Collecting data is one thing, but having centralized oversight is another. Today, organizations collect data from dozens, hundreds, or even thousands of data sources. That data spans numerous data ecosystems and various on-premises, hybrid, and cloud data infrastructures, with no visibility or documented ownership, thereby heightening the risk of data exposure.
This phenomenon is what’s referred to as data sprawl, where data is rambling across environments in an uncontrolled manner. As data sprawls across the digital landscape, collection is no longer a struggle; it’s a resource that organizations continuously struggle to control.
Is your organization collecting data from multiple sources and drowning in massive volumes of data, only to not find the right data just when you need it? You’re not the only one. Modern-day digital dilemma, data sprawl, is hiding in plain sight and legacy systems can’t keep up with its growing complexity.
What is Data Sprawl?
More than an IT jargon, data sprawl refers to all digital company-wide information, such as customer data, the organization’s own data, such as employee data, operational data, internal documents, emails, data storage buckets, and other forms of data the organization creates, collects, stores, processes, and shares across the data pipeline.
In short, data sprawl encompasses all the structured, semi-structured, and unstructured information an organization has at its disposal, often without centralized visibility, governance, or control.
Given the speed at which organizations operate today, especially as they embrace AI advancements and associated tools, it’s common for data to be distributed across multiple endpoints, including on-premises systems, cloud platforms, SaaS applications, data lakes, and other third-party data environments.
This makes it a nightmare for organizations, as regulations impose strict requirements on organizations handling sensitive data to know where data resides, limit its collection and retention, secure it, delete it when it’s no longer required and govern the entire data lifecycle.
The Challenge of Data Sprawl
As organizations collect, process, store, and share data at an unprecedented rate, data becomes increasingly distributed across data ecosystems, giving rise to myriad challenges that extend beyond the costs of handling and storing excess data. Various complexities arise, including:
a. Limited Data Visibility
You cannot protect what you cannot see. As organizations migrate data to cloud services and hybrid platforms, they traverse outside an organization’s governed data ecosystem. Lack of centralized data governance leads to limited data visibility as fragmented data environments provide minimal data discovery and classification capabilities. Teams often find themselves questioning which sensitive data exists, where it is, and whether it’s secured.
b. Enhanced Data Exposure
Without data visibility, each piece of data is at risk. Unsecured and outdated data buckets don’t stand a chance in navigating today’s attack surface. What makes matters worse is shadow data that exists entirely outside official, monitored company networks, heightening the risk of data exposure because security teams cannot track or protect it. Without security guardrails, data is at risk of unauthorized access and other security incidents.
c. Escalating Compliance Risks
Global data privacy regulations such as the GDPR, CCPA/CPRA, LGPD, HIPAA and others impose strict requirements on businesses, requiring organizations to know exactly what data resides where, how it is processed, with whom it is shared, what purpose it serves, for how long it is retained, when it’ll be deleted, whether data subject rights are being fulfilled, whether access controls are in place to safeguard data, and more. Ungoverned data sprawling across the digital landscape increases the risk of noncompliance penalties.
d. Increased Storage and Operational Costs
Having immense volumes of data comes at a cost. Aside from security concerns, lack of data visibility and compliance obligations, organizations handling data require a place to store it. This could be on-premises data storage, network servers, remote cloud facilities, data centers and warehouses, etc. This requires immense resources to handle data, adding to an organization’s expenses just to maintain it.
5 Best Practices to Manage Data Sprawl
Unlike other challenges that can be completely remediated, data sprawl is here to stay. This is because organizations won’t hold back from collecting and handling data and, in fact, will increase data generation altogether. With more data being created each day, managing data sprawl is no longer just a good practice but an operational and legal requirement. Best practices include:
1. Establish Enterprise-Wide Data Discovery
There’s no managing data without having granular insights into data assets. The key is to discover data stores and understand what data exists where and for what purpose. Data discovery shouldn’t be a one-time process but a recurring system with comprehensive visibility into enterprise data assets.
2. Classify Sensitive Data at Scale
Data discovery is the initial step, but classification determines which data is at the greatest risk. Right after the discovery phase, organizations should begin classifying data across data lakes, SaaS applications and other data environments based on its sensitivity (public, internal, confidential and restricted). Grouping data into tiers helps dedicate compliance efforts accordingly.
3. Minimize ROT Data
Redundant, Obsolete, and Trivial (ROT) data holds no significant value to businesses, yet its maintenance costs businesses significant resources. Organizations should invest in automated tools that rigorously scan systems to identify and delete duplicate, outdated, and unnecessary files, thereby minimizing storage costs and reducing the risk of inadvertent data exposure.
4. Adopt a Robust Data Governance Posture
There’s no managing data sprawl without a robust data governance posture backed by policies, best practices and accountability. Begin by assigning data ownership across departments, classifying data, assessing data quality, tracking data lineage, meeting security requirements, and maintaining an ongoing audit schedule, and much more. Formal policies should define these requirements, and an oversight body should govern the data lifecycle.
5. Continuously Monitor Data Risk
Managing data sprawl is an ongoing process. It requires consistency, real-time tracking, analyzing security risks, conducting risk assessments, and patching vulnerabilities to keep data secure, relevant and accurate. Continuous monitoring helps detect data repositories, assign sensitivity, implement access controls, and minimize security incidents.
Take Control of Data Sprawl with Securiti
It’s clear that data sprawl is a compliance risk, and legacy systems don’t stand a chance of defusing this increasing regulatory risk. In an AI-first era, organizations need an AI-powered automated tool that provides real-time visibility and unified governance across their entire data estate.
Securiti DataAI Command Platform is built for handling today’s complex challenges. The platform is designed to provide a unified layer for data security through Data Security Posture Management (DSPM), AI security through AI Security & Governance, data sprawl containment through Data Discovery & Classification, Data Access Governance, Data Minimization and several other modules that work across hybrid cloud, multicloud, SaaS, and on-premises environments.
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