Big Data Needs to Be Clean Data

BigData Needs Clean Data

Businesses are flocking to the cloud, and it’s no wonder. Cloud platforms are the perfect solution to avoiding hardware installation and maintenance costs.

On top of that, with cloud services you can easily add or remove resources. You can process and store data fast. That’s especially helpful when you need to handle “Big Data”.

Big Data and Its Forms

Big Data refers to the mammoth amount of data that businesses nowadays receive from different sources on a daily basis. In fact, 2.5 quintillion (that’s 18 zeros by the way!) data bytes are created on a daily basis.

The sources of Big Data can be:

  • Mobile devices
  • Smart devices
  • Sensors
  • Social media
  • Transactions, etc

Big Data can be unstructured, semi-structured, or structured. It provides huge benefits to businesses, but only if it’s treated the right way.

The treatment of your data begins with its cleaning followed by processing. We’ll talk about the first phase here.

Source of Errors in Big Data

Errors have a way of creeping into even a foolproof system. The errors in Big Data or, in fact, any data, may come from a variety of sources.

The most basic cause of inaccuracies in data is human error. For instance, while filling a survey form, a customer may enter his/her name with incorrect spelling. This may lead to problems when the feedback is integrated into an existing customer profile database.

There’s always a possibility of having fake entries, or even multiple entries, which may also create problems in your data analysis.

Finally, you can also create errors in your data by condensing it. This occurs more commonly when dealing with a database of product reviews.

Why Is Big Data Cleanup Necessary?

US businesses lose $600 million every year because of dirty data. Having clean data takes your revenue up by 66%!

Not to mention the fact that customers will be more willing to believe you if you have a reputation of maintaining clean data records.

Know that having clean Big Data can save you time and money. It can build you a good reputation in the market and trust among customers.

The major benefit of having clean Big Data, however, is better decisions. If you’re using some made up or unreliable data for your analysis, you’ll get only invalid conclusions. As the saying goes, “garbage in, garbage out”.

What Forms of Errors Appear in Big Data?

The list of errors that you will have to face while fixing up your data is endless and ever growing. However, typical errors are:

  • Aliasing – When different entities are merged, perhaps because of the same tag
  • Incorrect entries – Either intentional or unintentional
  • Missing entries – When data is lost in the system due to glitches, etc.
  • Multiple entries – When the same information has different tags

Data cleaning is a messy job. You can always hire someone to do it for you. After all, you need to have a clean source of information to take better, more informed business decisions.

But bear in mind that no one can ever know the dirt in your data like you do. You’re the only one who can truly identify the clean from the dirty. That’s because you know what it should look like. So, be brave and do it!

Can you draw good enough conclusions from raw data? Or is it necessary to have clean data? Share your opinions with us!