When the world turned to Zoom, the company scaled fast — and so did its content chaos.
Support information, product documentation, and marketing assets were all growing exponentially, but they were spread across disconnected systems. Customers hit dead ends. Search queries returned nothing useful. And internal teams were forced to manage the sprawl with point solutions that weren’t built to scale.
Zoom’s challenge wasn’t unique. It’s what many enterprises face today: data lives everywhere — but rarely where it needs to be.
“We’re with our customers 24/7,” said Jeff Harling, Head of Digital Support at Zoom. “So as you can imagine, those customers are looking for a responsiveness from Zoom that is second to none.”
And that’s where enterprise data integration becomes mission-critical.
Because without a unified foundation that connects systems, content, and context, you can’t power the experiences your customers expect: AI-driven answers, personalized journeys, fast self-service.
In this post, we’ll break down how forward-thinking companies like Zoom are reimagining enterprise data integration — not simply as a backend IT task, but as a strategic enabler of relevance, scalability, and operational agility.
What Is Enterprise Data Integration?
Enterprise data integration is the process of unifying structured and unstructured data from across multiple business systems into a single, accessible environment. It makes information spread across CRMs, ERPs, support platforms, websites, CMSs, and commerce tools more accessible, as well as support consistent, intelligent digital experiences.
For enterprises like Zoom, each of those point solutions was chosen for a specific purpose, yet were not evaluated for how well they’d perform in the future. Though they solved individual challenges, they contributed to inconsistent user journeys, inefficient search experiences, and rising case volumes.
Relevant reading: How The Silo Problem Is Killing Your Customer Service Experience
But fixing this rat king-type challenge isn’t easy. Traditional approaches to data integration, such as ETL pipelines or point-to-point connectors, often rely on data consolidation that requires physically moving data into centralized storage like a data warehouse, database, or data lake.
These methods are rigid, hard to scale, and prone to breaking as systems evolve. In today’s fast-moving digital landscape, they’re no longer enough. As Forbes recently noted, enterprises are increasingly pushing for stronger, more flexible connections between their data and systems.
Rather than centralizing everything in one database, modern enterprise data integration focuses on building a unified foundation that spans departments, platforms, and use cases. The goal is creating seamless access across systems; aka, effective information retrieval.
When done right, it powers capabilities like relevance-based search, AI-driven recommendations, and context-aware generative experiences.
This is how platforms like Coveo’s work: they don’t require data to be moved from its original repository. Instead, with connectors and a unified index, Coveo enables centralized data access without duplication — while preserving the structure, permissions, and security policies of each data source.
Why Data Integration Matters for Modern Enterprises
Digital transformation has made one thing clear: the success of your customer and employee experiences depends not only on the quality of your data, but also on how easily it can be accessed and used. Without enterprise integration, every function that relies on accurate, accessible information begins to suffer.
When enterprise data isn’t integrated, it doesn’t just stay hidden. It causes friction across the entire business.
For customers, that friction shows up as unnecessary effort and frustration. Answers that should be easy to find — like order status, product details, or troubleshooting steps — remain buried across siloed systems. Instead of resolving issues through self-service, customers are forced to call in, increasing support costs and wait times.
Many won’t bother at all. According to Coveo’s 2025 CX Relevance Report, over half of consumers say they’ve abandoned a brand simply because they couldn’t find the information they needed.
The stakes are just as high internally. Employees waste valuable time toggling between systems, searching for the right version of a document or answer.
Our 2025 EX Relevance Report found that the biggest hurdle to an employee’s productivity is that information is stored across numerous repositories, meaning they spend more time searching for things they need rather than doing impactful work. Without fast, centralized access to knowledge, even the most capable teams struggle to deliver.
Enterprise integration bridges these gaps. It connects the dots across systems, turning fragmented data into a unified foundation that supports smarter, more efficient, and more relevant experiences.
Why Data Integration Fails Without a Clear Strategy
Enterprise data integration promises a lot, but those benefits don’t materialize by default. Without a clear strategy, even well-intentioned efforts can stall or backfire.
Here are the most common ways integration initiatives go wrong, and what a strong strategy needs to address upfront.
Lack of Clear Business Goals
A lot of integration projects get off the ground with good intentions, but no one’s quite sure what they’re actually trying to achieve. Is it about helping customers find answers faster? Cutting down support costs? Powering personalized recommendations?
If the goals aren’t clear, it’s hard to measure success, or even know how to prioritize what data and systems need to be connected.
Siloed Systems and Poor Data Mapping
Enterprises often underestimate the complexity of their data landscape. When systems don’t “speak the same language,” mapping and normalizing data across sources becomes a major barrier. When data from different systems don’t align, it can cause confusion and reduce trust in your search results.
Imagine two sources that house similar content but are organized differently: support articles labeled one way in your knowledge base, and another way in your CRM. That’s how you end up with inconsistent naming conventions across platforms, duplicate or mismatched entries in different systems, and conflicting metadata that muddles indexing.
This leads to inaccurate results, missed insights, and frustrated users.
A strong integration strategy has to include a detailed data mapping process that reconciles terminology, formats, and relationships across platforms.
Relevant reading: How Coveo Helps With Data Normalization (+5 Other Data Cleaning Challenges)
Governance and Compliance Gaps
Poor integration doesn’t just slow things down, it can also introduce serious risk. Without clearly defined ownership, access controls, and validation protocols, enterprises open themselves up to privacy violations, security issues and regulatory missteps.
Moreover, systems can become unreliable over time. Integration strategies that don’t account for latency or data drift can undermine trust in search and AI outputs.
