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Big Data has the objective to automate the delivery of considerable business insights from data. So as to do this, you always end up needing different data sources, large data sets and a vast amount of computational power. But, most are indications of an approach, not requirements of the objective.

This repeatedly drives higher management to concentrate on tools used by competition in place of giving stress on why the competition is using that tool in the first place and what steps are needed to end up in the same group.

To highlight this, you can take the points from the 2013 post, “why does SCRUM fail?” and also substitute SCRUM with Big Data to highlight this failure.

Big Data, undoubtedly, is one of the most feasible and potentially relevant approaches to managing insight development projects. Being a powerful automated approach, Big Data has numerous advantages and that is why a growing number of companies in the recent past have either implemented or have been planning to implement Big Data; But the implementation does have its baggage. As the facts and figures recommend that Big Data has been successful in developing and delivering high-quality, business-valued insight, there have been examples where it has failed to bear fruit. Data Scientists as well as Data Strategists across the globe have been using their brains to find the reasons how and why the technology does not deliver.

An analysis of stories about some failed Big Data projects uncovered the following:

Big Data is neither a Magic stick nor a Silver Bullet — The publicity that comes with the promotion of any new approach or technology has led higher management to harbor the statement that Big Data is a silver bullet. However, to be exact and practical, Big Data is not a magic wand or potion that puts an end to all types of problems. Big Data is just a methodology which defines the processes and practices that help in managing large data processing. No, process, technique, or methodology will solve all your data analysis problems. Though tempting, expecting a single, silver bullet like, solution that will kill all insight problems is impractical.

Wrong application of Big Data can guide its destiny — Big Data is not a narrow method, rather a suggestive approach to data analysis. So, the method it is implemented makes all the difference. The team practicing Data Science should be well conscious of Big Data principles and of Big Data’s appropriateness to the problem at hand. People implementing Big Data should know its strengths and weaknesses. Uncertainty and indistinctness about either the approach or capabilities or both can result in confusion and redraft — bigger production cost and late delivery on commitments.

The problems underlined by Big Data require to be solved — One of the important advantages of Big Data is that it discloses problems at quite an initial stage in the development process; but, “knowing is half the battle.” The more vital task is to resolve the problems and make an effort to eliminate the obstructions which have risen. On the other hand, it has been seen that some companies do not try to deal with the problems; these problems are either ignored or concealed until the implanting phase of the project. It is actually this procrastination which causes delays in release or failures to fulfil commitments.

Lack of a capable and competent project team — A highly skilled and a competent project team can implement the technology efficiently and successfully. All the functions: Engineer, Scientist, Architect, and IT support, should be aware of the Big Data principles and help them as effectively as possible. In addition, the team members should be technically sound and experts in their fields, that is, the scientists should have expertise in the technologies to be used, while the engineer should possess all the appropriate and valuable business information about the systems required to embed the result.

Part of the successful execution of this technology depends on the team members being “generalists/specialists;” they should be skilled enough to facilitate a smooth transition from the domain-specific concept towards automated embedded insights.

Lack of skilled and visionary Data Scientist — The Data Scientist is accountable for introducing new ways of coming across at the data and verifying its value. He/she is responsible to maximize the value and repeatedly push the requirements of production to their limits. So, it is necessary that the Data Scientist is competent, experienced and visionary.