To Overcome Data Sprawl, Use the Right Data Patterns

Data is almost certainly the most valuable asset enterprises have. It’s the intellectual property that differentiates them in market, powers customer relationships and fuels decisions that drive growth. Most enterprise leaders intuitively understand how important data is, and most enterprises start out with solid data management strategies. However, this can change over time.

Today, most enterprises have diverse public and private environments, leading to cloud sprawl. This includes big-name hyperscalers like AWS, Microsoft Azure and Google Cloud, but also SaaS providers, networking and security specialists, AI-specific neoclouds and more, all providing real value to the organization. According to one survey, enterprises use an average of 36 different cloud and SaaS providers.

Without ongoing discipline, even the best-designed data strategies can drift. Consider these examples:

• A proof of concept spins up in one cloud and quietly transitions to production without documentation on the data architecture.
• A new SaaS vendor is onboarded for a specific use case but ends up holding a surprising amount of critical business data.
• A team builds a data pipeline to solve an urgent problem but then never revisits where that data lives or how it’s governed.

None of these are due to incompetence. They’re natural results of being a fast-moving enterprise. But the cumulative effect is data sprawl.

What is data sprawl, and what are the risks?

Data sprawl occurs when a company’s datasets are fragmented across many public and private environments, leaving them unable to manage data in a holistic manner. It makes life significantly more difficult for IT leaders, particularly for those looking to tackle cutting-edge use cases like agentic AI.

The value of enterprise data lies in the ability to correlate it: to draw connections across systems, teams and silos to build a complete picture that drives better decisions. When data is scattered across environments without clear governance, that correlation becomes more difficult. Thus, enterprises make decisions based on a narrow aperture of the overall picture. They miss potentially transformative insights because data is hiding in systems that are either disconnected or outright forgotten.

Data sprawl also erodes the main value of hybrid multicloud: the ability to treat infrastructure as ephemeral. When enterprises distribute datasets into different cloud environments, they risk losing control over that data, because it’s sometimes difficult to get data back out. If an organization has sensitive data that they can’t get out of a particular cloud, then they’re effectively stuck in that cloud, even if it no longer meets their infrastructure needs. This is especially true for large datasets that accumulate over time, where egress pricing grows and makes migration prohibitively expensive.

With the right data management strategy, enterprises can ensure data remains portable. In turn, this allows them to use the cloud as intended: as an infrastructure solution they can turn on when they need it and turn off when they don’t.

Of course, effective multicloud networking is an essential part of data management. Businesses need reliable connectivity to move data wherever it needs to go. However, it’s equally important to have a strategy for how they’ll utilize those connections. This means managing the complete data life cycle to optimize the value, privacy and cost-effectiveness of different datasets. Let’s look at a few of the data management patterns an enterprise might use to achieve this.

What is a data pattern? What are the five main data patterns?

Truly capitalizing on the value of enterprise data starts with a simple but powerful recognition: Not all data is equal, and different kinds of data should be managed differently. These different ways of managing data are known as data patterns.

A data pattern is a deliberate strategy for how a particular type of data should be stored, governed, accessed and positioned. Choosing the right data patterns plays a key role in addressing data sprawl, because it replaces ad hoc accumulation with intentional design.

1.     Your “crown jewel” datasets

This is the data that holds the most potential value for your business, including proprietary customer insights, core product data and competitive intelligence. It’s important to keep this data out of cloud native storage, where you might lose control over it. Instead, it belongs on neutral storage infrastructure, where it’s both accessible and portable. We call this neutral storage environment the authoritative data core. This signifies that any data you store there is the authoritative copy of that data.

Before: Unstructured data management leads to sprawl

After: Authoritative core enables consistent, centralized data management

If you need to move data into the cloud, you can do that without sacrificing control. That’s because you won’t move the raw data; you’ll move a temporary copy created specifically for that cloud use case. When you’re done with it, you can simply delete the copy.

2.     Your operational datasets

This is information your company aggregates during its operations, such as logging and telemetry data. It won’t unlock your next great business insight, but it’s still good to have around. The best way to store this information is in a standard data lake.

This can be a cost-effective way to store large volumes of data. It’s ideal for datasets that don’t move frequently and don’t require any special management. Enterprises often place data lakes in the public cloud, because cloud services provide scalable capacity on demand. Since this data doesn’t need to be highly accessible or portable, there’s less risk involved with placing it in a cloud environment.

3.     Your compliance and regulatory data

This is data your company needs to demonstrate compliance. It can be stored in an archival data management environment. This is long-term storage that needs to be secure and cost-effective.

You don’t want to pay a lot to store and move this data, because you’ll rarely need to access it. However, you need to be absolutely certain it remains secure and available for the times you do need it. The risks of being unprepared to demonstrate compliance are simply too high.

4.     Transient and ephemeral data

Not all enterprise data is meant to persist. Much of it is generated for a specific workload or computation and has little or no value after the task is complete. This could include intermediate processing results, staging data or short-lived copies created for testing. This data should be destroyed after use.

Transient data is one of the biggest contributors to data sprawl. Without intentional cleanup, temporary datasets accumulate across environments. This isn’t just about unnecessary storage costs; it’s about noise. When transient datasets linger alongside authoritative datasets, it can undermine the ability to correlate data across the organization.

5.     Shared and collaborative datasets

Many valuable insights come from combining internal datasets with data from external partners and suppliers. In healthcare, delivering effective patient outcomes requires data exchange between hospitals, insurance providers, pharmacy networks and specialist practices. Each provider operates in a separate environment with their own governance requirements. Similar dynamics occur in financial services, supply chain management and any industry where ecosystem partners need to share data across organizational boundaries.

Like crown jewel datasets, collaboration data requires neutral infrastructure. But while crown jewels require neutrality to preserve portability and control, collaboration data needs neutrality to ensure all participants can access shared datasets on equal terms, instead of one participant becoming the default hub. Strong governance guardrails are essential in both cases to ensure data consistency, security and compliance.

Where to implement data patterns

Now that you know these common data patterns, the next question is where you’ll get the infrastructure and solutions to execute them.

Inside an Equinix IBX® colocation data center, you can deploy the interconnected storage you need to fully capitalize on the value of your most important data. When you need access to cloud services, you can tap into our market-leading portfolio of native cloud on-ramps from all major cloud providers. You can do this across our global data center portfolio, while also accessing Equinix Fabric® for on-demand virtual connections that cut across cloud sprawl.

Enterprise leaders are recognizing that they need the right multicloud networking strategy to keep data portable and accessible. They’re also learning that interconnecting multicloud environments is no longer just a box to check. When done right, it can become a competitive differentiator. Learn what this looks like in practice: Access our research report on the global state of hybrid multicloud networking.