Big Data Challenges
With all the hype, it’s little wonder that organizations are getting caught up in the idea of having their own big data initiatives. But as promising as that idea sounds, the reality is that over half of all big data projects never reach fruition. And when it comes to on-premise big data initiatives, the majority are unsuccessful.
Clearly, there is a disparity between the idea of big data and the successful execution of a big data initiative in the enterprise. And the reason for that disparity is simple: implementing big data is challenging on a number of levels. If you are considering the idea of big data adoption in your organization, here’s a look at 3 major challenges to implementing big data that you need to be aware of.
Hadoop
The foundational technology supporting every big data initiative is the Hadoop analytics platform. Hadoop and its surrounding ecosystem of tools have been heavily hyped for their ability to handle massive volumes of structured and unstructured data to reveal hidden insights organizations can use to create a competitive advantage. And while the benefits of Hadoop adoption are many and varied, the reality is that implementing on-premise Hadoop is extremely difficult.
Not only is the software hard to manage, but the relatively new technology itself also presents a real challenge for data professionals that aren’t familiar with it. In addition, Hadoop often requires extensive internal resources to maintain. As a result, many companies that adopt Hadoop end up allocating the majority of their resources to the technology instead of the big data problem they are trying to solve. On-premise Hadoop is so challenging that a recent survey of data professionals found that 73% of respondents felt that understanding the big data platform was the number one challenge of a big data project.
Big Data
Big data projects can grow and evolve rapidly. Unfortunately, many organizations that adopt an on-premise Hadoop functionality fail to take into account that, sooner or later, their data storage and analytics demands are going to increase. That’s why it’s so important for those considering a big data initiative to realize that the solution they choose must be able to scale up and down on demand.
On-premise Hadoop analytics platforms rely on commodity servers, and that physical environment results in scalability problems and storage limitations. To solve these problems more physical servers must be added, and that can be expensive, time-consuming to procure in the enterprise, and disruptive to the project. In addition, big data workloads tend to be bursty, which makes it a challenge to predict where resources should be allocated.
While on-premise Hadoop might be the right fit for your organization, if you anticipate growing data demands you should definitely look into a cloud-based Hadoop solution, as Hadoop in the cloud offers faster and easier scalability to accommodate growing data demands.
Implementing Big Data
Successfully implementing big data is largely dependent upon getting the right people with the right skills. But as big data adoption accelerates, those people are getting harder to find. This is especially true for organizations that have adopted an on-premise big data solution. These functionalities typically require sophisticated teams of developers, data engineers, data scientists, and analysts who have the knowledge and skills required to identify actionable insights that create value and competitive advantage. Putting such a team together can be a painstaking and expensive process, and failing to do so can doom a project before it ever gets off the ground.
Organizations looking to implement a successful big data initiative that can solve the talent shortage would do well to consider partnering with a big data cloud vendor. Many cloud vendors provide their own educational resources as well as the bulk of the management that big data implementation may require.
The challenges to implementing big data are real, but so are the benefits. Those organizations that choose the right infrastructure for their big data needs can overcome those challenges, focus on asking the right business questions, and enjoy a significant competitive advantage.
