With every passing day, digital transformation is increasing in importance. With the development of new data science technologies, access to even more business data, and cheaper cloud infrastructure, achieving complete digital dominance has become a realistic aim for the finance function.

CFOs have access to the sales, supply chain, demand, performance, markets, and industry data required to generate forecasts and create business strategies.

There are many different niche areas that CFOs can choose to focus on, such as regulatory reporting or treasury, in their digital transformation endeavors. However, this whitepaper will detail a three-step definitive guide that will have the biggest impact on the finance function.

Step 1: Automation and Robotics

While some finance functions such as business development, external relations, and auditing will never be fully automated, there are other areas that can be majorly automated, such as accounting, cash disbursement, revenue management, financial controls and reporting, and F&A. This would increase efficiency and free up resources to focus other high-value areas.

An important technology that is becoming very popular in the world of finance is robotic process automation (RPA). This type of automation performs repetitive, redundant tasks at set intervals while ensuring timeliness, accuracy, and efficiency. IT can be used not only in discrete siloed business activities, but across functions.

RPA has the potential to not only help companies remain profitable and maintain a competitive advantage, but also build connections between IT, innovation, and business operations, and can be an effective entry point to enable the adoption of further technologies. Due to an increase in the number of reporting systems, globalization, and changes in regulatory requirements, finance teams are facing greater complexity. Furthermore, stakeholders have high expectations from the business, demanding better margins, business growth, and strategic contributions from the operations and finance departments. RPA is the right solution to address these challenges as it can help streamline processes to effectively respond to those external regulatory and compliance requirements, be a highly effective tool to control costs, and increase organizational efficiency. And, with organizations laying greater emphasis on business changes with a healthy return on investment, RPA delivers just that.

To implement RPA at the finance level, however, the operating model would need to be altered and the processes rethought. Furthermore, finance teams will have to be trained on RPA techniques so that they would no longer rely on IT.

This step is essential for achieving real outcomes. To accomplish this, the method of implementation would need to be interpreted in depth and width. These procedures operate with each other, as well as individuals like suppliers and associates from outside the organization. Having an isolated RPA mindset will inhibit scaling up. RPA is not substituting one ingredient for another. It is part of a shift to a whole new recipe.

This mindset will not only help identify more opportunities but also identify ones with the highest ROI.

Scaling up requires a formal framework and model of operation, centralized control, powerful governance, approved use cases and a long-term road map. It includes rolling in smaller projects into a wider program.

Step 2: Real Time Data Visualization

To make good resource-allocation decisions, teams need real-time financial information. They often lack access to such data because stores of data are in different parts of a company, data formats are not comparable, or data are not available at all.

Some finance groups are pairing automation capabilities with data visualization technologies to create clear, timely, actionable business reports. These reports push data to end users and present data in intuitive formats that encourage focused business discussions.

Data visualization helps finance communicate analytics insights to the broader organization. Consider that studies show that 65% of people are visual learners. Providing decision makers with visual illustrations of data increases understanding and can lead to better decisions.

The actual practice of creating data visualizations can help finance identify additional trends or find even deeper insights – especially when using multiple data sources or interactive features. For instance, modern finance leaders are now often tracking financial and non-financial KPIs. Data visualization can help correlate these metrics, reveal connections, and clarify actions needed to improve performance.

As volumes of Big Data grow and more organizations deal with complex data sets with scores of variables, data visualization will become even more important. The world is only becoming more complex, and organizations that can adapt will be the ones most likely to prosper. It is not about being perfect; it is all about being better than the competition.

To create effective visuals, their usage and purpose needs to be defined. A couple of important questions to ask about the data include whether the information is conceptual or data-driven (i.e. does it rely on qualitative or quantitative data). Then, address whether the purpose is declarative or exploratory. For example, if one wants to show the previous year’s sales, then the purpose is declarative. If, instead one wants to determine whether the increase in sales coincides with the spending on digital ads, then the purpose is exploratory. Determining these answers will help inform the types of tools and formats that will be required.

It is important to keep the audience in mind when designing the visuals. The level of detail that the data visualizations demand will depend on who is viewing them. For example, finance data presentations for the C-suite need high-level, highly relevant information that help leaders make strategic decisions. However, if presenting to line of business leaders, then digging into the finer details can provide them with information that impacts their daily operations.

Step 3: Advanced Analytics

Companies in all industries are now experimenting with advanced analytics, mining troves of business data on people, profits, processes, and other areas to find relevant insights that can improve tactical decision making. Similarly, the CFO and the finance function can use advanced analytics to manage standard financial transactions and core processes more efficiently to shape and accelerate tactical discussions. These decisions can include whether to optimize pricing, identify customer churn, prevent fraud, manage talent, or explore a host of other applications.

From an enterprise perspective, CFOs have a leading role to play in aligning investment in advanced analytics with business strategy by understanding how technologydriven disruption may impact their organizations. They must also form strategic, operational, and financial plans to respond, and establish disciplined, rigorous frameworks to align investment in advanced analytics to those plans, and measure and manage effectiveness.

From a functional perspective, increasing the value of finance’s contribution will take using advanced analytics to detect patterns that substantially impact business performance, improve forward looking forecasts and the ability to respond to these predictions, answer new kinds of questions, and test imaginative new scenarios.

In practice, predictive analytics systems can help finance teams identify the potential for performance shortfalls and engage business partners to take efficient, targeted actions to remedy them well before they impact financial statements. For example, a manufacturer could use analytics to predict shortfalls in receivables performance by measuring days sales outstanding metrics (DSO), and target specific accounts for collections based on factors geared towards improving financial performance. Rather than relying on conventional methods to prioritize accounts for collection, such as days past due or the size of the account, the company could prioritize collections based on factors signaling the likelihood to pay within a given time frame. The system would allow senior management to drill down on targeted accounts, identify the sales representatives associated with them, and automatically notify reps that their help with collections may be required. By using analytics to refine their collections targeting, the company could improve its DSO performance, and minimize the amount of sales time and effort required for collections.

While digital transformation can benefit the finance function, there are still be issues that technology cannot address, such as poorly managed data or long-term strategies requiring human judgement. Still, it is clear that the pros of digital transformation far outweigh the cons. The time is ripe for CFOs to develop and share their vision for a digitally transformed finance function.


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