Data has grown to become an integral part in the operation of any industry – big or small. The reason being fairly simple: data is gold. But just like gold, it takes a lot of time and effort for one to sift through a large chunk of waste and ultimately mine the gold from its source. But in the end, all that time and effort put into that mining is worth it. Same is the case with data. One needs to refine their data set and put in efforts in order to finally extract the information that they are actually looking for under a mountain.


In order to do just the exact thing and extract meaningful and useful chunks of data hidden underneath a load of junk, the world saw the inception of a whole new field of study dedicated to doing just that: the field of data analytics. One that makes use of big data, machine learning, and a couple more technologies and brings them together in one place in order to enhance decision-making, solve regressions, generate ideas etc. But doing so is not really as easy as it sounds. There are a lot of aspects in the data set that one needs to maintain in order to carry out their task. But what really are these aspects?

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This aspect describes that the data for the analytics should be concise and pertains to the topic or the standard on which the desired analytics has to be performed. At no point should the data set contain information or description of topics that are nowhere even related to the goals of the project. If relevance is not maintained in the data, apart from the desired results, the analysis will also yield outputs that will make absolutely no sense and that are totally unrelated to the desired results.


The data in question should be complete in all regards. Meaning to say, that the data should have no gaps or voids in it that will ultimately lead to either inaccurate or incomplete results in the desired analytics. A complete set of data measures, describes, as well as calculated all the aspects that were given as the basis for the analytics. Finding and filling such voids in the data is both tiring as well as time-consuming.

  1. CLEAN

Data being clean essentially means that out of all the randomized data that was collected, it has been now put together in a proper and structured manner. Redundancies and key duplication also make a data unclean. Unclean data does not hamper with the analytics process as such, but it definitely brings about fallacies and irregularities in the final output. The final product will be inconsistent, violating the principles of normalization, as well as will make the output difficult to read.


The timestamp on the data is also a pivotal part of data quality. The recentness of the data makes sure that the analytics of the data generates results that are up to date and correlate with the present-day workings of the world.


Playing with data is fun and furthermore, a lot rewarding in the current market scenario. Wanting to put that logical mind to work and earn at the same time? Join a data science training in hyderabad, the field of data analytics is waiting for you.

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