Big Data

3 ways data and analytics will evolve in the cloud

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wpid-thumbnail-afa15d476a005bf9000490965cd5403e-500x449For cloud data analysts, 2015 was all about setting the foundation for the next era of managed services. This year, we’re making data analytics available at the customers’ fingertips, making it easier to consume and use the insights that the data provides.

What Cloud Foundry did for application programmers, we’re providing for data analysts.

As the cloud matures, we’re bringing more of our managed data/analytics services to cloud, and enterprise customers are realizing the value of moving their workloads to these platforms.

This will only progress in 2016, and we’ll see three major evolutions for data and analytics in the cloud.

Cloud as a security solution

If you had asked a sampling of CIOs or CTOs in 2013 about their organization’s biggest hindrance in moving to the cloud, it’s more than likely that security concerns topped their list.

How times have changed.

The cloud has actually become a security solution, rather than a liability. Enterprises are finding that as hackers become more sophisticated, keeping data in-house can present greater vulnerabilities than relying on a cloud vendor.

It boils down to a simple question:

Can your organization afford a security expert (or team of experts) that would be better able to lock down machines in your building better than a cloud vendor can lock it down in, say, in a SoftLayer data center, which employs a host of security experts?

Reliance on real-time analytics with the evolution of Spark

Spark allows real-time analytics to immediately take data and provide actionable insights. In 2015, we saw a rapid adoption of Spark with analytics, but it’s the tip of the iceberg for its applications and ability to process a variety of data.

This year, we’ll see Spark applications evolve and applications built on top of these cloud data services.

As we unlock data from on-prem silos, Spark will be used to find patterns, predict behaviors, and facilitate real-time and preferably automated responses. While Spark really came to market in 2015, we’ll see an explosion in 2016 as demand for real-time analytics increases.

Data as a first-class citizen in the cloud

One of the most significant challenges with cloud today is getting the data into the cloud. In 2016, we’ll see a rapid evolution of our ability to get data into the cloud and we’ll treat it as a first class citizen.

What do I mean by this?

When you put data into the cloud, customers don’t just want to target one consumer for analytics; they want to be able to access data from the entire breadth of their consumer base. Previously, the application served the anchor point in many environments. For data-intensive analytics, the data becomes the anchor point.

Once data is moved to the cloud, pre-processed and cleansed, the data set remains constant, and opportunities open up for what more can be done with it. It can be processed in phases, and other analytics can be applied as the data is refined and used to generate other enriched data sets.

Treating data as a first-class citizen means that instead of looking at services as an anchor point in the catalogue, the service should be in the background.  The consumer should be able look at their data right away and know what analytics are available to enrich or draw insights from the data.  This is all part of a cloud data services experience.

Bobbie Cochrane is a Platform Architect for IBM’s Cloud Data Services. Follow her on Twitter: @BobbieCochrane


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