Cloud Computing + Data Analytics = Instant Business Intelligence
The cloud is the way to give millions affordable access to volumes of real-time, as well as the easy-to-use tools needed to make productive use of the data.
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Only by using cloud services will companies be able to offer their employees and managers access to Big Data, as well as the tools they'll need to analyze the information without being data scientists. A primary advantage of moving data analytics to the cloud is its potential to unleash the creativity of the data users, although a level of data governance is still required.
Data analytics are moving to the edge of the network, starting at the point of collection. That's one result of our applications getting smarter. According to the IDC FutureScape for Big Data and Analytics 2015 Predictions, apps that incorporate Machine Learning and other advanced or predictive analytics will grow 65 percent faster in 2015 than software without such abilities.
There's only one way to give millions of people affordable access to the volumes of data now being collected in real time, not to mention the easy-to-use tools they'll need to make productive use of the data. And that's via the cloud.
IDC also predicts a shortage of skilled data analysts: by 2018 there will be 181,000 positions requiring deep analytics skills, and five times that number requiring similar data interpretation abilities. Another of IDC's trends for 2015 is the booming market for visual data discovery tools, which are projected to grow at 2.5 times the rate of other business intelligence sectors.
As software gets smarter, more data conditioning and analysis is done automatically, which facilitates analysis by end users. Source: Software Development Times.
When you combine smarter software, a shortage of experts, and an increase in easy-to-use analysis tools, you get end users doing their own analyses, with the assistance of intelligent software. If all the pieces click into place, your organization can benefit by tapping into the creativity of its employees and managers.
The Yogurt Shop Model for Data Analytics
In a November 19, 2014 article, Forbes' Bill Franks compares DIY data analytics to self-serve yogurt shops. In both cases, the value proposition is transferred to the customer: analyzing the data becomes an engaging, rewarding experience, similar to choosing the type and amount of toppings for your cup of frozen yogurt.
More importantly, you can shift to the self-serve model without any big expenses in infrastructure, training, or other costs. You might even find your costs reduced, just as self-serve yogurt shops save on labor and other costs, particularly by tapping into the efficiency and scalability of the cloud.
Employees are more satisfied with their data analytics roles when their companies used cloud-based big data analytics. Source: Aberdeen Group (via Ricoh).
Last but not least, when you give people direct access to data and offer them tools that let them mash up the data as their creativity dictates, you'll generate valuable combinations you may never have come up with yourself.
Determining the Correct Level of Oversight for DIY Data Analysts
Considering the value of the company's data, it's understandable that IT managers would hesitate to turn employees loose on the data without some supervision. As Timo Elliott explains in a post from April 2014 on the Business Analytics blog, data governance remains the responsibility of the IT department.
Elliott defines data governance as "stopping people from doing stupid things with data." The concept encompasses security, data currency, and reliability, but it also entails ensuring that information in the organization gets into the hands of the people who need it when they need it.
Published at DZone with permission of Darren Perucci, DZone MVB. See the original article here.
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