This is the first article in our Frictionless Finance Blog Series:
Process Mining for Frictionless Finance: Q&A with Ray Wang Part 2
More coming soon!
Following our last Q&A blog with Ray Wang, Principal Analyst and Chairman of Constellation Research, we went a little deeper on the role of process mining and added an ‘Enterprise Performance Acceleration’ process layer to finance operations to achieve Frictionless Finance.
Here he broke down the 7 major reasons why you’ll need process mining technology in today’s shift towards Frictionless Finance and the 5 stages of process mining in Finance to give it context.
A: Process mining isn’t just something that’s desired, but needed by finance leaders today in order to achieve Frictionless Finance. Because the future of finance is powered by intelligence, and actionable metrics—and process mining is the only tool designed to truly deliver these needs. Here’s the 7 major reasons why you’ll need process mining technology in your journey towards Frictionless Finance:
Perception: Without process mining, you don’t really know what’s happening throughout each stage of your finance processes—period. Process mining technology draws from your systems—across departments, helping you avoid data silos— and organizes them in a way that gives you a completely transparent and full picture of what’s actually happening in your processes.
Notifications: In a frictionless environment, business leaders always know exactly what they need to know, exactly when they need to. Process mining sends notifications to users that alert them to any changes, opportunities or challenges that arise.
Suggestions: AI technology—like that from Celonis—is all about providing finance leaders with actionable insights. Process mining draws from your data to suggest your best possible courses of action to take moving forward with any business improvement initiative.
Automation: Eventually, the insights provided by process mining will lead to suggestions for you to replace specific manual work and touches with automation. This leads to an accelerated finance process with faster decision making times, reduced rework and error, and (typically) enormous amounts of capital saved each year.
Prediction: Over time, process mining establishes patterns in your data, and uses machine learning to predict the outcomes of your actions with pinpoint accuracy.
Prevention: Avoid risk, recalls, maverick buying, and countless other issues before they happen.
Situational Awareness: Ever find yourself wondering, “What do I do right now to improve my business? What can I do?” With tools that help users discover, enhance and monitor their ways to frictionless finance, process mining ensures you’re always perfectly aware of where and when you are throughout every stage of your finance optimization journey.
A: Simply put, process mining turns each and every finance process into an experience. Every insight and action presented by AI is measured by cross-departmental and actionable metrics, and is always tied to revenue or cost. Suddenly, you can see direct links between, for example, your finance and HR departments, finance and marketing, finance and your supply chain, etc.
This data gives you the power to expose, identify and take action in ways that maximize value realization, and accelerate your finance processes by decreasing decision time, eliminating guesswork, and removing friction.
Here’s how it works:
Stage 1: In every finance process—whether it be accounts payable or receivable, employee reimbursement, inventory management, payroll management etc.--every action leaves a digital footprint, which is recorded by your data systems.
Stage 2: AI technology collects these footprints from your systems, and from them creates ‘context’ that lets you know where, when, why, and how each stage of your finance process occurs in detail.
Stage 3: From here, your team can start building personalization of your data models at scale with Anticipatory Analytics (think predictions, suggestions), combined with catalyst opportunities for process acceleration.
Take your accounts receivable, for example. Maybe it’s actually in your best interest to offer discounts to help you rake in cash faster. Or, maybe it’s beneficial for you to delay payments on your accounts payable, so you can manage your cash better. Perhaps it’s time you implemented a spend out program to lock down on your spending costs.
Stage 4: This is when a value exchange takes place—whether it be monetary or non-monetary. This can result in a successful purchase, or a new workflow, or even a united consensus on the best step to take moving forward with a project.
Stage 5: Now we get to truly applying machine learning to your finance processes. Process mining gives your team the ability to personalize the provided data models, making them more intelligent and effective in discovering ways to improve your finance operations over time.
The more intelligent your finance operations are, the more suggestions you will receive for process improvement, and opportunities for automation will present themselves to you.
A: Although process mining has been around for a while in the past year or so the industry has been red hot and we’ve had a lot of inquiries about it.
Companies like that they can get up and running fast because they don’t need to prepare data. All the data they need is in their underlying systems ready to learn from. So my simple answer is, pick a process get started. When executives see your actual process flow vs what the intended path is and how that impacts customer experiences they can immediately see the opportunities that applying AI or automation to finance operations bring, or simply changing business rules for better outcomes.
The point is to see the process mining discovery stage as a starting point. From there you can enhance and monitor processes to meet global corporate goals beyond your department or function. But to win hearts and minds, you need to get started on the road to Frictionless Finance One process at a time.
Southard Jones is Celonis’ VP, Product Marketing. Prior to Celonis, Southard held various executive product and marketing roles at enterprise software companies in the Business Intelligence, Analytics, and Data Science market, including Domino Data Lab, Birst, Right 90, and Siebel Analytics.