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As a finance professional at Microsoft, Thiery Uribe works with a significant amount of data to ensure that the company’s payments align with the contracts made with suppliers, all while bringing the best economic value for the company. To identify trends, he looks through millions of rows of supplier payment data from multiple sources and tools.
Developing rich insights requires a strong foundation of quality, discoverable data about cash flow, and no tool offered this.
“I knew how to access data, so I spent a lot of time pulling data for myself and others,” says Uribe, a senior global process manager at Microsoft. “We needed a single source of data so we could focus on driving insights.”
Uribe’s role requires a deep understanding of Microsoft’s cash flow, which measures the company’s influx and outflux of money. This includes cash that comes in from customers and clients who buy the company’s products and services, and the cash that goes out in the form of payments or expenses.
In 2018, a directive came from Amy Hood, the executive vice president and chief financial officer at Microsoft. She wanted to build the company’s muscle around cash flow so finance professionals across business groups like Cloud and AI, Devices, Office, and Windows could improve cash flow management.
Hood called on the Microsoft Finance team to lead the initiative to support finance professionals in understanding Microsoft’s cash flow at a much deeper level than ever before.
“Microsoft is one of the most valuable companies in the world, which makes it a powerful force in the economy,” says Nimit Phutirat, the finance director on the Corporate Financial Planning and Analysis team at Microsoft. “Cash flow is a key metric that external investors use to evaluate Microsoft’s performance.”
Teams across finance operations, procurement, engineering, and other business groups worked together to develop an intelligent analytics platform that centralizes and provides finance professionals with visualizations of cash flow data.
“Whether we are in finance or business, we benefit from staying up-to-date with technology and learning how to leverage it,” Phutirat says. “As finance professionals, we have to elevate our roles and move up the value chain. This starts by creating a collective understanding of cash flow and making cash flow data connected and available to key decision-makers.”
In partnership with Microsoft Core Services Engineering and Operations, Microsoft’s IT and Operations division, the team developed an interactive dashboard that uses Microsoft Power BI and Microsoft Excel for front-end visualization. The back-end architecture pulls together data from multiple sources, creates models, and derives insights using Microsoft Azure.
“No matter how we pulled data in the past, it was siloed in different spreadsheets and sources,” Phutirat says. “With modern technology like Power BI and Microsoft Azure, we’re able to connect multiple data points and generate insights at the press of a button.”
With this solution, finance professionals can access cash flow analytics and machine learning models. Finance professionals like Uribe can use the dashboard to drill down and answer questions about the team, product, payment type, and geographic location associated with invoices.
“The interactive Power BI dashboard has democratized data within the team and created a greater understanding of what cash flow is,” Uribe says. “As finance professionals, we have the tools we need and the visibility into this data.”
[Learn how Microsoft used Microsoft Azure to transform our cash flow business. Read about how Microsoft uses AI and chatbots to simplify finance tools at Microsoft. Check out how Microsoft created efficiencies in finance with Dynamics 365 and machine learning.]
Democratizing the use of data
Microsoft spends 60 billion dollars annually and deals with approximately 35,000 unique suppliers, and often uses cash flow as part of the negotiation process. For example, Russell Maw, director of procurement services, can help negotiate a discount if Microsoft pays the supplier early or changes the length of the term.
Maw ensures that suppliers adhere to supplier payment terms, and the analytics platform has provided more visibility into anticipated payment terms and the terms on the final invoice.
In one instance, the supplier master terms suggested that Microsoft’s payment was due in 60 days. However, the company had paid the supplier in 30 days. Using the Power BI dashboard, Maw and Uribe discovered why the invoice was paid 30 days earlier than the agreed-upon date.
“Because we had visibility into the actual invoices, we could update our systems to ensure that the supplier had the right terms,” Maw says. “That 30-day difference equated to almost 7.5 million dollars of economic benefit for Microsoft.”
Maw has worked on supplier payment terms for almost seven years, and his team typically set a goal for the amount of adherence to standard payment terms. In most instances, Microsoft receives a 2 percent discount if the invoice is paid within 10 days. Otherwise, the entire invoice is paid in 60 days.
With increased visibility into Microsoft’s cash flow, he and his colleagues better understand the role of cash flow in Microsoft’s adherence to supplier payments.
“Using the visibility provided by the Power BI dashboard, we expect to exceed our target by 13 percent, which equates to tens of millions of dollars of additional economic benefit,” Maw says.
Using machine learning to inform decision-making
Phutirat says that forecasting is a critical part of a finance professional’s role, which is supported by the machine learning (ML) capabilities of the analytics platform using features in Microsoft Azure, such as Microsoft Azure Data Lake Storage and Microsoft Azure Machine Learning.
Phutirat noted that the analytics dashboard’s predictive analytics and ML models have improved each quarter, and more finance professionals are using it to drive decision-making.
“We are at the beginning of the journey of how we will leverage ML,” Phutirat says. “In the future, we envision using machine learning at an operational level to predict things like a customer’s likelihood to pay on time.”
Today, it’s about blending human intuition with ML insights to make an informed decision. Maw says he uses knowledge of cash flow with the ML-fueled suggestions he gets from the analytics dashboard.
“ML might be a good forecasting tool in some instances, but it might need to be adjusted in some cases,” Phutirat says. “For example, people can’t travel right now due to COVID-19—ML couldn’t account for that when we forecasted traveling expenses because of how unpredictable they were.”
Drawing on the knowledge of the collective
When using large datasets that stem from multiple sources, Uribe says that the key to success is effective partnerships.
“There’s no one person at the company who understands how cash flow works from end to end,” Uribe says. “To fill in the gaps, we work with stakeholders across finance, marketing, and other business functions to understand the trends we were seeing in our data and use it to inform our actions.”
Since the launch of the analytics dashboard, Uribe, Phutirat, and Maw regularly meet with employees from each business group to discuss ways to improve the dashboard. By drawing on collective knowledge and feedback from business groups, the finance team could separate the signal from the noise.
“Invoice payment terms don’t tell the whole story,” Uribe says. “For example, a payment term may show up as zero days, but it’s not until you reach out to that business groups that you understand why. It may have offered a competitive advantage. The key is to focus on actionable insights.”
Ultimately, the development of this analytics dashboard reflects an evolution in the role of finance professionals. If you’re beginning the journey to use data and AI for your work, Maw says that it’s best to start small.
“You will never figure out what to look at first or second until you start,” Maw says. “We started with a concept, and we’re always evolving our usage and how we display data. To me, you just have to jump in and do it.”
Phutirat knows that Microsoft’s finance professionals will continue to use the dashboard to add value to their organization and the company at large.
“Now, instead of pulling data, we can focus on driving business impact,” Phutirat says. “We love that this solution empowers us to transform how we work.”
Learn how Microsoft used Microsoft Azure to transform our cash flow business.
Read about how Microsoft uses AI and chatbots to simplify finance tools at Microsoft.
Check out how Microsoft created efficiencies in finance with Dynamics 365 and machine learning.
