Realizing the Power of Cloud-Based Data Analytics

By CIOReview | Monday, July 10, 2017
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Today, data has become a fuel that runs every business unit. Be it customer service or order processing, every department within an organization and every decision is supported by the data. Data analytics plays a crucial role in processing large streams of data sets and presenting rich insights that take the business decision making process away from traditional guesswork or gut feeling. The role of data analytics grows with the advent of technologies such as IoT and mobility as data generation has been witnessing an exponential growth.

In sync with the business requirements of this age, enterprises execute most of the processes using cloud based tools and services. From proposal generation to finally product delivery, they conduct and monitor every task in the cloud. The cloud native aspect plays a crucial role in data generation in an unprecedented scale and necessitates altogether different strategies to process voluminous data.

Businesses rely on on-premise data processing, which is an inherently non-scalable method. As the business requirements include faster data processing that may contribute to quicker decision making, the situation leads to deployment of tools and resources that would facilitate quick data processing. Operational limitations entail businesses to adopt Big Data as a Service (BDaaS)—a cloud based service. Migration to BDaaS opens up many avenues for the organizations and helps developers and system architects to build applications that would deliver services in real time after discerning crucial business conundrums. Migration to BDaaS provides an added benefit to the organizations as they no longer have to be dependent on IT teams for provisioning of additional resources. After moving data to the cloud, analytics becomes simpler to perform and different analytical models can be run to analyze the data in real time.

How to move further?

As the data comes in different forms namely distributed, non-relational, and relational, companies have to ponder over the best strategies to limit the complexities that might arise from the adoption of data model. Most enterprises create a relation-based database and run SQL queries to gain insights. However, this approach might fail. Data visualization remains a gargantuan task while dealing with structured data. If the applications are using non-structured data, which is stored in numerous clusters distributed on a network, then Hadoop and NoSQL emerge as the best options. If the data is organized in tabular format then firing queries becomes a tedious task.

While finalizing over the usage of SQL, NoSQL or Hadoop, businesses must give due attention towards the selection of cloud provider. Though there are established names such as Rackspace and Amazon; Qubole, and Cloudera are the other options that simplify the maze of data management.

Companies should also ponder over running data analytics on their own cloud infrastructure. Instead of switching between the cloud services to perform data analytics, running analytics on the self-hosted cloud brings down multiple intricacies and simplifies data hosting and management in the preferred format. Again, self-dependence in terms of data processing helps enterprises to constrain their processing expenditure as well as ease of running of instances.

Finally, businesses must invest more time and efforts to understand needs and relate them with the limitations and implications of deploying the data in public cloud. Though numerous parameters are associated with the selection of cloud service, frequency of firing queries, data structure hold keys to success.