That’s why legal, compliance, and security teams should be embedded in planning from the start. From enforcing permissions at the source level to ensuring data freshness and fidelity, strong governance helps protect the integrity of both your systems and your user experiences.
Limited Scalability and Extensibility
A well-structured integration strategy prioritizes scalability from the start. Instead of relying on point-to-point connectors or manual sync, a scalable integration strategy must be based on modular extensible architecture that can grow as your needs evolve.
Coveo achieves this through building a unified index. This allows data to stay in its source, but is still surfaceable to users who have the permissions and access controls to find it.
Missing Performance Metrics
Without defined KPIs, it’s impossible to know if your integration strategy is working.
Metrics like query success rates, case deflection rate, and time-to-resolution provide insight into how well search and integrated data are reducing friction and customer effort and help teams tune performance over time and demonstrate ROI to the business.
It’s important to make measurement part of your strategy.
Lack of Cross-Functional Collaboration
Data integration might live with IT, but it impacts everyone. From analytics and compliance to customer support and marketing, nearly every team relies on connected systems to do their jobs.
But too often, these teams plan in isolation, resulting in integrations that work on paper but fall short in practice. The most effective strategies bring everyone to the table early: IT leaders, data architects, business analysts, compliance teams, and customer experience owners.
Aligning across impacted teams and stakeholders ensures that integrations solve real problems and remain sustainable long term.
Successful Enterprise Data Integration Benefits
Consistent and Personalized Experiences
Today’s users expect seamless, relevant experiences, and they judge them in milliseconds. Whether it’s your website, support portal, or ecommerce platform, they expect it to understand what they need before they even ask. That level of personalization is only possible when access to your data is unified.
Data integration ensures that product info, support articles, transaction histories, and customer data like behavioral signals all work together to deliver personalized results and next-best actions.
This is especially critical when deploying AI tools like Coveo. AI search and generative answering systems are only as good as the data they’re built on. When data is fragmented or outdated, AI produces poor predictions, irrelevant results, and disjointed experiences.
A unified data foundation makes relevance not just possible, but reliable.
Better Decision-Making
From real-time analytics to proactive recommendations, business decisions are only as strong as the data behind them. Integration provides a trusted, up-to-date foundation of business intelligence that can be used across departments and use cases, without needing to centralize everything in one monolithic system.
This balance of flexibility and control is key to both agility and governance in enterprise environments.
Operational Efficiency
When enterprise data is integrated, visibility improves across the board, enabling teams to access the right information exactly when they need it.
That means that IT can maintain fewer custom connectors and fragile point solutions. Marketing and sales can work from shared, up-to-date customer profiles. And support teams gain a full view of the customer journey without endless toggling.
Data Integration Best Practices
A solid strategy is a great start, but execution is where enterprise data integration succeeds or fails.
Here are four best practices to help turn your integration goals into real business results.
Adopt an API-First, Modular Architecture
Rigid, all-in-one systems might have worked in the past, but today’s enterprise demands flexibility. That’s where a composable, API-first approach shines.
With modular architecture, your teams can plug in new capabilities as needed, swap out outdated tools without starting from scratch, and integrate systems that support everything from personalization to omnichannel service.
While composability is often associated with ecommerce, the value goes far beyond product discovery.
These same principles apply across industries: helping service teams surface better answers, enabling knowledge managers to connect distributed content, and giving IT teams a foundation that adapts as the business evolves.
Use Intelligent Connectors to Reduce Time to Insight
Manually stitching systems together wastes time and creates fragile dependencies.
Intelligent connectors, like those offered by the Coveo platform, allow enterprises to securely index content from across CRM, ERP, support, commerce, and content data sources without moving the data itself.
That means faster time to insight, fewer errors, and easier scalability.
Maintain Data Quality With Ongoing Monitoring and Validation
Without proper care, even well-integrated data can degrade over time.
That’s why your strategy should bake in routines for ongoing data quality monitoring, ensuring your integrations stay accurate and your experiences stay trusted.
- Define data freshness thresholds so changes sync fast and users consistently see up‑to‑date information.
- Set up alerts for sync failures or shifts in how data is structured
- Regularly audit access permissions and metadata alignment across systems to maintain relevance and security.
These routines not only protect user experiences but also ensure your AI models continue learning from reliable, up-to-date inputs.
Measure, Learn, and Iterate
Once your integration is live, the work isn’t over. That’s when optimization begins.
Treat integration like a living system: something that evolves as your data, teams, and user needs change. Define meaningful KPIs early on, such as content findability, case deflection, time to resolution, or self-service success rates, and use those metrics to guide your ongoing improvements.
Coveo’s usage analytics tools make it easy to identify where users are struggling to find answers, which queries are failing, and where search experiences can be improved. These insights let teams move from reactive to proactive, helping deliver smarter, more personalized experiences.
Future-Proofing Your Integration Strategy
If your tech stack feels more complex than ever, you’re not alone. But the goal shouldn’t be to force everything into a single system because that’s unrealistic. It should be to make meaningful access to distributed data seamless, secure, and scalable.
That’s why future-ready organizations are shifting their focus: from migrating data to intelligently accessing it, wherever it lives. Julie Hamlin, Lead SEO Analyst at Zoom, put it plainly:
“We knew we needed a third-party solution that could index from multiple locations because it’s unlikely we’re ever going to get everybody on one platform. Using in-house search was unrealistic, with the number and scale of domains as well as the frequency that technology and data sources change at Zoom.”
That’s the model future-ready enterprises are adopting: one built on flexibility, composability, and AI-powered relevance. And this is what Coveo is built for.
Ready to make your digital experiences smarter? Check out our on-demand demo hub to get a firsthand view of how Coveo can help.