Financial Analytics as a Strategic Business Asset

Financial Analytics as a Strategic Business Asset

Reading time: 9 mins

By Brianna Shipley, Director, Editorial, SAPinsider

A desire to experiment is compelling finance professionals to create new forecasting methods, share value-based insights with other lines of business (LoB), and enable alignment, agility, and modernization across systems. Finance leaders have discovered an opportunity to accelerate digital transformation and accomplish resilient growth initiatives. A curiosity drives them to learn how automation, artificial intelligence (AI), and machine learning can elevate the finance function and with it, the company’s entire analytics strategy. At the heart of this desire is data.

Creating value from data, however, remains a barrier for many organizations. Accenture found that only 32% of companies reported being able to realize tangible and measurable value from their data. These results highlight room for improvement. How can businesses — and the finance department in particular — progress in this area?

Amy McNee, Head of WW Analytics Platform Strategy, Specialized Sales at Amazon Web Services, believes that implementing a modern strategy by moving to the cloud is foundational to accelerate change in data value. “Modernizing your platform changes the entire calculus of how quickly and easily you can deploy and align with business outcomes. And it also allows us to engage business stakeholders with data in a way that we’ve never done before,” she says. “Hardware, software, and data center space are no longer boundaries to innovation with data.”

Mayank Sinha, Senior Director Solutions & Value Engineering at Qlik, suggests that the need for cloud computing, along with the need to democratize data at scale, is being recognized by SAP customers around the globe at an accelerated rate. “Today’s workforce contains a lot of remote users who need accurate and consistent data so everyone within the organization can work from a single source of truth,” he says. Sinha confirms that SAP customers are in luck: “Many of the modern data analytics solutions and strategies available on the market today, including those provided by Qlik, are complementary to SAP customers’ modernization strategies.”

This article will explore how modernization and financial analytics can work together to help the finance department deliver value to the business in new ways and what’s required to unlock opportunities in this space.

Integrate SAP and Non-SAP Data: Opportunities in Forecasting and Customer Experience

Sinha suggests that one of the keys to business acceleration is being able to quickly and easily access all data, “and not just SAP data, but SAP data combined with data across the enterprise, such as e-commerce or other Internet of Things (IoT) devices.” Many businesses struggle to gain a complete view of their order-to-cash process, for example, which is complex and spans across many functions, teams, and systems.

Imagine if inventory, shipping, and accounts receivable data — which often sit in an SAP ERP system — could be combined with customer data from a customer relationship management (CRM) system, with employee data from HR systems, or customer invoice data from third-party billing systems?

By integrating data from multiple systems and sources that support the order-to-cash process, the business can gain a clear view of where to focus, whether it’s booking or fulfilling orders faster, or receiving customer payments sooner. Combined with modern analytics capabilities like intelligent alerts, businesses not only have new insights but up-to-date insights on a timely basis.

With that knowledge coming from the finance function, the company could improve on key measures like days sales outstanding and potentially unlock significant cash, which might be sitting in working capital. Sinha points SAP customers to Qlik’s order-to-cash solution accelerators for help in this area. Qlik has also made available solution accelerators for multiple business processes to help SAP customers jump start their data modernization and data analytics journeys.

McNee explains that implementing a modernized platform frees companies from being limited to receiving this information on a monthly or quarterly basis and can help enable a rolling forecasts model and continuous planning approach.

For example, a vendor at a baseball game could stream weather data or the score of the game and compare that to past trends and behaviors to predict whether fans will be leaving during the fifth inning. Based on those insights, the hot dog vendor might then decide about whether it should produce more or less food. “People have talked about rolling forecasts for a long time, but this is a rolling forecast in the moment that matters with immediate insight and results that impact the bottom line,” McNee says.

Many companies have rich sources of content stored in unstructured repositories, such as presentation slides or spreadsheets. Finance professionals are starting to use machine learning technology to extract insights from these types of documents, which are often the most prevalent assets for the business, and contemplate ways to monetize them to drive new revenue streams within the organization.

Consider how this scenario could benefit a company in the insurance industry. The finance department may use machine learning to unlock custom terms in its policy documents. “By stripping those terms out, they can be treated like attributes that the company can analyze to understand whether the terms are resulting in a cost to the company or an improvement in revenue,” McNee says. What the cloud enables is the ability to experiment quickly to determine if what you expect to drive value, actually does drive value.

This strategy is also being driven by an emerging business trend: achieving a 360-degree customer view across product lines. By combining financial data with other unstructured sources of information, a claims adjuster can meet with a strategic customer with all the necessary real-time information to have a productive conversation.

