ORLANDO, Fla. — A business dashboard can look healthy and still be wrong.
That is the kind of problem many companies face as they rely on more apps, cloud platforms and distributed systems to run daily operations. A service may be online, alerts may show green, and teams may believe everything is working until two reports show different revenue totals, customer records do not match or a transaction appears where it should not.
That gap between a system being available and a system being accurate is the focus of Bhavna Hirani’s new book, The Architecture of Truth: Engineering Resilient Transactional Integrity in Global Distributed Systems.
“Most teams optimize for uptime because uptime is easy to measure,” Hirani said. “You ping the service, you get a 200, you call it healthy. Healthy and correct are different problems, and the gap between them is where most of the real money quietly gets lost.”
Hirani is a software engineering leader with more than 12 years of experience scaling enterprise systems across fintech, IoT, consumer services and media streaming. She currently works as a software development manager at Salesforce.
Her work focuses on a problem that has become harder for companies to ignore: as systems grow larger and more connected, it becomes more difficult to know whether business data is accurate across every platform.
That matters because incorrect data can create real costs. A duplicate charge, mismatched revenue figure, inaccurate inventory count or broken customer record may not look like a system outage. But for a business, those errors can affect trust, reporting, operations and customer experience.
Hirani said many engineering teams have strong tools for knowing when a service is down, but fewer tools for knowing when a service is giving the wrong answer.
“The hardest part of building a distributed system isn’t making it scale,” Hirani said. “It is making sure that when something goes wrong, and something always goes wrong, the system still tells you the truth about what happened.”
The issue is becoming more important as companies prepare for wider use of artificial intelligence. AI tools depend on accurate, connected and trustworthy data. If the underlying systems are fragmented or inconsistent, businesses may struggle to use AI effectively.
Hirani said her experience working on large-scale systems in fintech, commerce and consumer technology showed her the same issue in different ways. As business systems grow, keeping data accurate becomes just as important as keeping systems online.
Hirani said the next challenge for many companies is not simply keeping systems running. It is making sure those systems can be trusted.
As more of the global economy depends on distributed software systems, data accuracy is becoming a business issue, not just an engineering concern. For companies handling payments, customer records, inventory, streaming systems or commerce platforms, the difference between “working” and “correct” can carry real financial consequences.
As companies continue to connect more platforms and rely on AI-driven tools, Hirani argues that systems cannot just stay online. They have to tell the truth.
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