Do you want to leverage the power of big data, but believe that data analytics platforms like Hadoop and Spark are too complicated for you to set up and use? Then cloud analytics (which allows you to run big data analytics with minimal overhead and setup) are just the thing for you.

Here's why you need cloud analytics, and how you can get started with it:

Don’t Pay for Data You Don’t Use

Bernard Marr of Forbes makes two important observations about the way companies tend to use data today:

The first is that businesses will see a 4,300% increase (no, that’s not a typo) in the amount of data they generate by 2020. That’s a huge jump — especially when you consider that your business is already generating a hefty amount of data today from machine data, emails, files, connected devices, and so much more.

Marr’s second observation is that, 'on average, companies use only a fraction of the data they collect and store.' In other words, organisations spend a lot of time and money collecting data, then store it somewhere, yet they let that effort go to waste because they fail to translate most of the data into value.

Taken together, these two points suggest that data is severely underutilised by most organisations, and that the problem is only going to get exponentially worse over the coming years as the volume of data that companies store reaches new heights.

Reasons for the Big Data Drain

There are lots of reasons why companies spend a lot of money to collect and store data that they don’t use. Organisational bloat, lack of direction, data quality issues and other factors are all at play.

But likely the biggest reason is that the tools required to analyse large amounts of data have traditionally been difficult to install, maintain and use effectively. Any seasoned admin can set up a web server or a storage array. But installing Hadoop, Spark or any of the dozens of other big data and analytics tools in existence today requires specialised expertise that tends to exist only among people with specific experience in the big data field, and many companies don’t have these dedicated big data expert inhouse.

One solution to this problem is to use a commercial distribution of open source big data platforms. Commercial distributions are designed to make tools like Hadoop and Spark easier to set up and use. The problem with this approach, however, is that commercial distributions don’t completely solve the problem. You still have to install them, you have to maintain infrastructure to host them, and you have to learn how to use them. Plus, the commercial distributions themselves can be costly. So you end up paying a lot of money for open source big data software that you could download in raw form for free.

Cloud Analytics Are the Solution

A much better approach to leveraging the value of the big data your company stores is to turn to the cloud. Today, providers like AWS offer feature-rich big data and analytics tools based in the cloud. They require no setup and very little maintenance, and are designed to be extremely user-friendly.

Cloud analytics are not completely free. You have to pay for the cloud service. But for what you pay, you get both infrastructure and big data software that is immediately actionable. Overall, your costs will be much lower than if you had to purchase, set up and administer your own on-premises servers to host a big data environment. Plus, unlike a commercial distribution of Hadoop or Spark, cloud analytics give you infrastructure and user-friendly analytics tools in one convenient package. You get much more value.

So, if your business is sitting on reams of data that it is not using, it’s time to embrace cloud analytics. They’ll enable a return on the significant investment you are making in data collection and storage, and they’ll help you prepare for the data deluge that will come as the amount of data organisations store and generate reaches new heights.



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Benjamin Wootton

Co-Founder and CTO

Benjamin Wootton is the Co-Founder and CTO of Contino. He has worked with tens of enterprise organisations on DevOps transformation and is a hands-on DevOps engineer with expertise in cloud and containers.

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