Understanding Big Data Better
If you have been exposed in the IT industry for a long time, then there is no doubt that you have heard the words ‘big data’. You can see that there are just a lot of people who make mention of this concept just to get a better image in the world of information technology even if they are at a loss for words when asked more to expound about the topic. Most of the time, the term is misconstrued, and it has become a major gimmick to market the company in more ways than one. Luckily, you can learn what you can about big data here and then learn more of its being useful in being used as a tool to solve a number of problems.
Mathematics and Physics are the two things that help in calculating what exact distance can be obtained from the West Coast to the East Coast of the country. These two things have made it very much possible for the great achievements that are being used across technologies as people live their lives on a daily basis. What then becomes challenging will be the taking of measurement of data that is not static. What makes non-static data very difficult to obtain is their being able to change rapidly in volumes and rates that get to happen constantly and in real time. Utilizing some computers seems to be the only viable option in being able to process such crucial date.
According to IBM data scientists, big data can be broken down into four aspects, they are called veracity, velocity, variety, and volume. But then, big data cannot just be classified into these four factors, there are still other factors that are part of it. Here are some of the descriptions of big data that you need to know.
To ascertain if the data that you have is called big data, its volume will be assessed that is its data size to ascertain what its potential and value are. With big data, data analysts must make sure to look at what classification the data is a part of and this the aspect of variety. This is proven to be very helpful in finding out what the date is all about and what might be associated with it. Such data has been proven to be very beneficial among these people for use to their own advantage in more ways than one. Velocity is then more about finding out how to put to good use how fast the processing and generating of data are being done. The aspect of variability is also crucial to determining what problem data analysts might be coming across. And finally, you have veracity that identifies the captured data quality. For accurate assessment of your big data quality, it will have to depend on how much veracity your source data has.