This is just one example of how world-class finance organizations are more transparent in communicating their data, according to Paul Gilman, Senior Manager Global Analytics Platform Strategy, Specialized Sales at Amazon Web Services. “In the past, that was usually siloed, but because the financial information in data and analytics is so important across the organization, you’re starting to see finance become a lot more transparent and communicate what they have. This is really driving the vision of creating a data community or a data marketplace, where at the end of the day, the office of finance can be both producers and consumers,” Gilman says.

Help Accelerate Innovation

The position of finance within the enterprise is being shaped by a need to accelerate business innovation. The most strategic finance leaders are partners in this. Enabled by growing volumes of data, one emerging priority is gaining efficiency in digitizing business models or developing new products and services. Finance has to build financial models to forecast and evaluate the business case for new products or services. “As a result, Qlik is seeing a lot of momentum around SAP test data management. For many, there is a huge risk management and efficiency value here,” Sinha says.

He recommends that SAP customers explore Qlik’s data integration automation and test data management solutions to help them leverage SAP data from production environments, obfuscate as required, and securely deliver it into test or development environments where innovation of new products or services can be prototyped faster, financially unviable options can be more quickly eliminated, and the best ones prioritized.

Simplifying the data environment can also help companies gain a single source of truth and become more efficient around spend.

For example, consider a tier-one automotive supplier that needed to modernize and consolidate its seven SAP instances (including SAP ECC and SAP S/4HANA) into a single SAP S/4HANA instance. The company employs approximately 27,000 employees and produces $10 billion in sales across more than 60 locations around the world. The organization was facing a lot of global supply chain disruptions and pressure on its gross profits.

Leveraging some of Qlik’s solutions, including a data analytics platform, Qlik Sense, and Qlik’s SAP test data management solutions, the company has saved over $8 million in the first year. Accelerating SAP modernization enabled employees to focus on financial analytics use cases like better demand forecasting and planning, as well as cost reduction around spend on direct materials, all of which directly impacted the cost of goods sold and gross margins.

Communicate Signals in Business Terms: Why Data Literacy Is a Competitive Advantage

Implementing a platform that can support data analytics is necessary for businesses that want to use information to their advantage. However, according to research conducted by Qlik, only 24% of business decision makers surveyed are fully confident in their ability to read, work with, analyze, and argue with their data.

“Data literacy — which is the ability to read, work with, analyze, and argue with data — is a must-have skill for the 21st century, but there’s a huge gap and opportunity there,” Sinha says. “SAP customers are investing in this skill set across the entire organization so they can achieve a data-driven culture.”

Accurately translating data into business terms becomes particularly important as the stakes grow higher for interpreting a chart or table accurately. “As businesses enter the recovery phase following business disruption, it’s essential that signals and risks to the business be identified. A lot has changed regarding how we do business with the impact of virtual workforces — we need to leverage technology to help us find those patterns and new signals that we didn’t necessarily have before,” McNee says.

Adopting a conversational style of analytics is one example of how businesses are moving away from the drag-and-drop style of translating insights to a description that’s clear to leadership and allows them to ask follow-up questions — What are my top 10 revenue generators? What is my cost per region? What are my top 10 costs?

Utilizing AI and machine learning capabilities, finance professionals can quickly derive this information and make it available to constituents in the organization. “You can no longer have just a small number of people who are able to interpret data for everyone else if you want to use data as a competitive advantage, and this is particularly true in the office of finance,” McNee says. “Businesses are evolving in this area and that evolution is what’s driving data literacy deep into the organization to achieve not just analytics but interpreted analytics.”

This evolution in the way analytics are derived and communicated, says Gilman, presents the classic three-legged stool, where people, process, and technology are considered enablers of innovation. The ability to use machine learning capabilities and advanced analytics is empowering the office of finance to uplevel skills and become more predictive. Businesses that are using cloud technologies to support their processes are able to innovate and experiment at a reduced cost.

“The office of finance not only has confidence in its data but, more importantly, is spreading that confidence to other LoBs, achieving a trusted single source of truth and fostering collaboration throughout the organization,” Gilman says. These companies are best positioned to realize world-class objectives.

Amazon Web Services is seeing financial information being integrated across LoBs like sales and marketing “and we’re seeing the chief financial officer (CFO) catalyze change and adopt a more strategic approach to providing data than they have in the past,” McNee says.

Looking Forward

The traditional rear-view perspective that the finance function provided in the past is too static to sustain business success in today’s dynamic economy. Finance professionals require new skills and tools in order to equip the business with accurate and real-time information that can drive decision-making, respond to disruption, and become more predictive in nature.

Gilman challenges businesses to think about how they will bring in the next generation of finance professionals and empower current workforces with new tools and skills. “The incoming generation is driven by the ability to experiment and innovate — how will you ensure they have access to the data they need to move the business forward?”

According to Sinha, “the best finance leaders are using financial data and analytics to drive business transformation, monetize data for growth, or provide financial governance in the new digital economy.”

In this new world, McNee says, “we’re not bound by the same things we were before.”

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