Changes in the data landscape are driving many organizations to move their Big Data platform to the cloud. As per a recent BI on Big Data Adoption survey conducted by Kyvos Insights, more than half of the organizations plan to move their Big Data infrastructure fully to the cloud over the next 3 years.
Moving Big Data on the cloud is like outsourcing all your data management and storage workloads to the cloud so that you can focus on your core business. Instead of worrying about the infrastructure, you can work on solving the problems that need to be solved with your data.
Cloud Transforms Big Data Economics
Moving Big Data to the cloud may not always be a cheap option, but it is more cost effective as you pay only for the resources that you need. So, it becomes easy to scale up and down as required and cut down costs by using the resources optimally.
Besides this, it gets easier to make Big Data available for analytics to remote or distributed teams. In fact, with the cloud, you do not need to move petabytes of data from one place to another. The data can be stored where it belongs, and instead of relocating data to the analytical environment, you can shift analytics to the data environment.
Using cloud services also eliminates the overheads associated with Big Data infrastructure management. Since the service provider takes care of administrative tasks such as back-ups, disaster management, infrastructure health and maintenance, and so on, it saves you from a lot of distractions. This helps in cutting down hidden costs associated with maintaining an on-premise data infrastructure.
Helps in Reducing the Time-to-market New Ideas
Another major benefit of the cloud is that it makes your Big Data platform more agile. You can easily integrate more data sources and respond faster to changes in the data environment. The unlimited storage and elasticity of the cloud enable you to scale up quickly to meet the growing requirements.
Increasing data volumes with the constant addition of new data sources are one of the key reasons why organizations think about deploying their Big Data infrastructure. As per Kyvosâ€™ BI on Big Data Adoption survey, 54% organizations deployed their Big Data infrastructures to incorporate new data sources.
But as data grows exponentially, scaling up is not always as easy as it appears. Many big organizations who have an on-premise infrastructure in place, have already started looking at hybrid architectures to take advantage of the numerous benefits that the cloud offers. Besides this, cloud-based environments help smaller and mid-sized organizations to set-up their data platform easily and quickly without the need for massive investments and specialized skill-set. The same environment that needs months and years of effort and planning can be set up in a matter of days in the cloud.
With the cloud, anyone across the organization can access Big Data for BI from anywhere and on any device. Highly-accessible data not only improves decision-making but also increases adoption amongst the business users at all levels.
An organization that has already invested heavily in their Big Data infrastructure find it difficult to justify the move to the cloud. The management team may not be forthcoming with additional investments and need to be made aware of the benefits of the initiative. The data teams, who may not be comfortable passing control to third-parties, must be provided with enough reasons to see value in the transition.
Data security and governance are key factors that deter organizations from moving to the cloud. If your data is critical or sensitive, you need to address security carefully before planning the transition. Though most cloud environments offer secure models, implementing them the right way becomes vital to secure your data.
Despite the challenges, moving to the cloud is a trend that cannot be ignored for long. As more and more data gets accumulated, organizations need to focus more on Big Data analytics and making it work for their business.
Big Data Analytics in the cloud
Since the cloud stores data in a distributed manner, analytics is not always easy unless your BI tool supports and works well in the cloud environment. Even if your existing BI tools are not able to deliver on performance, you do not need to bring in new tools for cloud analytics. You can modernize your BI on Big Data platform by building an enterprise BI consumption layer and enable your existing BI tools to work seamlessly with Big Data irrespective of whether it is on the cloud or within the premise. This will ensure that the end users do not get affected by the move and adoption does not suffer.
If you want to learn more about how Kyvos’ disruptive BI on Big Data platform can help you achieve high performance at massive scale, request a demo now.
The bottom line is that the cloud helps you focus on what you want to do with your Big Data rather than where you want to do it.
To learn more about our cloud solutions, join us at the DataWorks Summit, June 17-21, 2018 at San Jose, CA.
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