Businesses are collecting more data than ever but many still struggle to answer a simple question: “What is happening across the business right now?”
Customer data sits in CRM platforms. Finance operates from ERP systems. Operations relies on business applications. Marketing has campaign data. And critical information is still exchanged through spreadsheets, emails, and manual reports.
The result is a familiar problem: too much data, too many systems, and not enough connection between them. In a 2024 DATAVERSITY survey, 68% of organizations identified the number of data silos as a major data-management challenge, making it the top challenge reported. Data quality issues followed at 56%.
And the cost isn’t just technical.
Poor data quality and availability can consume significant employee time. McKinsey found that employees spent an average of 30% of enterprise time on non-value-added activities because of poor data quality and availability.
So, what happens when that data finally works together?
The Business Problem Is Bigger Than Data Silos
Data silos don’t always look like a major technology problem.
Sometimes, they look like:
- A finance team waiting for data from operations before closing a report.
- A sales team working with outdated customer information.
- A supply chain team discovering inventory problems after orders are placed.
- A leadership team receiving last month’s numbers when today’s decisions need today’s information.
- A data scientist spending day preparing datasets before a model can even be tested.
Individually, these may seem like small inefficiencies. Across a large organization, they become lost time, higher operating costs, missed revenue opportunities, and slower decisions.
The hidden cost can add up quickly
IBM reports that poor data collectively costs organizations in the U.S. $3.1 trillion annually, while its 2026 analysis found that more than a quarter of organizations estimate losing over $5 million each year because of poor data quality.
For enterprises, the issue is therefore not simply:
“Do we have enough data?”
It is:
“Can we trust it, connect it, and use it quickly enough to make the right decision?”
What Data Silos Are Really Costing Businesses
When information remains fragmented, the losses can appear in several ways.
1. Lost productivity
Data teams and business users spend time searching, cleaning, validating, and reconciling information. Instead of analysing a problem, teams may spend hours answering:
Which number is correct?
McKinsey found that data processing and clean-up can consume more than half of an analytics team’s time in some organizations.
2. Higher technology costs
Maintaining multiple repositories, pipelines, integrations, and duplicated datasets increases infrastructure and operational costs.
One McKinsey example involved a global bank with more than 600 data repositories. Managing those repositories cost approximately $2 billion annually. After simplifying its data environment to 40 domains and establishing standardized sources, the bank reduced annual data costs by more than $400 million.
That’s a powerful reminder: Data fragmentation can become an infrastructure problem, a cost problem, and eventually a business problem.
3. Missed revenue opportunities
When customer, product, pricing, and transaction data cannot easily be connected, organizations may struggle to identify buying patterns, churn risks, cross-sell opportunities, or changing customer behaviour.
The data exists. The opportunity exists. But the connection between them is missing.
4. Slower decisions
In fast-moving markets, waiting several days for a consolidated report can mean making decisions based on information that is already outdated.
This becomes particularly important in areas such as:
- Supply chain planning
- Financial services
- Retail pricing
- Fraud detection
- Customer experience
- Manufacturing operations
The value of data increasingly depends not only on accuracy, but also on how quickly the business can act on it.
A Quick Look at Today’s Data Management Challenge
The pattern is clear: organizations aren’t struggling because data doesn’t exist. They are struggling to manage, connect, govern, and use it effectively.
What Changes When Data Is Organized and Connected?
Organizing enterprise data doesn’t mean putting every piece of information into a single database. It means creating a reliable data foundation where information from different sources can be connected, understood, governed, and made available to the people and systems that need it. Think about a customer who interacts with a business through five different channels.
Without connected data, the organization may see:
- A CRM records
- A support tickets
- A purchase transaction
- A website interaction
- A marketing response
as five separate events.
With connected data, these interactions can become one customer story.
That enables the business to understand not only what the customer did, but also what those actions mean when viewed together.
From Data Integration to Business Intelligence
This is where organized data starts creating measurable business value.
A connected data environment can help organizations:
- Create a trusted view of critical business information
- Reduce duplicate and inconsistent data
- Automate data preparation and reporting
- Improve visibility across departments
- Track data lineage and ownership
- Identify relationships across datasets
- Support faster analytics and decision-making
- Create a stronger foundation for AI
The important point is that data organization isn’t the end goal. Better decisions are.
AI Makes the Data Problem Even More Important
AI has changed the conversation around enterprise data. Companies are moving from experimentation toward AI-powered workflows, analytics, and automation. But AI needs reliable data and context to produce reliable results.
McKinsey’s 2026 research found that more than two-thirds of high-performing companies say data is the primary obstacle to enabling AI. Its research also highlights the need for structured and unstructured data to be connected into a governed, reusable foundation.
This means organizations can’t treat data quality, governance, metadata, and integration as back-office IT activities anymore. They are becoming AI-readiness requirements. A connected data foundation can help AI applications access the information they need while maintaining context, lineage, governance, and control.
A Real-World Example: From Hundreds of Repositories to a Simpler Data Environment
The global bank example shows what can happen when organizations take a fragmented data environment seriously. With more than 600 repositories, the bank faced high maintenance costs and complexity. By consolidating its data environment into 40 unique domains and standardizing trusted sources, it reduced annual data costs by more than $400 million.
The lesson isn’t that every company should have exactly 40 data domains. The lesson is that simplifying and organizing data can directly influence the economics of the business.
The Future Is Connected Intelligence
The goal shouldn’t be to create more dashboards, build more databases, or collect more data simply because technology makes it possible. The goal should be to make data usable.
When data is connected and governed:
Data becomes easier to find.
Insights become easier to generate.
Decisions become faster.
AI becomes easier to scale.
And teams spend less time asking where the information is and more time figuring out what to do with it.
What Happens When Your Data Finally Works Together?
You don’t simply get a cleaner data environment. You get a business that can see the bigger picture. Customer behavior can be connected with revenue. Operations can be connected with costs. Supply chain signals can be connected with demand. Risk can be connected with real-time activity. And AI can be connected with trusted enterprise information.
That’s when data stops being an IT asset sitting across disconnected systems and starts becoming a business advantage.
The Bottom Line
The organizations that win with data won’t necessarily be the ones collecting the most of it. They’ll be the ones that can connect it, trust it, understand it, and act on it faster.
The question for business leaders is no longer:
“How much data do we have?”
It is:
“Wondering how connected your enterprise data really is?” And that is where a connected data strategy can make the difference.
