As enterprise tech stacks expand, the risk of integration chaos grows with them. For Anil Madithati, Director of Go-To-Market Systems at Wasabi Technologies, solving that complexity is a strategic imperative. With more than 16 years of experience leading digital transformation across companies like ForgeRock, Airtable, Fivetran, and Samsara, Anil specializes in architecting systems that unify customer, finance, and product data at scale.
From implementing robust Quote-to-Cash automation to building AI-driven observability tools, Anil brings a rare blend of systems thinking, operational rigor, and forward-looking innovation. In this interview, he breaks down the most common mistakes in enterprise integration, how to future-proof your data model, and why governance and real-time data should never be afterthoughts.
Many companies still rely on point-to-point integrations that become difficult to manage as they scale. In your experience, what are the earliest warning signs that an organization’s integration strategy is becoming unsustainable?
Anil Madithati: One early sign is when teams rely on manual workarounds like exporting CSVs, writing scripts, or pinging each other on Slack to fix sync issues. Another big red flag is when simple questions like “What’s the latest status of a customer?” require digging through multiple systems to piece together an answer.
Also, when you see too many point-to-point (P2P) integrations, it’s a warning. It adds complexity fast; every new system creates more connections to manage. That leads to challenges with data sync, exception handling, scaling, and overall data quality. Over time, the system becomes fragile, and changes in one place break things somewhere else.
That’s usually the point when companies realize they need a more unified, scalable integration layer.
When it comes to connecting customer, finance, and product data, what’s the most common architectural mistake you see teams make — and how can they avoid it?
Anil Madithati: A big mistake I’ve seen is teams skipping a proper Common Data Model (CDM). They connect systems like Salesforce, NetSuite, and product usage platforms without agreeing on shared definitions, such as what counts as a customer or how a product is identified. That creates confusion and mismatched reports across teams.
Another common issue is ignoring Master Data Management (MDM). Without a single source of truth for things like customer IDs or SKUs, your dashboards and workflows lose trust quickly.
To avoid this, we use Snowflake as a central data layer and tools like Fivetran to bring data from each system into one place. Then we apply transformation logic to align everything to a unified model. This lets each system do its job while giving the business a clean, consistent view of data they can rely on.
Can you walk us through an example of a successful enterprise integration project you’ve led or observed? What made it work, technically and organizationally?
Anil Madithati: At Fivetran, we integrated Salesforce, NetSuite, and our usage platform to support real-time billing, renewals, and product-led growth workflows.
What made it successful wasn’t just the tools but the collaboration. We got Sales Ops, Finance, and Engineering aligned early. We defined ownership of each data field, created a shared schema in Snowflake, and used Fivetran for clean data ingestion.
We didn’t try to solve everything at once. We launched with core use cases, validated the flow, and then scaled gradually. Having transparent data governance, alerts for sync issues, and a strong feedback loop across teams made all the difference.
How do you approach integration when key platforms (like CRM, ERP, and product databases) all operate on different data models and update cycles?
Anil Madithati: You can’t force everything into one system or expect perfect alignment. What’s worked for me is creating a shared data layer, often in Snowflake, where we bring in source data through tools like Fivetran and normalize it.
Each system continues to operate on its cadence: Salesforce for CRM, NetSuite for finance, and product usage tools running separately, but the Snowflake layer acts as the translation hub. It allows us to align key entities like account IDs, products, and transactions without breaking the native models.
It also gives us a clean handoff point for automation, analytics, and AI agents without tightly coupling the systems,
What role does data governance play in enabling seamless data flow across systems? How early should governance be integrated into an enterprise integration initiative?
Anil Madithati: Governance is often seen as a “later” thing, but I’ve learned it needs to be there from the beginning. As soon as multiple teams rely on the same data, you need to define ownership, naming conventions, and transformation rules.
At ForgeRock, we created a simple data dictionary and assigned field owners before even kicking off the first integration project. That small step avoided a ton of confusion later. It doesn’t need to be heavy, just enough to give teams clarity on how the data flows and who’s responsible.
How do you balance the need for real-time data visibility with the operational risks of tightly coupled systems? Are there cases where “real-time” does more harm than good?
Anil Madithati: Absolutely, real-time isn’t always the correct answer. I’ve seen it cause more harm than good when it leads to cascading failures or sync loops.
The way I approach it is: What’s the decision we’re enabling, and how fast does that decision need to be made? For most business processes, near-real-time (every 15 minutes or even hourly) is more than enough. That gives you reliability without the overhead.
We reserve true real-time for things like usage metering or fraud detection—where timing matters. Everything else can be batched or queued.
Looking ahead, how are trends like event-driven architecture, composable platforms, or AI-driven observability changing the way companies approach enterprise data integration?
Anil Madithati: They’re changing the game. Event-driven architecture helps decouple systems and lets us react to changes faster without constant polling. That’s been a big win for PLG flows and usage-based billing.
Composable platforms give us building blocks rather than monoliths, making it easier to test and scale individual components.
And AI-driven observability is a huge unlock. At Wasabi, we’re experimenting with bots that monitor exception logs and sync failures automatically. They alert us before users even notice a problem. That kind of proactive monitoring helps keep systems healthy and reduces firefighting
