Part 1 of a 2-part series. Read Part 2.
“Your margin is my opportunity.” —Jeff Bezos
Finance executives know that competing effectively in the digital world requires them to become intelligent finance organizations: to harness modern technology that enables innovative business models and supports their ongoing efforts to optimize business processes. In this era of intense global competition, the finance function is not only the organizational steward focused on ensuring regulatory compliance and managing efficient finance operations. The CFO’s team is also a strategic partner analyzing business performance and modeling initiatives to drive profitable growth.
A key challenge for finance digital transformation is creating a prioritized technology roadmap that addresses immediate pain points while maximizing long-term ROI, minimizing throw-away deployments, and balancing the use of consultants with the experiential learning of internal resources. With the pace of technology evolution ever accelerating, progressive CFOs are harnessing new capabilities to generate real-time insights, implementing automation for increased productivity, and integrating heterogeneous enterprise data. The result: reduced total cost of ownership, reconciliations, and debate as to the veracity of various numbers from different systems.
Leading finance functions focused on intelligent decision-making are embedding analytics in the single source of truth. They are simulating scenarios using machine learning and exploring how blockchain can simplify the supply chain. And they are utilizing continuous accounting methods to deliver instant results on any day of the month.
Proactive CFOs are leveraging technology to disrupt their own businesses before they are themselves are disrupted. Digital business finance leaders are capitalizing on five technology megatrends and shaping the finance function of the future. Let’s start with automation and analytics.
Megatrend #1: Automation
Standard robotic process automation can be combined with machine learning to analyze unstructured data and utilize clustering to enable complex scenarios. With the addition of conversational artificial intelligence (CAI), process managers can create, run, and monitor bots without coding. The user can easily translate UI clicks to back-end API calls, freeing up people from mundane and tactical tasks for responsibilities that require human judgment. For example, this “lights-out” operating model in shared-service finance ensures that most accounts receivable and accounts payable processing can be executed with no or minimal intervention. By implementing a policy of managing by exception, investment in intelligent process-automation technologies provides a compelling payback given the scalability and substantial efficiency gains for finance operations.
Megatrend #2: Analytics made simple
The vision for a single source of truth in finance has been ever-present since the beginning of the ERP movement in the 1990s. Yet finance teams are still burdened with multiple systems to access disparate datasets for basic financial analysis. Commonly, the analyst will utilize different interfaces, field names, and process workflows just to complete a simple query. In the case of pulling together a comprehensive analysis for the executive team, the financial analyst not only has to access various systems with vastly different data models and transform the data manually using spreadsheets. The analyst also manually creates charts and graphs, searches for data relationships or patterns that are not necessarily intuitive, and pulls together a PowerPoint presentation or formatted spreadsheet with perhaps rushed but hopefully incisive commentary as to the current results and variance analysis. This process is repeated for each ad-hoc strategic analysis performed throughout the month.
When finance teams articulate their salient pain points, a common refrain is that they spend too much time gathering and harmonizing disparate data and not enough time analyzing business performance. With business intelligence and advanced analytics now merged with financial planning capabilities, these platforms provide a one-stop shop for scenario modeling, visualization, and predictive forecasting. These modern financial planning and analysis systems have also made data aggregation, wrangling, and scenario-planning easy. These technologies instantly deliver insights, eliminating the need to call IT to move or transform the data.
Connecting to multiple data sources is straightforward. With just a few clicks, the analyst has a single view of the business across HR, financials, supply chain, and R&D. As needed, the business user can clean and model the data, change assumptions in the plan, and dynamically build interactive visual stories. The reporting objective might be variance analysis or modeling a redesign of the organization, or perhaps simulating the synergies of an acquisition. In any case, the analyst is now using one tool with one interface that consumes all relevant data, leverages machine learning for making predictions, and delivers compelling visualizations that would have required specialized software just a few years ago.
The self-service movement has democratized the data and enables more business leaders and team members to directly engage with the data. From sales projections to working-capital analytics and operating-expense forecasting, machine learning can not only automate planning but improve accuracy far beyond what an analyst could perform manually on spreadsheets, running regressions and searching for data relationships. By augmenting the crowdsourced human cognition with AI, finance can accelerate decision-making by performing unlimited simulations, determining best actions, and eliminating (or at least minimizing) human bias. Machine learning even examines the data and runs discovery to serve up what the user will find interesting.
No data science degree is required; business analysts can utilize these modern applications to improve insight, shorten cycle time, and dramatically increase value-add – not to mention job satisfaction – by spending their valuable time on analytics and not data management.
In Part 2 of this series, we’ll explore three more megatrends that are already making a huge impact.
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