Data sprawl is a common problem for businesses and enterprises with a long history of data collection and management. Sprawl happens when companies adopt new solutions without integrating legacy systems, acquire other companies and their long data history, and gather data without a plan. Some organizations may think moving to the cloud will solve these data issues. However, data sprawl is a cloud problem too.
The cloud is not a fix-all solution
Despite growing pains, the cloud is extremely popular among businesses seeking the next level of digital transformation. More companies are migrating and hoping that the cloud will help smooth out existing challenges with data and operations. Unfortunately, many companies will end up with a mirror of their existing data ecosystem without a clear plan.
Data sprawl in the cloud looks like hosting a large volume of data in disparate places with no clear visibility into where it is, who uses it, and why. Companies may have migrated operations to the cloud instead of an on-premises system, but this doesn’t automatically fix fractured data. In fact, thanks to the ever-evolving nature of the cloud, it can make data sprawl worse.
Many different people will access the data every day through multiple tools and for different reasons. Depending on governance structures and policies, companies may end up with multiple copies of the same data floating around. And that’s just static data. Data also moves constantly from cloud locations to the device and back for processing.
Even more concerning, a recent threat report released by Netskope found that one in five users uploads an unusually high amount of data to personal locations the month before leaving an organization. If companies don’t know what data they have, they could miss these potential breaches.
Data sprawl is a serious security concern and prevents full cloud potential
Companies with data sprawl don’t know where all their data is located and therefore can’t protect it sufficiently. When companies had this issue with on-premises systems, they still had some control over their network. With data located in the cloud, it’s even more likely that sensitive data could move to a location that puts it out of compliance.
A recent cybersecurity cloud study found that a significant number of businesses they surveyed had failed an audit recently or were found out of compliance. Data sprawl prevents the most basic data governance from happening:
- Companies don’t know what data they have available and whether it constitutes sensitive data requiring greater protection.
- They also don’t know where their data is located at any given time, making it challenging to be in compliance when important audits happen.
- Companies don’t know who (or what device) is accessing and activating data. With remote work and distributed systems, the number of person and machine identities only increases.
- Without a coherent, integrated system, companies have no way to know why each identity is attempting to access data and cannot respond proactively to anomalies. When companies can only react, they’re more at risk of data breaches.
Companies cannot realize the full potential of their cloud investments without streamlining and automating data workflows. As long as data remains hopelessly tied up in complex systems with no observability, companies migrating to the cloud bring the same challenges into a new environment.
Ending data sprawl in the cloud requires several steps
Experts recommend taking steps such as multi-factor authentication and establishing a zero-trust architecture to protect data. The underlying reasons for taking these steps involve simplification and observability.
Improving classification of data allows companies to understand their data
Classification improvements make it clear what data is. Is it sensitive data? What is its risk? We’ve moved beyond gathering all data with no real purpose and into understanding exactly why we’re keeping data. Even better, automatic tagging and metadata collection help companies understand the entire lineage of their data.
Automating data discovery ensures nothing falls through the cracks
Data discovery helps companies understand where new data comes from and allows better processing for data quality. Continuous data discovery also determines if and when data appears in a new place.
Implement machine-learning driven observability
Machine learning can take companies from visibility to observability over their data ecosystem. While visibility means that organizations can see into their ecosystem, observability provides actionable and context-driven insights into potential anomalies. This process is automated thanks to machine learning and gets more accurate over time without increasing IT headcount.
Instead of working to lock data down, companies should have a governance strategy that allows stakeholders to access the data they need. The cloud can enable real-time processing and real-time insights if companies can find a way to free data through identity management.
Don’t replicate on-premises challenges in the world of cloud
Companies dealing with existing data sprawl could potentially migrate that sprawl to the cloud along with other operational components. Having a plan to address sprawl by leveraging cloud tools to understand the who, what, and where of their data is key.
Cloud does have the potential to help companies manage their disparate systems and glean insights from data more rapidly. With the right plan in place and a full accounting of existing data assets and tools, companies could overcome risky security threats and make data more available to stakeholders despite work location.
Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.