What is Data Gravity? 

If you have ever wondered why data keeps growing and creating big data, there’s a simple and familiar concept behind it. As organizations grow, amassing vast amounts of data, the amount of data in their repository keeps growing, creating an ever-increasing repository of information.

This has to do with how large data attracts more data, applications, and services, increasing in size over time. This phenomenon is called data gravity. Since data gravity is unstoppable, it’s crucial to understand what it is and how to manage and optimize it.

This article explores the concept of data gravity, its effect on organizations, and how to manage it to help you use it to your benefit.

What is the Definition of Data Gravity?

Data gravity is very similar to the physical gravity you are used to. It refers to how big data attracts applications, services, and more data, leading to a snowball effect that quickly increases data size. According to Newton’s law of gravity, the earth attracts other smaller objects to it. Similarly, large data sets attract applications, services, and other data.

Typically, the larger the data set, the more data it attracts, creating a gravitational pull that keeps the data pool close by. This concept applies not only to data in physical proximity to big data but also to the digital realm, that is, data in cloud storage. Examples of data gravity are data warehouses and data lakes.

Consider a business keeping vast volumes of consumer data in a data warehouse. The warehouse expands in complexity and scale as it gathers and analyses increasing volumes of data.

This expansion will draw new uses and services, including customer relationship management (CRM), which is applied for more thorough consumer analysis. This analysis also draws in more data, creating a continuous cycle of data growth over time.

History of Data Gravity

The history of data gravity is relatively close. The term was first introduced in a 2010 blog post by Dave McCory, who was a software engineer at GE Digital. When explaining the concept of data gravity, he used the analogy of physical gravity to explain how large datasets attract IT systems, like how a planet’s gravitational pull attracts objects around it. For example, the moon orbits around the Earth because of gravity. So, similarly, large data, which in this case are applications and services, is like the Earth, attracting the moon.

David McCory also explains in another blog post that data gravity doesn’t only occur naturally; external forces like costs, specialization, and legislative can indirectly influence data gravity. This is called artificial data gravity. He gives an example of AWS S3 that allows unlimited transfer inbound traffic for free. This free unlimited transfer encourages users to gather data, leading to artificial data gravity because it is externally influenced.

Effects of Data Gravity

Data gravity has both positive and negative effects on organizations. Being aware of both sides can help you manage data gravity effectively.

Pros of Data Gravity

The perks include:

• Centralized Data Management: Data gravity allows organizations to manage data in a centralized data hub, making it easier to manage data across multiple applications and departments.
• Improved Data Integrity: Centralized data management reduces the risk of data inconsistencies by helping an organization manage its data from one location. Thus, they can monitor data and ensure it is up-to-date and accurate.
• Better Data Utilization: Big data enables organizations to utilize data effectively. For example, the availability of more data provides more information when performing data analysis.

Cons of Data Gravity

Some major disadvantages are:

• Scalability Problem: As the size of the data increases, organizations could face scalability issues. Due to the large data size, migrating to better resources or another platform could be uneconomical. This can lead to vendor lock-in as the organization will find it difficult to switch to another platform. Thus, you may become solely dependent on a single provider.
• Latency: Organizations can face the issue of latency if the applications and services are far from the large data set. If the distance between where data is stored and processed is significant, this distance causes latency, crippling performance. To reduce latency, it’s best to ensure that the data and the gravitating applications and services are close or co-located.
• Higher Costs: Another problem data gravity poses is the higher cost involved. For example, organizations may need to acquire new storage tools and applications, which could significantly increase data management costs.

Managing Data Gravity

Big data can be overwhelming, so managing data gravity is crucial to ensure that you take advantage of its benefits. Below are some ways to manage your growing data:

• Cloud-Based Solutions

Opting for cloud storage offers a scalable and flexible solution, enabling organizations to manage large data sets better. Also, cloud services reduce the complexity of data management by making data accessible across different devices and departments. However, storing all data on the cloud is not always possible. So, organizations that need on-premise storage systems should opt for scalable systems that reduce latency. One such solution is hyper-converged systems, which combine storage and networking in one platform, cutting down latency and ensuring effective data management.

• Data Integration

You can take advantage of data gravity by integrating several data sources into one data hub. Although combining data to form one gigantic data set may seem ineffective, one data source means you have to contend with only one outlet instead of several, making it more organized. Doing so also makes accessing and managing data easier, leading to better performance and fewer errors.

• Data Governance

Robust data governance policies can also help manage and utilize data gravity. These policies include data standards, access controls, and accountability measures set to ensure the smooth management of big data.

• Decentralized Architectures

Decentralized architectures like cloud storage can also reduce the risks associated with data gravity. Since these tools don’t need a central physical location, data can be processed closer to where it is generated. As a result, latency can be reduced, and data processing times can be improved.

• Effective Data Planning

Generally, effective data management can help prevent the risks involved in data gravity. Taking care to consider not just the current needs, but also the future data needs of an organization. Making the right decisions for your data can help manage data gravity.

The Importance of Data Backup in Data Gravity

The more data there is, the higher the risk of corruption and loss. In the event of a data disaster, an organization will lose a large amount of data created by gravity. Thus, it’s crucial to implement robust backup solutions that will protect against data loss during disasters.

However, the biggest problem with data backup in such an environment is not its size. Data attracts new applications or services, which often decentralize data processing, creating new data sources. Therefore, without versatility, data protection can focus only on selected silos, ignoring new data sources. In such a case, we may: 1) consciously not expand the ecosystem with modern tools, 2) agree that some data will not be secured, 3) or implement an additional tool to secure modern workloads, which will only complicate the data management process and may negatively impact their consistency.

No. 4 is, of course, replacing the backup tool with Storware Backup and Recovery, which supports protection for virtual, physical, and cloud data and allows integration with enterprise-class backup devices, expanding their data protection capabilities with new sources.

Also, modern data backup facilitates data mobility by moving data to a different site, reducing the effect of data gravity. Data gravity could easily make data heavy and difficult to move around, but with backup systems, you can reduce the load through regular backup that provides bits of data available for a period. Thus, it’s crucial to prioritize data backup as data pulls more data, application, and services, increasing its bulk.

Conclusion

Like physical gravity, data gravity is inevitable, and unfortunately, if not well managed, it can lead to negative consequences like latency, high cost of operation, and scalability issues. Hence, organizations need to understand how it works, how best to manage it and how to use it to their advantage. Data gravity can lead to better data utilization, centralized data management, and improved data integrity. By following our guide on managing data gravity, you can harness these perks and ensure they work to your advantage.