Over the years, our team has been tasked with examining and/or rescuing a variety of enterprise software implementation failures. As such, we’ve seen a variety of nasty consequences, from month-long shipping standstills to payroll disruptions.
According to Bill Baumann, Director of Software Expert Witnesses, “One of the gnarliest consequences we’ve seen, especially in recent years, is an inability to make accurate financial forecasts. These businesses expected their new system to help them make data-driven decisions, but instead, they were making bad decisions that were costing them customers.”
Especially if an enterprise software platform uses machine learning (ML) algorithms to make financial predictions, you must have accurate and complete data.
Here are some of the reasons that modern enterprise software can cause financial forecasting problems and our advice for circumventing these issues before they happen.
7 Reasons for Financial Forecasting Problems
1. Rushed Data Migrations
Are you planning to migrate all of your disparate business data onto a centralized platform, such as an enterprise resource planning (ERP) system? If so, be sure to allocate plenty of time and money for the data migration phase.
Whether you’re transferring information from one on-premise system to the next or moving it onto a public or private cloud, this is one step you can’t afford to rush.
When financial forecasting challenges occur, it’s often because there’s a disconnect that occurs. Blending data from multiple sources into an aggregated application can streamline business operations. Yet, at the same time, it also opens your business up to inconsistencies, inaccuracies, and simple human error.
Give this stage the attention it deserves to ensure all data is properly connected, trustworthy, and reliable.
A Failed Payroll System Implementation
Panorama’s Expert Witness team was retained to provide a forensic analysis and written report to the court regarding the failed implementation of a major software developer’s ERP/payroll system.
2. Transitory Work Environments
A shift in one’s work environment can compromise data quality. As more employees continue to change from in-office to at-home work, companies are looking at the cloud more seriously than ever.
While this allows them to expand exponentially, it also means that entire departments could be sitting at home, unable to access the information they need.
Remote workers need the ability to locate and use the exact same data as their colleagues. This means equipping them with intuitive, user-friendly tools that reduce their reliance on IT support and empower them to make business decisions.
3. Lack of Human Involvement
When companies experience financial forecasting failures after implementing ERP software, we look at how they’re balancing ML capabilities with human involvement.
Yes, these technologies have come a long way and they hold a great deal of potential. However, humans still possess their own unique knowledge, judgment, and experience that can’t always be replicated on a machine.
Often, the problem is that companies are over-relying on their software, expecting it to do everything and automate each function.
Instead of looking at your system as an all-in-one decision-making solution, think of it as a helpful tool to support your human teams. The insights and forecasts that it provides will be undeniably useful, but they can’t replace the work of your teams.
4. Poor Quality, Inconsistent Data
In some cases, companies handled the data migration stage well but still ended up with forecasting problems down the road. When this roadblock occurs, the issue isn’t likely with the transition itself. Instead, it’s with the actual information that was transferred over.
When data isn’t correct or is sub-par, you’ll notice the difference in your forecasting accuracy. Your leadership team should be able to trust that the information they’re referencing is right, or it’s not useful.
5. Explaining Complex Machine Learning Models
The realm of financial forecasting can be intricate and complicated. When looking at a decision made by a machine learning algorithm, you might be able to see the prediction, but exactly how the AI got there can be a bit of a mystery.
Not only can this confuse your employees, but it could also make it difficult to explain forecasting results to your stakeholders and regulators.
6. New Security and Privacy Concerns
When you mix technology with sensitive or confidential data, there will naturally be concerns about privacy and security. Without proper protocols in place, such as user authorizations, information could travel from a secure place into a compromised one.
Finding the right technology expert is essential when implementing machine learning. This ensures you’re implementing standards around who can access which types of data and how. Without such measures, data could get into the hands of the wrong person, where it could be purposefully or inadvertently changed or manipulated.
7. Issues Around Bias
Data sets inherently contain biases. When you train ML algorithms on them, those biases can become amplified. Sometimes, users don’t realize this has occurred until it’s too late and their forecasts are skewed.
It’s important to catch these biases and eliminate them before they can go further. Routinely monitor your machine learning models and examine any biases that may be present within them.
Avoid These Financial Forecasting Problems
When you implement an ERP system, you expect it to deliver on your expectations. You don’t expect to experience financial forecasting problems right from the start.
An unbiased financial software expert can help you prepare your data for new enterprise software so you can make the most of the data at your fingertips.
Contact our enterprise software consultants so we can help you strengthen your forecasting efforts. Before you begin ERP selection, be sure to request a free ERP consultation.
