In this insight, we look at what ‘data gravity’ is, what challenges it creates, and some ways in which businesses can tackle data gravity challenges.
What Is Data Gravity?
Working with (larger) datasets means the need to collect, store and manage the data and move it around to different applications. The data then accumulates (builds mass), attracts services and applications which need to be close to the data to improve the latency and throughput, and leverage high bandwidth. As more data collects and grows at a specific location / a central data store (on-premises or co-located), the process accelerates, to the point where it’s difficult or impossible to move data and applications anywhere else. This affects workflows, creates higher costs, and results in lower system performance, and management overheads.
The term for this cumbersome, dragging effect of a central store data of costly, difficult to manage data on a business was, therefore, first dubbed ‘data gravity’ by IT researcher Dave McCrory in 2010.
Can Occur In The Cloud Too – Artificial Data Gravity
So-called ‘artificial’ data gravity can also occur when attractive forces are created through indirect or outside influence, such as costs, throttling, specialisation, legislation, or usage.
- With cloud storage, although the cloud allows fast scalability, large and growing datasets stored there also attract analytics and applications, and more cloud storage egress fees (charged when applications write data out to the network or repatriate data back to the on-premises environment).
- Usage, e.g. Dropbox charging each individual user for use of Shared Data (Artificial Usage), so each person pays for the data consuming their storage, but Dropbox only stores and directs authorised users to a single copy.
In essence, therefore, artificial data gravity is a product of cloud services’ financial models, not technology.
Tackling The Data Gravity Challenges
Ways in which businesses and organisations can try to tackle data gravity challenges include:
- Separating data storage, by utilising event-driven architectures.
- Investing in new storage solutions, e.g. solid-state storage or tiering, and storage management tools.
- Using hyperconverged systems, i.e. consolidating resources and reducing costs by combining computing, storage, networking, and management in one unified system. This, however, can have scalability challenges.
- Using cloud-based solutions. This can require the use of Cloud Architects (cloud management specialists), using Cloud-native applications such as Amazon QuickSight, or using cloud gateways and cloud-native technologies (container-based environments) e.g., object storage.
- Opting for a multi-cloud strategy (to reduce vendor dependency), using cloud-native storage tiers, e.g. on AWS, Google Cloud, and Azure and matching then them to performance and access frequency of different types of data processing.
- Scaling public cloud computing for batch processes and large-scale analysis.
- Closely monitoring costs to ensure there are no data gravity cost hotspots.
- Making greater use of analytics (analysing data at the edge) and developing better data management and data governance strategies.
What Does This Mean For Your Businesses?
For businesses that collect large amounts of datasets, managing that data in a cost-effective way, and in a way that maintains workflow is a serious issue. Keeping a close eye on costs and analytics, making better, smarter use of the cloud, taking specialist cloud advice, and using cloud-native applications are some of the ways that businesses can avoid falling victim to the effects of costly and cumbersome data gravity. Although a proportion of the data collected may generate value for businesses, too much data in one location can reduce that value by attracting costs and creating an issue that can affect competitiveness. Recognising and understanding what data gravity is and how it occurs, coupled with more of a focus on data management and planning can prevent data gravity problems in the future.
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