Dirty, Clean Or Cleanish: What’s The Quality Of Your Big Bata?

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Did you know that analysts spend at least 80% of their time cleaning and restructuring the data and only 20% or less time on getting insights from them which is their main purpose? Big data won’t magically boost your business, you’ll have to work hard for that. The uncleanness is one of the challenges of big data. Business owners turn to big data services to deal with it.

In today’s article we’re going go over what big data is, how significant is its quality and whether if you really need 100% clean data? But before you start reading, why don’t you take a few minutes of your time to go over these short reads about stepping up training with augmented reality and must-haves of application strategy.

What’s Big Data?

Big data is also data but huge in size, more complex and it grows exponentially with time. No traditional management tools can process big data efficiently. Example of big data is the stock exchange of New York, where 1 terabyte of data is produced every day and Facebook, where 500 plus terabyte of data is produced every single day. 

Big Data Cleaning

Data cleaning has a very important role to play in data management and analytics and it continues to do so. Data cleaning is actually the process of finding and getting rid of bad data. Bad data is the unfinished, inaccurate and unreliable data that comes from the databases. 

This data needs to be sorted out and cleaned or sometimes removed completely. You can clean the data interactively with cleansing tools or via scripting in batch process. 

Importance of Data Quality

The data that we receive from various databases from real life events is dirty, hence it costs a lot to cleanse it. This where the importance of data quality is highlighted in business. Cleaning, correcting or appending of data is important because, wrong data means wrong insights, which means wrong decisions. Wrong decisions can lead your business to failure. Many businesses have suffered huge losses from bad data. 

The problem is not the software used but the data as there is so much of it laying around. It may not necessarily be a bad thing but issues occur when the raw data received from sources is not properly filtered. 

Bad Quality Data

Using big data of bad quality can either bring you destructive results or have no impact at all. For example, you want to know the activity of your customers on your website and for that you use big data tool. You don’t really have to know the exact activity record to see the big picture, you just have to know enough.

So, the end results decide what you really need, it depends on what your company wants or the task that you want to complete. You don’t really have to use the highest precision data to do everything. To decide how good the quality of your big data should be, you should first think about your needs.

Good Quality Data

The good and bad data is distinguished from each other by a set of characteristics. These characteristics apply to big data and data in general and they are as follows:

  • Accuracy
  • Completeness
  • Consistency
  • Orderliness
  • Auditability

Good Enough Data

But whether to clean the data or use it in its dirty form can depend on the situation. To cleanse the data you may have to pay huge bucks, invest long hours of your precious time and scale down the performance of your company. 

That’s why there’re companies that aren’t totally crazy about clean data. They choose data which is just good enough. They set a threshold which will help them achieve the results they want without having to go overboard.  

Final Words

Data quality can be a complex issue in big data management. But what we’ve been able to learn is that it depends on the task you want to achieve or rather the needs of your company. You don’t always need high precision data. However, wrong data and the information drawn from it can adversely affect your business too. Good quality data has five characteristics that we’ve laid out for you. To make sure the data is suitable for use, always hold audits. 

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