SyncHub urges firms to fix fragmented financial data

Businesses should focus on making existing financial data usable across their organisations rather than adding more fintech tools, according to SyncHub. The Auckland-based company argues that fragmented data is limiting real-time financial decision-making.

Many companies still keep financial information across separate banking, payments, accounting and operational systems. That slows reporting and makes reconciliation more difficult. SyncHub also argues that the same fragmentation is affecting how reliably businesses can use artificial intelligence, because incomplete or inconsistent data produces weaker answers.

SyncHub connects software platforms including Xero, HubSpot, Harvest, Lightspeed, Airtable and Employment Hero, then normalises the data into a relational database. The resulting data can be used in reporting tools such as Power BI, Tableau and Excel, or queried through AI tools using natural language.

Ben Liebert, Founder of SyncHub, who built the first version in 2019, said many businesses still treat data access as a reporting issue when it has become a broader operational concern.

"A lot of businesses think the next step is to buy another app," Liebert said. "What we keep seeing is that the bottleneck is much simpler. Financial data is spread across banking, payments, accounting and operational tools, and each system only tells part of the story. If you are trying to make decisions in real time from fragmented data, the answer can look precise without being complete."

He said the problem is not a shortage of software, but that each platform is designed around its own workflow and reporting logic. That means different systems can each provide a valid view of the business while still failing to offer a complete picture when used in isolation.

"A bank account can tell you cash movement. Your accounting system can tell you how transactions are categorised. Your payments platform can show how money was collected. Your operational systems can tell you what actually happened in the business," Liebert said. "Those are all useful views, but none of them on their own is the whole picture."

Data structure

The issue is becoming more pressing as finance teams, operators and founders try to use AI more directly in day-to-day work, according to SyncHub. In its view, businesses need data that is structured, queryable and connected before they can expect reliable answers from AI systems.

Liebert said customer expectations have shifted from periodic reporting to faster cross-system answers that can support action.

"For a long time, businesses accepted manual exports, spreadsheet workarounds and one-off reporting as normal," Liebert said. "What is changing now is the expectation. People want answers across systems, and they want them quickly enough to act on them."

He added that the quality of the underlying data determines whether AI can be trusted in an operational setting.

"AI is only as useful as the data foundation underneath it," Liebert said. "You cannot point a model at scattered SaaS data and expect reliable operational answers. The data has to be structured, queryable and connected in a way the system can reason over."

SyncHub said businesses increasingly want direct answers to questions such as which product sold last month, whether staffing is keeping pace with demand, whether sales have fallen week on week, or whether leave balances and payroll costs could create pressure in the next quarter. Those answers often sit across several systems and require significant manual work before they can be trusted.

According to the company, that demand extends beyond finance departments as business owners and operators look to compare revenue, staffing, sales and inventory data in one place. Some also want to set conditions that trigger alerts when performance changes or operational problems begin to emerge.

Growth path

SyncHub now manages more than two billion records for more than 500 businesses across 30 countries, and Liebert built the first version of the product after customers sought a less costly way to connect software-as-a-service platforms to reporting tools without bespoke development work.

He said the company's work began with dashboard reporting, but customer demand has broadened towards systems that allow businesses to question their own operations more effectively.

"Until that information is structured and queryable in one place, reporting stays too manual and AI is mostly guesswork," Liebert said. "What businesses actually need is a way to ask clear questions across systems, monitor what matters and act on the answer with confidence."

"We started by helping businesses get data into dashboards," Liebert said. "What we are increasingly seeing is that the bigger need is not another report. It is infrastructure that lets a business ask better questions of itself, and trust the answer enough to do something with it."