How the Cloud is Changing Data Analytics

DLT’s Dave Rubal was interviewed for HP Enterprise's blog about cloud analytics, highlighting the trend of organizations investing and moving to the cloud, and how they can ease the transition of cloud migration as technology continues to transform. Please see clip and link to full article below. Thanks!

How the cloud is changing data analytics

Analytics are seeping into more functional areas of enterprises, often without IT's involvement and with mixed results.

March 14, 2017

By Lisa Morgan

Once upon a time, understanding and communicating business metrics was the job of the IT department or, more specifically, the IT function responsible for what we used to call “data processing.” But today, analytics is spreading out across organizations, fueled by business units and departments that want something better than what IT is providing, faster than IT can provide it. As a result, analytics are seeping into more functional areas of enterprises, often without IT’s involvement and with mixed results.

That has a downstream effect on who is managing which data, and under whose control. And it's happening primarily in the cloud. Most on-premises analytics vendors are adding software-as-a-service versions of their products, while the number of native SaaS analytics options continue to grow. Both vendor types are targeting business units and departments (in addition to or instead of IT) because the line-of-business teams are hungry for analytics and have their own IT budgets.

Why cloud-based analytics are so attractive

The scalability and elasticity of the cloud, combined with its computing and storage capabilities, enable organizations to work with larger data sets from which they can gain insights that were previously difficult or impossible to unearth. With cloud analytics, users can combine internal data in new ways, mix internal data with third-party data, and get predictive views of success levers, such as customer behavior and supply chain impacts as opposed to historical views only.

It isn’t all rosy, however. While SaaS-based analytics capabilities are attractive, there are some very real issues enterprise buyers should consider, whether they’re adding cloud analytics to an existing mix of on-premises solutions or migrating from on-premises solutions to cloud alternatives.

It’s also important to realize that different analytics options serve different roles within the organization, including data scientists, data analysts, business analysts, and business users. Sometimes, the attractiveness of one type of option is offset by limitations that hadn’t been considered.

For example, IT industry association CompTIA learned firsthand that some cloud analytics platforms do a great job of uploading data in its current format and displaying the data, but the software is not necessarily designed to do calculations. “If you have raw data and you want to show the mean, the median, and the range, you may want to do regressions analysis that not all analytics platforms are able to do,” says Tim Herbert, senior vice president, research and market intelligence, at CompTIA. “You may have to perform the calculations using whatever you’re using on-premises, Excel or database analytics, and upload it to the cloud.”

You're adding to on-prem solutions

Many organizations have on-premises analytics solutions and are adding SaaS analytics solutions to the mix. The cloud offerings likely need to be populated with data, more data than is practical to move over a network connection.

“You can’t do that over the wire. So you have to do it by another means, such as a box the vendor ships you, like AWS Snowball or AWS Snowmobile, which is a truck they drive to the customer facility,” says Mike Gualtieri, vice president and principal analyst at Forrester Research. “Once you do that, it’s not a big deal to move data back and forth because it’s drips and drabs compared to the original move.”

The city of Los Angeles uses Amazon Web Services (AWS) for cybersecurity analytics. According to CIO Ted Ross, Los Angeles is the biggest security target on the West Coast: It has the second busiest airport in the U.S., the largest port in the Western Hemisphere, and 4 million residents. With a high-profile police department—think of the number of TV shows and news stories in which it’s been featured—that makes it top of mind for hackers, who know any hacking success gains more notoriety.

“We’re ingesting 240 million records every 24 hours across 37 different departments,” Ross says. “It’s the proverbial haystack in which we have to find the needles that represent breaches. The cloud provides an effective mechanism at a reasonable cost for us to perform large amounts of data analysis, whether it’s cybersecurity or otherwise.”

Organizations with significant investments in on-premises hardware and software have to decide which analytics processes to migrate to the cloud, at what pace, and for what reason. 

“It’s a workload conversation first because it depends on what applications have been developed and when. Some of those applications may not be cloud-ready,” says David Rubal, chief technologist for data and analytics at DLT Solutions, a government-focused value-added reseller. “Technology is advancing so fast that there have been systems, applications, and databases that were developed years ago and [their creators] have gone out of business. So there’s an island in the IT environment [that requires] a separate migration path.”

When migrating from a traditional data warehouse environment to the cloud, unanticipated latency issues can arise. Latency can adversely affect application performance and therefore user experience, the timeliness of analytics, and even the accuracy of time-sensitive insights.

Also, endeavor to understand the comparative TCO and ROI for on-premises and cloud analytics. To optimize the respective workloads and investments, do your best to judge what each is best suited to. 

“If you’re putting something into the cloud, you’re not locked in. You need to be able to move workloads up or pull them down, so you can start off in the cloud and move on-prem if that makes sense, or start on-prem and move into the cloud,” says Ross. "A wise organization is always evaluating and always getting the best possible benefit.”

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