How to implement self-service data analytics for finance teams

No longer is it enough for finance teams to report financial results and streamline processes; in fact, 85% of an ESG survey respondents believe it is imperative for the finance organization to transform from reporting on “what” is happening in the business to “why” things are happening. Finance leaders are expected to answer new questions from executives every day, and to guide business strategy. It is therefore not surprising that according to Gartner, the #1 priority of CFOs is advanced data analytics technologies.

While Deloitte notes that “CFOs are in a unique position to become the chief analytics officers as finance gains a bigger influence in driving the company’s strategy”, CFOs do spend more time sifting through spreadsheets than doing anything else—an average of 2.24 hours per day. Finance teams face significant challenges combining ever growing data sets across different formats and sources into a single source of truth to provide actionable insights to other departments.

For IT teams, those needs translate into an increasing number of complex, time consuming demands from financial analysts.

How can IT enable finance teams to get the deep, trustworthy, data-driven insights they need to make quick decisions—while ensuring data governance, security, and saving significant time and efforts?

Real-time insights to drive innovation and growth while reducing costs

Let’s consider the following customer stories highlighting how, with Oracle’s solution, finance teams have been able to dramatically improve their data analytics processes while significantly reducing costs.

Dou Yue’s 30 restaurants across China are committed to serving traditional Chinese cuisine on premises and for takeout. Multiple isolated legacy data platforms prevented the company from gaining the comprehensive, real-time insights it needed. If Dou Yue executives wanted to get a view of the entire business, they had to export spreadsheets from various systems to then manually aggregate and calculate results, a process that was outdated and error prone.

By deploying Oracle Autonomous Data Warehouse and Oracle Analytics Cloud on Oracle Cloud Infrastructure, Dou Yue integrated the data from multiple business systems onto a single, cloud-based platform. The financial team can now pull revenue, inventory, and other data—by restaurant—for company executives to act on. For example, when a restaurant posts a revenue decline, Dou Yue executives can analyze the environmental conditions (traffic, weather), sales model (dine-in or takeout), as well as menu and pricing in the region where the restaurant is located to determine the root causes and adjust strategy. By analyzing historical trends, repeat-consumption, and other data, Dou Yue is now able to understand which dishes customers like and adjust them—or create new ones—in a timely manner. They can also determine which commercial buildings tend to order the most take-out to inform targeted marketing campaigns and where to locate future restaurants. In the past, the company required two or three full-time staffers to spend a few hours to manually produce reports. With Oracle’s solution, reports are generated with a simple click, dramatically improving business decision-making while reducing labor costs.

At Data Intensity, the finance team was spending 60% of the time just getting the data out of the systems. With Autonomous Data Warehouse, they can now run 200 reports in seconds. A testament to the value of the solution, 10X more users are now accessing the system, driving value. Additionally, the company reduced costs by 30%. “Our CFO is delighted, he could do what he could not do before,sit in a board meeting and get the data then and there at his fingertips.” said James Anthony, CTO.

Lyft, the transportation network, was busy reimagining the future of transportation. Behind the scenes though, the company had gone from a high-growth start-up to a publicly traded enterprise processing billions of transactions a year—and its finance systems hadn’t kept up. Lyft turned to Oracle for its integrated Oracle Fusion applications and data analytics solution. Jay Weiland, Director of Financial Solutions at Lyft said “When I have a process running in the middle of the night, I can’t tell you the exact minute it’s going to stop. With the autoscaler in Autonomous Data Warehouse, it scales the number of CPUs automatically, so I don’t have to pay for idle hours. That’s very attractive to my finance team.”

A complete, self-service data analytics solution

Oracle delivers a complete, self-service data analytics solution empowering finance teams to rapidly get the deep, trustworthy, data-driven insights they need to make quick decisions.

The architecture of the solution is represented below:

Data from all sources and formats can be combined in Autonomous Data Warehouse to drive secure collaboration around a single source of truth. Autonomous Data Warehouse intelligently automates provisioning, configuring, securing, patching, backing up, performance tuning, and repairing of a data warehouse. This reduces administration effort by up to 90%, enabling finance teams to operate independently while freeing up valuable resources for IT teams.

It is the only cloud data warehouse that is autonomous, self-service, and complete, providing finance teams with a comprehensive suite of built-in tools:

◉ Data tools enable self-service drag-and-drop data loading, data transformation, and business modelling. Financial analysts can automatically discover insights with machine learning algorithms—no coding required—saving them significant time and efforts.

◉ Built-in graph analytics enables financial analysts to visualize relationships and connections between data entities. They can for example instantly see all costs and headcount associated to a given project, or understand all dependencies associated to a given supplier to best manage supplier relationships.

◉ With built-in spatial analytics, they can rapidly answer financial questions such as “where did bad weather impact revenue?”, or “where are our most profitable customers?”

◉ Financial analysts can build machine learning models—with a no code interface—to predict likely financial outcomes, e.g. customers likely to default on payment, transactions likely to be fraudulent, expected revenue based on forecast and historical patterns…etc

◉ With the built-in Oracle APEX low-code development platform, finance teams can quickly develop applications for ad hoc needs and gaps/processes handled outside of their ERP—without having to join a queue of IT projects. Such applications can include ad hoc data rooms for acquisitions, tracking the progress of digital transformation initiatives, or COVID-19 related applications.

 

“It's like the iOS of the enterprise cloud data warehouse space.” 

Patrick Moorhead - Founder, President, & Principal Analyst at Moor Insights & Strategy

Oracle Analytics Cloud is connected to Autonomous Data Warehouse, empowering business users and executives with modern, AI-powered, self-service analytics capabilities for data preparation, visualization, enterprise reporting, augmented analysis, and natural language processing/generation. Alternatively, Autonomous Data Warehouse is certified with all popular analytics tools inlcuding Tableau, Looker, and Microsoft Power BI, ensuring freedom of choice for customers.

The governed, secure solution allows IT teams to reduce risks. They can additionally rely on a simple, reliable, and repeatable approach for all data analytics requests from finance teams.

Beyond their core financial responsibilities, finance leaders are in a unique position to guide business strategy and help other departments achieve their goals. Having the ability to rapidly and independently turn a growing mountain of data into insights is essential to achieve these objectives. With Oracle’s complete, self-service data analytics solution powered by Autonomous Data Warehouse, IT can enable finance teams to efficiently take on the leadership role that is increasingly expected of them—while reducing risks, costs, and increasing both IT and analysts' productivity.

Source: oracle.com