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.
• Clean & Validate Data: With your datasets outlined, it will be time to start cleaning the data. This will involve filling missing values, removing incorrect information, and converting the data into a standard format.
• Enrich The Data: Data will be added and connected within your set, enriching it, and giving you a better understanding of what it means to your business.
• Store The Data: Once prepared, the data will be stored on a cloud server until it is time for it to be used.

Self-Service Data Preparation Tools

Data preparation can be extremely time-consuming, leaving a lot of data scientists looking for ways to make it faster. Self-service data preparation tools can be a major help with this, with options like Talend Data Preparation using special AI and machine learning to give you the best possible results.

Some of these platforms will simply make it easier for you to prepare you data by giving you smart systems that work for the job. In more extreme cases, though, they will be able to analyze and change the data on your behalf. Each of the steps outlined above can be handled by the most technical options on the market.

The Future of Data Preparation

With AI and machine learning tools improving all the time, the future is bright for data preparation. It’s only going to get easier to have the tedious parts of this job taken out of your hands, with powerful algorithms doing all of the really difficult stuff. This doesn’t mean that you’ll be able to get rid of humans for good, though, as it always helps to have a person check over your data before it is used.

Alongside the systems behind data preparation getting better, the datasets that scientists are having to work with are always getting bigger. This inflation could maker it hard for data centers and other service providers to keep up, and this has the potential to leave your company in the dust. Hopefully, the systems responsible for data preparation will be good enough by the time datasets become truly unmanageable.

Data preparation has always been a crucial element of a data scientist’s job. In fact, many of these professionals spend most of their time at work preparing data, with the tests they have to run being relatively short. This makes it well worth looking for ways to improve your situation.