constantly acquiring and selling legal entities, and it therefore is not reasonable to expect complete compatibility across all accounting systems at all times.
Vendor lock-in. Having the same ERP or accounting system across the entire company is a big commitment to one vendor. These vendors usually lock data into their own systems and make it onerous to export. In contrast, the new ways of doing business command simple and unbound access to data across the organization.
The Growing Non-Financial Reporting Needs
Data is changing the way business is run. Finance is at the epicenter of this revolution. Finance used to be simple: Profit and Losses — Balance Sheet — Cash Flow. Tracking the impact of business operations has since become a lot harder.
In the past, it was sufficient for global corporations to track sales across a few dimensions, like countries and products. Now, with the introduction of CRMs, new data has come in and companies want to know a lot more. General Managers and leaders ask about lead generation, opportunities, and market share. All of this needs to be reconciled with financial reporting.
Finance departments can no longer afford to focus solely on classic financial reports but need to reconcile and analyze a lot more information. This information is not typically available in ERPs or accounting systems, so again the idea of solving all reporting needs via ERP/accounting rollouts is not in line with current analytics trends.
With so much information coming in, and the ever-increasing need for fast decisions, organizations face the risk of producing incorrect interpretations of data. We fail to understand how everyday objects work, so no wonder that we struggle with complicated data dependencies and concepts. How to make sure managers are looking at the right picture, driving the right decisions? Finance quickly becomes a critical business partner due to their ability to analyze numbers.
Image explanation: When their understanding of the basics of bicycle design was assessed objectively, people were found to make frequent and serious mistakes, such as believing that the chain went around the front wheel as well as the back wheel. Errors were reduced but not eliminated for bicycle experts, for men more than women, and for people who were shown a real bicycle as they were tested. The results demonstrate that most people’s conceptual understanding of this familiar, everyday object is sketchy and shallow, even for information that is frequently encountered and easily perceived. This evidence of a minimal and even inaccurate causal understanding is inconsistent with that of strong versions of explanation-based (or theory-based) theories of categorization (Lawson, 2006).
Due to its strict quality requirements and expectations, Finance is becoming increasingly important for businesses to make the correct interpretations and thus more accurately simulate the impact of business decisions and events. With this in mind, we can ask ourselves: What are the big improvement opportunities in Finance today? How can technology further help Finance influence the business leader’s decision-making?
Growing Opportunities with Financial Data Analytics
One of the greatest opportunities in Finance today is automating the consolidation of data and cross-referencing of multiple sources of information. This consolidation will improve financial forecasting and simulations. For instance, the ability to track consumption in factories at each step of product creation enables deeper insight into the actual cost of production and allows for more proactive actions to protect profit margins.
This is not necessarily Big Data, but more Complex Data. Financial insight needs to be correct and simulate outcomes of disparate business decisions. The complexity lies in the dependencies across all the business processes and how they ultimately impact the P&L and Balance Sheet.
Working with Complex Data, as well as building automated processes to improve forecasting and value of insights calls for a different, more foundational approach. Instead of focusing solely on the final outcome, such as the Excel reports, it is critical to dive deeper into the financial value chain, all the way down to data foundations. We see three areas of improvements:
Gathering data. ERP systems store only a subset of the required data to provide accurate financial predictions, so gathering data in an easily accessible way is the first low hanging fruit today. Data Lakes – a data repository that operates as unified source of truth and detailed information – are a good way to gather information because they allow for storage of significant amounts of data at very low costs.
Processing data. Purely Excel-based financial consolidation processes are problematic. These processes are not only prone to error, but are exceedingly difficult to audit. How do you know who added a $0.5m adjustment to your sales forecast in Excel? Sure, if you have a well-organized team you might know, but there is still doubt. Enough doubt to make you pick up the phone, call someone… a slow, uneasy process. It is much better to have financial rules strictly defined in systems that track every transaction and change.
Interpreting. Some vendors are trying to push the idea of “autonomous organizations”, where people are removed from processes to save costs. At dyvenia, we disagree. Technology should help employees to focus on areas that bring more value, and not replace them. . For this reason, our vision is to empower analysts. Financial analysts are usually busy consolidating information, but in this new world, they could do so much more. They could work hand in hand with data scientists to build financial models and simulate scenarios for various business decisions and events. The collaboration between Finance and Data Science works well because Data Science brings the technical expertise, while Finance brings the rigour and focus on financial impact.
Here is the thing. Forecasting is the holy grail of Finance, but the best road to a good Forecast is a deep comprehension of figures AND conversations with experts in the business. This is essential to understanding trends, making better decisions impacting the books, and improving profitability. Business experts know and appreciate finance analysts with a deeper data image – an image that is supported by analyzing figures across many systems and processes. The trust-building this enables is invaluable. In time, it will make information sharing easier and ultimately allow Finance to become more influential.
Taming the Beast… One MVP at a Time
The data analytics beast does not have to be — and shouldn’t be — tamed in one big battle. That would be too costly, too slow and — in our experience — prone to fail.
Instead, the big problem should be broken down into smaller phases (MVPs), so that your data-fueled evolution is run in an agile, result-focused and pragmatic manner.
Where to Start?
Your data-fueled evolution should start with cost-effective initiatives that can prove their value fast.
Look around. Is there a financial reporting need that could be solved in three months or less? Start there. Avoid big projects. Big projects are like trucks, in order to get them going you need a lot of initial push. Small projects are like bikes, they need less of an initial push and can adapt a lot more to various types of roads ahead.
Only after your initial MVPs prove their value should you start exploring your strategic data direction in order to industrialize them for the company-wide avail. In addition to serving their core purpose, your discoveries from the MVP stage will serve as invaluable case studies for your strategy and future products.
Only 3% of CFOs today don’t lose their sleep over their financial reporting process. The vast majority of finance teams still spend an overwhelming amount of time consolidating never-ending spreadsheets.
But there is a reliable, efficient and manageable solution to this conundrum. The answer lies in an augmented analytics team that not only takes care of automating the consolidation of data and cross-referencing of multiple sources of information, but also works closely with the business stakeholders to ensure the high quality of information.
Starting such a team should be time and resource efficient and aimed at achieving quick wins that inform future data endeavors. They should be kicked off in a close collaboration with a strategic partner whose purpose is to equip you not only with a solid data foundation but also with all the required knowledge and skills to someday be able to run such projects independently.