What is data gravity?

Data gravity defines the behavior of large datasets to pull smaller datasets. It talks about the difficulties of managing large data sets. Also, it is a concept used to characterize the size of a dataset.

Considering the amount of data produced daily, you can easily understand why your company should care about data management. Any business that fails to manage all data effectively faces various challenges in its operations.

What is data gravity?

Companies have to deal with larger datasets daily than in the past. Dealing with data is an expensive business and can progress very slowly. This process is called data gravity because of the force application of data stacks in information technologies.

The data do not apply the force at all. The data of mostly small applications start to gather around large datasets. Transferring data from one place to another becomes quite challenging as data grows.

Data gravity can make you dependent on only one cloud provider. When this happens, your company starts to have problems being innovative. Multi-cloud or hybrid cloud services are required to overcome the issue.

What does data gravity affect?

Data gravity does not have a wide-ranging effect. However, it is something to be careful about. Because it makes the management of datasets as complex as possible. Additional capacity is required to handle massive amounts of data.

What does data gravity affect
What does data gravity affect?

1. Data gravity and cloud strategy

As datasets grow, you may lose control over the data produced. Additional services are required to use the data. This is where the cloud strategy comes into play. The cloud strategy becomes a significant gain for your organization.

Dealing with large datasets is a very costly business. The cost of accessing data increases considerably. Hosting and processing repetitive datasets may become unaffordable with your organization’s capital.

When you apply a suitable cloud strategy, you can eliminate the data gravity problem. For this, you should pay attention to the scale and latency factors. The cloud is an essential solution; as data increases, it becomes harder to move them.

2. Data gravity and storage

Repetitive data other than backup always takes up space. A better solution is to create interconnected data lakes rather than a single sea of data. Thus, you can manage to process data from different sources.

The further you are from your cloud server, the slower you may experience data transfer. Cloud services eliminate this problem and allow you to bypass the data gravity problem. So you balance data management costs.

As datasets grow, you need more capacity. Upgrading capacity on standard servers is exceptionally costly. In cloud solutions, the cost problem is eliminated as much as possible. You can take more effective steps with innovative approaches.

3. Data gravity and latency

If you want to reduce latency, the most common approach is to put data in a single cloud. However, choosing a single cloud comes with some disadvantages. You may encounter serious problems, especially regarding fees and compatibility.

In addition to paying a certain fee to store data in the cloud, you may have to pay a transaction fee. All this means additional cost. When the storage available to you is insufficient, the compatibility issue will cause you to incur additional charges.

Initially, seeking support from a single cloud provider may be a good idea. But as time goes on, you can discover better solutions. However, you may encounter expensive options to manage the data and eliminate the latency problem.

How to deal with data gravity?

Data gravity can make it difficult to do many things. Data gravity should be handled in detail to progress efficiently. Also, you can eliminate this problem by taking advantage of technological solutions and data integrations.

How to deal with data gravity
How to deal with data gravity?

1. Data governance

Data governance is the most valuable part of data management. You have an accountability approach to data. At the same time, you can best define your responsibilities. In short, you manage to explain data management principles.

Suppose you are careful about data problems that may arise, such as data gravity. In that case, you can intervene at the right points. Thus, you can create higher-quality data and gain advantages that make data mapping much more accessible.

2. Data integration

Data integration is essential for any organization. It is the most accurate way to increase efficiency in data management. It also allows you to take advantage of data where you need it. However, it is not the absolute solution for data gravity.

Data integration allows you to manage data from a single center. Instead of dealing with large volumes of data, you deal with smaller pieces. For this reason, you can implement data management steps without encountering the difficulties of enormous data gravity.

3. Data management

Wherever data is stored, you need to have a data management strategy. You must determine how and by whom the data will be managed. You must go about defining the state of the data in the cloud.

When you define the data correctly in cloud systems, data gravity ceases to be a problem. When you need more apps or services, you can take fundamental steps. So you can maintain data integrity.

Data gravity is not an insurmountable problem. It indeed affects data management. You can produce solutions at a certain level by using cloud services. However, you should know that no precise method can be seen as a solution for data gravity.


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