Big Data-Big Blunders

 Big Data-Big Blunders

Companies find that big data does not always translate into easy success, but involves a lot of processing for effective utilization. Companies which specialize in computer driven analysis of large streams of information are prone to many mistakes. This blog talks about the top 5 mistakes a company makes with big data and some of the remedial measures to allow for their shortcoming.

1) Data for Data’s sake

Each project or venture, a company undertakes should have a clear cut objective stating its goals, paths to achieve this goal, questions to answer and so on. They are sometimes under the impression that big data alone can solve their problems and give an understanding. Technology is not a substitute for leadership when it comes to deciding data objectives. Data should be gathered with an objective to carefully analyze and find what one is looking for.

2) Talent Gap

The McKinsey Global has estimated a high demand for employees skilled in data analysis which would outstrip the current supply by 2018. Big data is no longer in the infancy stage and people are required everywhere to start prepping their talent skills in data analysis. This is a skill which can be used across various platforms of the business. Educational institutes should set up certification programs in data sciences to ease the shortage. With the advent of technology it will become easier for non-experts to utilize sophisticated statistics and put them to effective use. Companies can develop their internal talent pool by giving existing analytics specialists in big data training.

3) Data is Everywhere

Many companies collect realms of data but fail to keep it organized. Big data software may be able to link the data fragments together but this takes money, time and resources away from the main goal of the project. Companies must perform some sort of a ‘data audit’ to ensure that the data is in a single format and single database and is easily available for data mining.

4) Infighting

With the advent of so much data and public knowledge, it could result in internal friction within the company. Big data initiatives can result in haphazard decisions. Companies must effectively develop a management layer with a key management group in charge of analytics at each wing and they in turn must work in harmony with the various departments in the company.

5) Aiming too high

Many companies start out with expensive, high risk and unrealistic data initiatives. Often a complex problem requires a more traditional approach rather than purely relying on data. It goes on to say just by implementing Big data analysis should not change the

earlier goals of the project. Big data can be used to aid the problem not change the course of the problem. It helps build confidence and sometimes see tangible results quickly.

Source: The Wall Street Journal