Big data, as the compendium of myriad disruptive technologies, is the next significant step in enabling integrated analytics in major common business scenarios. Big data is the next technological revolution in the field of information technology. It is maturing to a point where more and more organizations are getting prepared to pilot, making sure to adopt big data as a vital and integral component of their information analytics and management infrastructure. As big data makes its way into the enterprises, both IT practitioners and business sponsors are likely to bump into a number of challenges.





Challenge 1: Getting Data Into the Big Data Platform

The volume, variety, and velocity of big data is nothing unknown, but it can still prove to be overwhelming for the IT practitioner who is unprepared for the scale and variety of data to be absorbed by the big data platform. Analyzing massive amounts of data sitting on the big data platform is a highly complex process, and accessing and transmitting data from numerous data sources may lead to significant amount of mismanagement. The need to quickly be able to manage and analyze huge volumes of data often leads to overshadowing the aspect of providing seamless provision of data to the big data environment. Another challenge is navigating the response time expectations for lodging the data into the platform because making an attempt to squeeze gargantuan volume of data through limited bandwidth data pipes will lead to the degradation of the performance, as well as, impact the data currency. Hence, data accessibility and integration ranks as one of the top challenges of big data.

Challenge 2: Uncertainty Faced by the Data Management Landscape

One of the major challenges is to make the right choice amongst the various competing technologies, keeping the risk to a minimum. Big data implementation requires the use of data management frameworks that support both operational and analytical processing. These are usually referred to as NoSQL frameworks and they differ from the traditional RDBMS in terms of data access, storage model, etc. There is a huge variety of NoSQL frameworks. Some use hierarchical object representation using encoding methods like JSON, BSON, or XML associated with an object, whereas others use the concept of keys to enable a schema-less model. There are still others that maintain graph databases to associate different objects. Even within these NoSQL choices, there are numerous models that are being developed for various purposes, like providing flexibility, scalability, and a lot more. So, the endless choices of the NoSQL tools and the present market scenario render a high degree of uncertainty to the data management landscape and making a wrong choice will definitely prove fatal to the budget, as well as to the end expectations.

Challenge 3: Scalability Problems

Big data projects can have a very fast growth in terms of massive inflow of data and can evolve rapidly. However, most of the organizations do not realize the fact that sooner or later, their data storage, as well as their analytics demand, is going to increase. Hence, it is very vital for organizations to realize the need to scale up before making the choice of solution they are planning to adopt. For example, the on-premise Hadoop analytics rely on the commodity servers, and the physical environment eventually results in scalability problems and severe storage limitations. To solve this issue, you need more physical servers that will be both time-consuming and expensive and thus will prove fatal to the organization. However, in some other cases, the on-premise Hadoop might prove to be the perfect fit for the organization — and if there is any anticipation of data growth, then one should look for a cloud-based Hadoop solution, as it offers scalability to deal with the growing data demands.

Challenge 4: Synchronizing Data Across All Sources and Getting Useful Insights

Once you are able to get data in the big data platform, you encounter the next set of problems: dealing with the unsynchronized data. When data copies are brought in from various sources and at different times and at different rates, it has a possibility of rapidly getting out of sync with the originating systems. The inability to keep a proper check on the synchrony for the big data holds a tremendous risk on the analysis. It might use inconsistent data or potentially invalid data to draw insights, and the faulty analytical data will be passed downstream, allowing more inconsistencies and eventually having a disastrous effect on the big data environment. However, along with the drawing of insights, it has to be ensured that the data is adequately provisioned back within the other components of the enterprise architecture.

Challenge 5: Talent Shortage

Implementing the big data environment largely depends on the hiring of the right kind of people with the right experience and the right set of skills. But it has become one of the most cumbersome and tedious jobs to find people with the right caliber. This is mostly true for the enterprises who have adopted on-premise big data solutions and who require experienced data scientists and analysts who can identify actionable insights that will provide a competitive edge. So, building up such a team is a painstaking and expensive process.

Final Note

I would like to assure that, of course, the challenges of implementing big data are real, but so are the benefits. If you strategize and choose your big data solutions wisely — considering the future elements, as well — you will definitely reap the gold and enjoy the significant competitive edge that you desire!

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