In today’s world when everyone is battling to get better and better in their business it is very important for any organization to remain competent in the market and go one step ahead of what already prevails in the industry. To do so, organizations need to have a sound knowledge of trends and demands in the world. Organizations need to understand the customers and their needs. Organizations can do so by closely observing and analyzing their existing customer and understanding their behavioral patterns. Now, doing this is not a child’s play. Companies need to go through tons and tons of data produced over the years to understand how their products/solutions have impacted their customers. This tons and tons of data produced in any organization is termed as Big Data in the technical terminology. And mind you, Big Data is a Big Deal these days.
Big Data can be described as a huge Volume of data generated from a Variety of data sources at a very high Velocity.These three V’s make the processing and analysis of Big Data difficult and complex.All things considered, Big Data is being generated everywhere nowadays, so not having the capacity to utilize it effectively can crash organizations’ endeavors to drive their business ahead.
“Make use of Big Data because it is good for companies growth” this is the reason why most of the Big Data projects go towards failure. Working on Big Data just for the sake of it without a clear vision of growth and business requirement can be a major pitfall for all those organizations who wish to grow by putting their Big data to use. Here are some reasons why the majority of the Big Data projects fail:
- Lack of clarity on the END result of a Big Data Project:-As soon as Organizations begin working on Big Data they start focusing on how they will solve the problem and fail to understand why they need to solve it in the first place.Instead, if organization lay down the business objectives and goals of a Big Data projects at the very beginning then to achieve these goals it becomes a smoother process.
- No or Poor Planning:-Like someone rightly said “failing to plan leads to planning to fail”. This is especially true in the case of Big Data projects. Big data undertakings have the wide variety of scope of growth and hence the second most important thing that companies need to do is to plan ahead of time and remain prepared for all sorts of challenges and possibilities. From the smallest bug to major complexities everything needs to be foreseen and calculated even before starting with the projects.
- Lack of Skills:-Big data projects needs a lot of analytical minds working collectively and asking right questions to the data at the right time. Lack of analytical skills is one of the major reasons why big data projects derail. People involved in these projects come from an IT background and sometimes fall short of business knowledge and analytical mindset. People involved in these projects must be trained to think that there is no pre-defined methodology to be followed in big data projects, unlike IT processes Big data projects require rigorous mind storming of various possibilities in which the available data can be put to use.
- Complex Structure of data:- Very often organizations receive data that is in weird data formats and needs a lot of filtering and processing to actually make sense to the decision makers. Big data project faces this as a major hurdle in the way of implementation. Project workers spend a lot of time structuring the data in the required format for Big data Implementation. Also, some of the organizations store their data locally which generates a situation of Data Silos. To summarise all these points if data is not properly identified and structured in the early stages then Big Data projects turn out to be very time to consume and often exceed the given timelines leading to customer dissatisfaction.
- Management Resistance:-The prime decision makers of a company often fail to understand what advantages Big Data as the whole package can bring to their organization. There is an obvious reason for their resistance as Big data projects take up a lot of time, resources, and requires huge funding. If the management on board is not in agreement with the project team then there is every possibility the project will fail.
To conclude, Organizations must keep all these points in mind before jumping-in into Big Data undertakings. Like any other IT projects Big Data projects also require planning, setting up clear goals to achieve, a good and sound knowledge of skills required for Big data projects, Some basic investments in terms of server and other IT infrastructure required.
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