How to Optimize Analytics for Growing Data Stores

How to Optimize Analytics for Growing Data Stores

How to Optimize Analytics for Growing Data Stores

Every minute of every day, mind-blowing amounts of data are generated. Twitter users send 347,222 tweets, YouTube users upload 300 hours of video, and Google receives more than four million search queries. And in a single hour, Walmart processes more than a million customer transactions.

With the Internet of Things accelerating at lightning speed – to the tune of 6.4 billion connected devices in 2016 (up 30 percent from 2015) – this already staggering amount of data is about to explode. By 2020, IDC estimates there will be 40 zettabytes of data. That’s 5,200 GB for every person on the planet.

This data is a gold mine for businesses. Or, at least, it can be. On its own, data has zero value. To turn it into a valuable asset, one that delivers the actionable intelligence needed to transform business, you need to know how to apply analytics to that treasure trove.

To set yourself up for success, start out by answering these questions:

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What Is the Size, Volume, Type and Velocity of your Data?

The answers to this will help you determine the best kind of database to store your data and fuel your analysis. For instance, some databases handle structured data, and others are focused on semi-structured or unstructured data. Some are better with high-velocity and high-volume data.

Which Analytics Use Cases will You Be Supporting?

The type of use cases will drive the business intelligence capabilities you’ll require (Figure 1).

Analyst-driven BI.  Operator seeking insights across a range of business data to find cross-group efficiencies, profit leakage, cost challenges, etc.

Workgroup-driven BI.  Small teams focused on a sub-section of the overall strategy and reporting on KPIs for specific tasks.

Strategy-driven BI. Insights mapped against a particular strategy with the dashboard becoming the “single source of truth” for business performance.

Process-driven BI. Business automation and workflow built as an autonomic process based on outside events.

Where Do You Want your Data and Analytics to Live?

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The main choices are on-premises or in the cloud. Until recently, for many companies – particularly those concerned about security – on-prem won out. However, that’s changing significantly as cloud-based solutions have proven to be solidly secure. In fact, a recent survey found that 40 percent of big data practitioners use cloud services for analytics and that number is growing.

The cloud is attractive for many reasons. The biggest is fast time-to-impact.


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