EuroFinance

As global operations grow more complex, visualising data has become a powerful way to connect the dots, highlight trends, and reveal strategic insights that might otherwise get buried in spreadsheets.

For Sunnie Ho, Head of global cash operations at Atlassian, and Vidhi Jain, Global treasurer at Qualtrics, turning raw data into visual intelligence isn’t just about aesthetics, it’s about control, clarity, and speed. Whether it’s a dashboard that unlocks excess cash, or a manually built model that tracks billing trends end-to-end, the focus is shifting from data collection to data interpretation. And with the right lens, treasurers can finally see—and act on—the full picture.

From execution to strategic insight

At Atlassian, a software company known for its collaborative tools, Sunnie Ho realised the value of turning data into a story that could drive action. “Last year, we increased our invested cash balance by more than 15%,” Ho shared. “This wasn’t by chance; it was the result of a dashboard we built, combined with a short-term cash forecast that helped us time our AP payments more effectively.”

This shift from simply managing cash to making proactive, data-driven decisions represents a transformation in how treasury teams operate. “Treasury is no longer just about execution,” Ho explained. “It’s about asking, ‘Where can we do better? Where are the opportunities we’re missing?’” This mindset has allowed her team to move from just tracking numbers to creating a strategy that shapes the company’s financial direction.

Vidhi Jain’s experience at Qualtrics, an experience management company, was a similar journey but with its own set of challenges. “I remember my first week—five days in, our CFO asked me what the company’s cash balance was. I didn’t have an answer,” Jain recalled.

With operations in over 30 countries, outdated, manual processes made it impossible to answer such questions efficiently. “The real strategic value of analytics isn’t just in summarising exposure. It’s about seeing the full picture, your geographic footprint, your next steps, whether it’s five days or five years down the line, and presenting it as one coherent story.”

Jain and her team quickly learned the importance of not just collecting data but interpreting it in a way that could provide the clarity needed for strategic decisions. Within a month, they implemented manual processes to collect daily cash data, but they soon realised the importance of improving integration to achieve true visibility.

Also read: Taking the pulse of treasury management

The integration challenge

That story, however, is only as good as the data behind it. Both experts candidly shared the struggles of integrating systems across ERP, TMS, and banking platforms.

“There’s no single source of truth,” Jain noted. “We still sit late at month-ends with accounting trying to reconcile data across systems.” While large banks offer strong connectivity, smaller banks—especially those inherited through acquisitions—often lack even basic statement delivery capabilities.

Ho added: “Coming from Microsoft, where everything was consolidated, I was shocked after joining Atlassian,” she said. “We have folders where accounting downloads statements in PDF. Why PDFs?” While Atlassian has implemented TMS and ERP tools, the lack of full integration leaves them with fragmented reports and limited flexibility. “You can’t slice and dice the data as needed. One report gives you one piece of information.”

Chipping away at complexity

Despite these challenges, both teams are finding ways to improve. At Atlassian, Ho has been experimenting with a method she calls “tagging the bank statement.” By assigning transaction categories using simple rules, her team is moving from guesswork to predictive forecasting.

“It’s not quite AI,” she said, “but it’s the first stage of machine learning.” Over time, financial institutions can learn from transaction patterns, reducing the need for human intervention and improving cash flow forecast accuracy—without waiting for every department to submit their projections.

At Qualtrics, analytics also go beyond cash flow. Jain’s team analyses the full billing-to-cash lifecycle, a complex but critical process for a SaaS company. “We look at historical billing trends, revenue growth, and payment behaviors to forecast collections,” she explained. “From there, we tie it to AP payments and forecast final cash positions.” This represents a meaningful step toward end-to-end visibility.

Visualising data for actionable insights

Creating accurate forecasts is only half the battle. The real challenge lies in how treasury teams present this data to decision-makers. “Visualisation is about tailoring the message for your audience,” Ho said. “The CFO doesn’t need to see every single transaction in a bank account. But the treasurer needs to see it all.” At Atlassian, the team uses a combination of tools to create customised dashboards for different decision-makers, ensuring that each individual receives the data most relevant to their role.

Jain’s team at Qualtrics faces a similar challenge in integrating and presenting data across various functions. “We have separate dashboards for daily cash visibility, forecasting, term loans, and counterparty reporting,” Jain explained. However, the team’s ultimate goal is to create a unified platform that draws from a single, reliable source. “Integration and consistency are where we face the real challenge.”

Also read: Derivatives, data and decisions: hedging strategies for FX risk across industries

Building cross-functional partnerships for success

Collaboration across departments is critical to building a comprehensive financial picture. At Qualtrics, Jain emphasises the importance of collaborating with procurement, FP&A, tax, and legal departments. “We have to manage working capital optimally, especially as a private equity-backed company,” she said. “We’ve built a strong cadence of communication with all our stakeholders to align on treasury priorities.”

For Ho, the key has been encouraging company-wide collaboration. “We didn’t even know which entities were consistently running out of cash,” she explained. “We weren’t looking at the big picture. Now, we involve everyone—finance, tax, payroll, AP—everyone plays a role in understanding cash flow.”

Ho also recommends working closely with data scientists to overcome manual inputs and inconsistent formats. “They might not understand the treasury’s business insights, but they excel at extracting and restructuring data. Together, we can build meaningful analysis.”

Harnessing data for predictive action

Looking ahead, both Ho and Jain are optimistic about the future of treasury. While they recognise the ongoing challenges, particularly in system integration and data accuracy, they’re confident that analytics will continue to be a driving force in the evolution of treasury functions.

“There’s a long journey ahead,” Ho said. “But each step we take with data brings us closer to a more strategic, more agile treasury that can predict, recommend, and ultimately drive change.”

As treasury teams increasingly leverage data not just for reporting but for forecasting and decision-making, the role of the treasurer is evolving from a passive guardian of cash to a proactive driver of business strategy.

Also read:

Keeping data at the centre of future proofing businesses 

Building a data driven treasury organisation

What’s your experience?

Has data visualisation helped your treasury function become more strategic?
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