Data Gravity: Why does it matter?

Data is only as valuable as the information it is used to create. The need for valuable, business-specific, data-driven information has created a level of demand that can only be met by maintaining vast amounts of data. As enterprises move forward, data will only continue to grow. This continual expansion has given rise to the phenomenon known as data gravity.

What is Data Gravity?

Data gravity appears when the amount of data volume in a repository grows and the number of uses also grows. At some point, the ability to copy or migrate data becomes onerous and expensive. Thus, the data tends to pull services, applications, and other data into its repository. Primary examples of data gravity are data warehouses and data lakes. Data in these systems have inertia. Scalable data volumes often break existing infrastructure and processes, which require risky and expensive remedies. Thus, the best practice design is to move processing to the data, not the other way around.

Data gravity has affected terabyte- and petabyte-size data warehouses for many years. It is one reason scalable parallel processing of big data is required. This principle is now extending to data lakes which offer different use cases. Teradata helps clients manage data gravity.

Why is it important?

Data gravity is important for several reasons. An intentional and well-planned growth in the gravity of data sets can greatly boost their utility and value. It can also have the downline effect of increasing the accuracy and applicability of analyses the data might yield.

It’s also important to monitor the gravity of growing bodies of data to curb negative effects and to ensure that the data doesn’t become too unwieldy to be maintained.

In practical terms, moving data farther and more frequently impacts workload performance, so it makes sense for data to be amassed and for associated applications and services to be located nearby. This is one reason why Internet of Things (IoT) applications must be hosted as close as possible to where the data they use is being generated and stored. Increasing data gravity, then, is a matter of configuring it and storing it in such a way as to optimize its utility and accessibility.

Hyperconvergence is often used to illustrate the concept of data gravity. In a hyperconverged infrastructure, computing, networking, and virtualization resources are tightly integrated with data storage within a commodity hardware box. The greater the amount of data, and the more other data might be connected to it, the more value the data has for analytics.

Developers and managers of high-volume cloud applications and IoT systems are among the IT professionals who maintain a keen awareness of data gravity and actively cultivate data sources with configurations that optimize it. Data sources optimized for high gravity strike a balance between maximum utility and the diminishing returns of burdensome maintenance.

Implications of data gravity

Data gravity significantly impacts various aspects of IT infrastructure, shaping how data is stored, managed, and secured across networks. This gravitational pull influences both the technical and the strategic decisions made by organizations.

On network infrastructure

Data gravity requires organizations to establish robust network infrastructure frameworks capable of handling increased data flows. As data accumulates, bandwidth requirements escalate, and networks must be designed to handle high throughput with low latency to ensure efficient data access and transfer.

On data storage

The location and architecture of data storage systems are directly affected by data gravity. Organizations must strategically select the geographic location of their data centers and cloud storage to minimize latency and manage costs. This helps ensure the data is both accessible and compliant with regional regulations.

On data governance

Because of data gravity, effective data governance becomes both more challenging and more crucial. Organizations must implement comprehensive policies and practices to manage data securely. As they do so, they must balance the task of ensuring compliance with privacy laws and regulations while meeting the challenges of data accessibility and usability.

Addressing these implications requires a thoughtful approach to IT infrastructure planning, emphasizing scalability, security, and compliance to harness the benefits of data gravity without being overwhelmed by its challenges.

How to manage data gravity

Data gravity cannot be avoided in today’s data-dependent world. If not managed properly, data gravity can slow down processes, from accessing, organizing, and validating to integrating, migrating, and analyzing. Data integrity degrades; processes become delayed, and inaccuracies show up, which impacts precise analysis.

Data gravity has a profound effect on migration and integration projects – whether the data resides on-premises or in the cloud – so plans must include how to manage the “weight” of the datasets – separately, and as they are brought together or moved.

To be useful, data needs to be current, accurate, and collected and maintained according to security policies, governance, and regulations. Speed is also essential for businesses to stay competitive. Timely access to, and analysis of, data is critical in informing business operations and strategies.

A data fabric approach can help to manage large datasets in different locations, counteracting the negative effects of data gravity. Data fabric can help connect disparate data across your ecosystem for simplified data access and management. When data is effectively managed and connected across the entire tech stack, it becomes less burdensome and more vital to the success of the organization.

Conclusion

Understanding and managing data gravity is crucial for businesses and technology professionals in today’s data-driven world. As data continues to grow in size and complexity, the ability to effectively navigate the challenges posed by data gravity will become a key differentiator for successful data management strategies. By aligning data storage, processing, and analytics strategies with the principles of data gravity, organizations can optimize their data handling practices for better performance, efficiency, and compliance.