Companies today are handling larger amounts of data than ever before, and with large datasets come applications and services that make moving this data more difficult. For these companies, data gravity poses a significant challenge within cloud strategy, impacting cost, flexibility, and performance.
See also: Data Gravity: A Comprehensive Guide
So, what is data gravity? Coined by Dave McCrory in 2010, data gravity compares data to physical objects with mass, which attracts other objects towards them. Within the digital realm, data gravity is the idea that as data grows, it attracts other data, services, and applications, hereby creating a centralized hub. In terms of cloud strategy, data gravity refers to how, as companies accumulate more data within a cloud environment, it becomes more efficient to run related applications and services within the same environment. While on paper, this seems to be the best choice, it can result in vendor-lock in, along with other unexpected setbacks.
Data Gravity and the Impact on Cloud Strategy
Data gravity can result in the inability to seamlessly move data, guarantee performance, and present the possibility of vendor lock-in because of heightened reliance on a single cloud provider. Because the movement of big data can be not only a costly endeavor but also a confusing and time-consuming one, companies might avoid the transition to cloud-based solutions, impacting their potential for digital transformation.
Performance issues, including delayed access times and heightened latency, can arise when data is stored in multiple locations. These issues bottleneck the ability of applications relying on data processing in real-time to function efficiently. By storing data under a sole cloud provider, organizations sidestep these issues, but put themselves at risk of vendor lock-in, making changing providers a challenge fueled by high costs and potential disruption to operations.
Alleviating Challenges of Data Gravity
How can companies manage the challenges data gravity poses to operations? Consistent data lifecycle oversight, edge computing, and the integration of hybrid or multi-cloud solutions can all help companies managing large datasets to avoid vendor lock-in and migration issues, all while guaranteeing peak performance.
The negative effects of data gravity can be consolidated through data lifecycle management. By deciding which data stays or migrates based on scope, value, and use, effective data lifecycle management can improve accessibility and help companies limit data gravity’s residual impact on operations and reduce costs associated with moving unimportant data.
By providing organizations the power to process data in close proximity to its source, edge computing lowers the pressure to migrate high volumes of data, therefore lowering latency. Additionally, edge computing can boost performance by decreasing strain on centralized cloud operations and consequentially improving data management and processing.
Decentralized storage, or cloud spread, is a strategy that helps organizations limit possible reliance on a single cloud provider through delegating workloads across hybrid and multi-cloud providers. Because data gravity can influence organizations to store all information under a single cloud, cloud spread mitigates this setback by preventing over-reliance on a single provider. By using hybrid or multi-cloud solutions, organizations enhance data resilience and redundancy, ensuring continuous access to data even if one provider experiences downtime or data loss. It also helps avoid vendor lock-in, preventing over-dependence on one provider, and giving organizations better leverage in negotiating prices and services.
Considerations Looking Ahead
As data volumes are on track to keep growing, the negative impacts of data gravity seem inevitable. To mitigate these potential side effects, consider how your organization can integrate AI-driven data management alongside additional technologies such as decentralized storage.
Amidst this unpredictable world of technological advancement, adaptability is proving essential to survival. With flexible data management techniques, companies can prevent their important data from becoming a burden. To stay competitive is to stay up to date on advancements in the management and storage of data and be willing and able to integrate emerging solutions into operations to ensure efficiency.
Eli Lahr is the Senior Solutions Engineer for Leaseweb USA, specializing in advanced cloud solutions. With extensive consultative experience and technical knowledge, Eli excels in optimizing infrastructure for diverse organizational needs. He is committed to ensuring efficient and scalable solutions for both large enterprises and smaller businesses.
