Tackling Today's Top Big Data Pain Points, Part 1: A Sea of Data

A Sea of Data

Big data is now a more important part of corporate processes than ever before. Insights gathered from the analysis of this information can be used to inform critical business decisions, identify trends to get ahead of the market and address a range of other corporate needs. However, as big data continues to come to the forefront in companies across nearly every industrial sector, a few pain points have emerged alongside this practice.

While a challenge, these difficulties are pretty par for the course for any innovative technology strategy. However, it's in the best interest of the business as well as the success of its big data initiative to identify these obstacles and address them as quickly as possible.

Thankfully, we at Data Realty are here to help. As experts in the data storage and management field, we're in a unique position to provide a bird's eye view of the big data industry, as well as the common pain points that frequently come up. Over the course of these series, we'll be taking a look at the top problems associated with big data processes, as well as the best ways to address them. In this first part, we'll examine certain challenges that emerge as part of the sea of data your business is likely working with. Read on, and we'll elaborate:

A sea of data: Volume

When we say "a sea of data," we mean exactly what you'd envision: large, expansive repositories of data spanning a whole host of corporate processes, information and activities. What's more, in most settings, additional information flows in nearly every day, similar to a river flowing into the ocean – the tide of data seems to never stop.

While volume is certainly an integral part of big data – it is the information's volume that puts the "big" in big data – problems can come to the surface when companies look to analyze and deal with their seas of information. This issue shines through particularly when it comes to the expenses involved in processing big data, and doing it in a way that will be valuable and meaningful for the organization.

After all, it's not simply about gathering as much information as possible. The details collected must be tied with critical business functionality, and be able to provide the necessary insights for decision makers. At the same time, this data must be analyzed quickly to ensure that the knowledge and trends identified aren't outdated or irrelevant.

"A common misconception is when customers confuse the term big data with having to deal with lots of data," Dave Beeston, Portal's head of information management, told Computer Weekly. "Volume is clearly part of a big data solution, but big data is more about unlocking the potential of structured and unstructured information, inside and potentially outside of our firewall, and doing it in 'right time.'"

How much is too much?

This begs the question, "How much big data is too much?" As senior Hadoop consultants wrote for Hadoop in Real World, this is very much a moving target, and will depend on the organization's industry, its needs and what it is leveraging its big data for.

"First, what is considered big or huge today in terms [of] data size or volume may not be considered big a year from now," Hadoop in Real World stated. "Second, it is all relative. What you and I consider to be 'big' may not be the case for companies like Facebook and Google."

For this reason, it's important that decision makers take a step back and ensure that they have full control over their big data repositories. If information has been sitting within a database without being used for a long period of time, or has become outdated, the issue of volume might be impacting your organization, and it could be time for a new solution.

Disparate data: Multiple storage locations

In addition to possibly having too much information, problems also arise when it comes to the storage and management of this data. Often, businesses are so focused on the collection of usable statistics and details that they don't pay much attention to their storage methods. This can create considerable challenges, especially when data becomes disparate and is stored in several different locations.

Not only does this make visibility and management of information difficult, but can also create gaps in knowledge or skewed insights if data is missing from analysis due to the company's disorganized storage.

In order to make the most of their big data, those in charge of specific initiatives must have a full understanding of the details the company has gathered, as well as where this information is stored. In this way, those leading the big data charge will know what details need to be pulled from certain databases to ensure analysis reveals the best insights possible. This process is made much easier when the business has a holistic, organized strategy to storing its structured and unstructured data.

The solution: Data Gateway and Hadoop Data Hub

If you're one of the many organizations dealing with a high volume of data stored across disparate environments, Data Realty and our partner Aunalytics have the perfect solutions to address these exact pain points. Best of all, this technology comes from experts in the big data industry, helping to ensure that your business is in the best position to achieve success.

Data Gateway is a unique solution from Data Realty that helps organize data from disparate sources. Whether you have a sea of information sitting across multiple environments or just a trickle of data in a small number of databases, Data Gateway can ensure that all of these details are collected in a single location with a built-in firewall, ready for analysis. From here, Hadoop Data Hub – an optimized, hosted cluster of compliant, integrated hardware and software elements – takes over for analytics activities. This critical asset ensures that not only is data aggregated and organized, but that every piece is cleaned and adequately prepared to be analyzed for valuable insights.

To find out more about Data Gateway and Hadoop Data Hub, contact Data Realty today. And tune in for the next part of this series, where we'll discuss other big data pain points and the best ways to address and resolve these issues.