Common mistakes that firms make in Big Data projects

Have you ever thought that how the voluminous amount of data will be stored in organizations?  If data can fit into some word document then we can say data is small. If data is made to store in hard disk then data is medium. But what do we say about data that is being stored across server or some hardware/software?
Every two days, we create as much information as we did from the beginning of time until 2003” – Eric Schmidt (Google)
Well, that sounds very interesting! That data would be something around 5,000,000,000,000,000,000 approximately.
What Is Big Data:
There is no particular definition for big data. It refers to the technology which is generally used to extract information in organizations. It can extract structured,unstructured and semi structured data.
In present scenario data is multiplying at a rapid speed and it becomes difficult for organizations to store large amount of information. Big Data helps you in doing that. Today 90% of data which is existing has been created in last two years.
Understand the importance of Big Data in industries.
In today’s competitive world it is very important to understand the relevance of big data. It is available for us in three different forms:
Semi-structured Data:

  • A semi-structured data is a data that has not been arranged/well ordered in a systematic way. Further it becomes difficult to extract information. For example: Data Base.

Structured Data:

  • A structured data is a data that endures in a particular field that makes easier for us to access the information whenever required. For Example: Legal records, phone numbers/ phone book.

Un-Structured Data:

  • Un-structured data is essentially the opposite of structured data. It is not well organized in a predefined manner. For Example: Word Documents, videos, photos.

Do not miscalculate the data quality:
Poor data quality results in destroying the essence of the data, especially in big data projects. The implementation of structured data, unstructured and semi structured data into data sets can reduce the data quality to large extent. This makes to understand the brunt created by data quality in big data.
Strategy for Data Preparation should be accurate:
Big data in organizations needs preparation of data in prior. It is very crucial to provide some additional inputs to meta data. In most of the firms, people ignore preparation steps that tells how acquired data has to combined with meta data. This often results in problems for big data operators and users.
Not realizing organizations maturity:
The success of any project related to big data is regulated with the team that drives the program to be successful. The team should have basic knowledge on data and domain of the project to deliver the proper outcomes that can further lead to success of the project.
Eventually proper planning and learning can make the big data project successful.

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