What is Data Gravity? Managing the Impact of Your Cloud

While attracting more to your business is normally a good thing, this is not the case when it comes to data gravity. Data gravity can slow performance, inflate storage costs, and strain your resources. To avoid the black hole effect caused by data gravity, you must understand what causes it to know how to mitigate it. We’ll cover what data gravity is and how you can keep it at bay.

What is Data Gravity? 

The concept of data gravity describes the gravitational pull data exerts on other data, applications, and services. With cloud computing, data gravity often manifests as large volumes of data accumulating in a specific data storage service. As this data mass grows, it attracts more applications and services, creating a concentrated hub of data activity. This phenomenon isn’t limited to cloud environments; it can scale up to describe similar effects within data centers.

What Causes Data Gravity?

While some people may operate on an “inbox zero” philosophy, many others may allow their inboxes to pile up, and they may find that the fuller the inbox gets, the less they pay attention to deleting new emails. This inertia also describes how data gravity works – as data grows in a specific area, it’s easier for more to accumulate in the same area.

It can also be difficult to move, organize, or delete data when applications are dependent on certain datasets. Data governance policies can also restrict the movement of certain sensitive data subject to regulatory standards.

According to the 2023 Data Gravity Index 2.0 report, the shift from a physical economy to a digital economy is one of the driving forces behind data gravity. By 2025, it is estimated that 80% of data will live within enterprises. Plus, compliance requirements will mandate that IT leaders retain copies of customer data for longer. Some data gravity is unavoidable, but there are also unnecessary accumulations businesses should work to mitigate.

How Does Data Gravity Affect Cloud Environments?

Because data isn’t necessarily tangible, it can be difficult to see how data gravity impacts cloud environments. However, there are several negative impacts and cloud risks associated with data gravity.

Performance Degradation

When large datasets are localized to one cloud location, they can put a strain on resources in that area and decrease processing times. This can cause drags on application performance and lead to poor user experience.

As artificial intelligence and machine learning use become more prevalent, performance will become an even bigger priority. The Data Gravity Index 2.0 predicts that an increase in these tools will likewise increase data gravity.

Latency and Network Congestion

Latency is a particular performance metric that can be strained with data gravity. When data needs to be accessed frequently from users in geographically dispersed locations, low latency is vital. However, data gravity can increase the time it takes for data to travel from applications to users. Longer processing times can also result in more users on the network at any one time, increasing congestion.

Cost Inefficiencies

Data storage costs can start to creep up with data gravity, leading to significant budgetary drains over time. Worse yet, organizations may be paying for inactive or redundant data without noticing.

Data Management Complexity

As data increases, cloud becomes more complex, making it harder to manage and govern. Keeping the quality and access control consistent becomes harder due to data gravity.

Security, Regulatory, and Compliance Concerns

Security and regulatory concerns also become more difficult. Depending on where the cloud storage is located, data residency and sovereignty laws might be in effect, and risks go up with a greater concentration of data.

5 Strategies for Mitigating Data Gravity in the Cloud

Just because inertia has led to more data that you can handle doesn’t mean you can’t counteract it. Here are a few ways you can mitigate the effects of data gravity in your cloud infrastructure.

Data Classification and Prioritization

Some data is more critical for safety or daily operations reasons. Classifying your data based on how critical it is for company operations and access frequency can help you prioritize where it is stored.

High performance cloud tiers cost more and should only be used for data that requires high performance, low latency, and stringent security controls. Data that isn’t accessed as frequently can be either archived or put into lower-cost tiers.

Data Archiving and Deletion

As data comes in, there should be a plan for how to treat it during its lifecycle. Some may be destined for deletion or archival, which can help free up resources, reduce storage costs, and improve performance. A data lifecycle management plan can serve as a framework for these data decisions. Deleting data securely can also keep security risks lower.

Data Compression and Optimization

Another way you can reduce the strain from data in the cloud is through compression and optimization of storage efficiency. Specific data types may work well with certain compression algorithms, for example, while other data can be optimized through the removal of duplicates or conversion to more efficient formats.

Data Lakehouse Architecture

Data lakehouses can centralize your data storage and enable more advanced analytics. Serving as a hybrid solution, data lakehouses offer both the flexibility of data lakes and the structure of data warehouses. Businesses can store data of different types while maintaining strong data quality and analytics capabilities. When it comes to data gravity, data lakehouses can reduce siloing, leading to more efficient storage setups.

Hybrid and Multi-Cloud Strategies

Various data needs and access requirements can be met with more specificity. For example, sensitive data can be stored in a private cloud, whereas often-accessed data can exist in a high-performance public cloud environment. Tiering data storage can also reduce reliance on any one vendor. Another option is utilizing a cloud service like Managed Azure Stack to help organizations leverage the full range of Azure to gain insights from their data.        

Building a Data-Centric Cloud Strategy

The best time to get a handle on your data is right from the beginning of your cloud architecture design. If you’re about to make the move to the cloud, ensure that data gravity mitigation strategies are built into the foundation of your plan. TierPoint can help you manage your data and build a path to the cloud with optimization and performance top-of-mind. Contact us to learn more.