What is Data Preparation And Its Challenges

 Data preparation is the process of getting raw data ready for analysis and processing. This can mean restructuring the data at hand, merging sets for a more complete view, and even making corrections to data that isn’t recorded properly. While this sort of work is highly time-consuming, it is essential for any job that involves working with large amounts of complex data.

Benefits of Data Preparation And The Cloud 

Data preparation may not be a popular job amongst data scientists, this process can’t be avoided. Thankfully, it comes with plenty of benefits that can make the whole thing worth your while, and this is where we’re going to start this exploration into this vital field.

• Fix Errors Quickly: Fixing errors before processing data is much faster than doing it after the fact.
• High-Quality Data: With errors being fixed so quickly, your data will always see a quality increase after preparation.
• More Usable Data: Higher quality data will be easier to read and make use of, making this process well worth it.

Alongside the benefits that data preparation can provide, this gets even better once you add cloud services to the mix.

• Easy Collaboration: Storing all of your data on the cloud will make it easier for the whole team to access, aiding collaboration.
• Future Proof: Unlike having your own servers, cloud options can scale with your business, securing your future without forcing you to constantly upgrade.

Data Preparation Steps

The process of data preparation can be split into five simple steps, each of which is outlined below to give you a deeper insight into this job.

• Gather/Create Data: You won’t be able to get very far with this if you don’t have any data available. This makes the first stage in this process gathering data.
• Discovery: Once you have some data, it will be time to begin the discovery process, hunting for the data sets that are important to you.