Anything involving data processing requires the movement of information, and moving stuff is not fun. No amount of pizza or beer can entice friends and relatives to help you on a moving day – they conveniently have other plans but are happy to pitch in any other day.
Enterprises have a similar, possibly dreadful proposition. There is a ton of data everywhere, which can be incredibly cumbersome to deal with when needed. Just like on moving day, where a basement full of unused exercise equipment or overstuffed closets of memorabilia that you just can’t let go of ends up going with you instead of being tossed, enterprises typically have huge data sets that can be anywhere in the data center or at a remote, physical site for archival data.
Maintaining all of this might be useful for an auditing requirement or a backup and restore scenario, while other data is stuck in random places because no one has time to figure out what to do with it and the effort’s not worth the expense.
Big Data for the Enterprise
But wait! Many enterprises are now upping their game for addressing the value of data because of how much easier it is to get and store. The ability to get data from just about anywhere and to do more comprehensive analysis can help with new digital initiatives. Although this data is no fun to move or maintain, it’s a lot cheaper than it used to be. Data scientists can now mine both real-time and historical data a lot easier than ever.
Insights from what is known as big data drive anything from improving booking rates for hotels to reducing errors in cancer diagnosis. Big data was first defined as the combination of data volume, velocity, and variety, and a fourth quality is now been introduced – veracity.
To put it simply, big data gives you the ability to get value from super-large data sets. The value can come from analyzing multiple data sets in parallel in order to find correlating factors, discover anomalies, and predict outcomes. Typically, this is done using machine learning algorithms that look for specific patterns that reveal which information is not necessarily evident from traditional statistics used to quantify minimums, maximums, and averages of data sets.
For example, a big data insight would be something like this: someone who typically buys rain gear during the summer months is more likely to book a vacation rental in a wilderness setting. A travel site would then figure out how to target advertising for this more qualified potential customer.
More everyday companies using big data include Netflix, where big data helps to suggest what movie you should watch next, or Starbucks, which crunches numbers to determine where new stores should be located, in some cases placing them only blocks from each other. Most folks might have a hunch that having the same store in the same proximity would be a mistake but the big data insights tell a different story.
Key Components of Big Data
The key components of big data for enterprises require data sets and analytics from software designed to execute the necessary algorithms. Apache Spark and Hadoop clusters provide the processing environment for the large volume of data.
Big Data for Hybrid Cloud
A lot of enterprises that first started working with big data initiatives did so on-premises. Big data typically needs infrastructure that requires a lot of attention. You have to set up Hadoop clusters and have Apache Spark in a separate networked environment, and then IT has to maintain it. This can require petabytes of storage, a capital expenditure (CapEx) required for getting it all set up, and an operating expenditure (OpEx) for all the expertise needed just to manage the data flows regardless of any analysis.
Recently, enterprises have begun moving their big data processing entirely or partially to major public cloud providers such as AWS, MS Azure, and GCP. The advantage is that this eliminates the CapEx for the computing environment, giving them the ability to scale on demand. Specialized knowledge for maintaining the environment is no longer necessary.
Enterprises realize that their existing attempts on-premises, although had great intentions, do not provide the same value that big data processing can do all or in part in a public cloud. This also provides the perfect use case for a hybrid cloud strategy were some of the data sets can still remain on-premises, regardless of where the processing is taking place.
Enterprises have also considered moving all or part of their data sets to public clouds into what are called data lakes for big data processing where a combination of many kinds of data can be accessed easily allowing insights to deliver value in real time.
CloudBolt Helps with Big Data
IT leaders turn to CloudBolt for all things hybrid cloud, and big data is no exception. Big data initiatives can often span the hosting of the resources either on-premises or in a public cloud, and data can be accessed in any part of the enterprise ecosystem. Please refer to last week’s post about Hybrid Cloud and Enterprise Digital Ecosystems for more information.