The difficulties and challenges of managing a big data project are many, and unfortunately, failure is the result more often than not. Though businesses may give their projects all their attention, certain shortcomings will usually lead to them struggling to achieve their goals. This can be seen in a recent study from Capgemini, which discovered that only about a fourth (27 percent) of all executives who were surveyed say their respective big data initiatives could be deemed a success. That’s a high rate of failure in any industry, one that needs to be improved upon. One reason for this failure is the simple fact that big data, and the tools for analyzing big data, can be extremely difficult to work with, often requiring the investment of a large number of resources, not to mention time and effort. In fact, many companies are hesitant to adopt big data tools like Hadoop for fear of having it consume their businesses with no guarantee of a return.
This fear is understandable to certain extents. After all, big data projects will often fail because the people on staff simply aren’t skilled in big data analytics. Big data is complex, requiring intense study to truly understand its inner workings. Compounding the matter is the simple truth that access to big data within an organization is typically limited. Only a select few have access to the important information businesses collect, resulting in more strain on those chosen and a virtual data blackout for the rest of the company.
Some companies may choose to resolve this issue by training up their employees to be more adept at handling big data, but this once again takes time. In some cases, it may take too much time, resulting in lack of collaboration among employees of varying skills levels. In a world where businesses are looking for the best time-to-value with big data projects, delays as a result of lack of expertise can be crippling.
Going beyond that issue, companies also have to contend with the time needed to expand their own on-site big data capabilities. Particularly concerning is the time it takes to build in-house infrastructure. That time can range anywhere from six to nine months depending on the business. Expanding in such a manner is also a costly endeavor. Big data projects have to scale according to a company’s needs, and that also means having more employees on hand to handle the extra workload. When deploying Hadoop on-premise, the more large clusters there are, the more people are needed. It’s a tall order to meet, especially in the face of a data talent gap many within the industry are experiencing.
Considering these challenges, it’s easy to see how Hadoop can quickly consume all of a company’s time and energy. Though big data offers a lot of potential, the problems may seem too tough to overcome. Luckily, businesses have a helpful option available that may act as the right solution. This option comes in the form of Big-Data-as-a-Service (BDaaS). Put simply, BDaaS is able to turn the complex nature of using a big data tool such as Hadoop and boils it down into a simpler form. This allows companies to focus more of their attention on specific data problems and not the complexities of big data analytics. BDaaS can also facilitate collaboration within an organization. This leads to better insights since businesses are getting a wider range of perspectives, but it also reduces the chance that certain efforts will be duplicated, which saves overall on time, money, and resources.
Big-Data-as-a-Service greatly simplifies scaling a Big Data project up or down. Whereas on premises expansion requires months to enact and does not easily scale down, using a service from a big data vendor provides elasticity. Big data services also give organizations access to the latest big data tools available. This, in addition to support that aids in reducing a business’s skills gap, helps to make big data more manageable, essentially eliminating the difficulties that come from using Hadoop and other similar tools.
It’s easy to see how Hadoop could consume a company. Without the right expertise on hand, and limited resources available, things can get difficult quickly. But with Big-Data-as-a-Service as an option, companies now have the capabilities of using tools like Hadoop for the benefit of their businesses. It’s a savvy way of overcoming those challenges that often doom big data projects.
Review other crucial considerations when selecting a big data vendor in this full infographic.