Big Data best practices: top 5 principles

1. Identify your business goals before conducting analytics

Before data mining, a data scientist is responsible for understanding and analyzing the business requirements of the project. Organizations often create a roadmap where they envision both technical and business goals they want to reach during the project. Selecting and sorting out the relevant data necessary for the project is a must to reduce additional work. This follows the specific data services and tools, which would be used during the project and serve as a cornerstone to help you get started.

2. Choose the best strategy and encourage team collaboration

Assessing and controlling big data processes is a multi-role process, which requires a set of parties to keep eyes on the project. It is usually guided by the data owner, which administers a specific IT department or IT vendor, which provides the given technology for data mining, or a consultancy to have an additional hand for keeping the situation under control.

3. Begin from small projects and use Agile approach to ensure high quality

It might be complex to start from big projects when you have little experience. Besides, it may pose a risk to your business if the big data solution does not work appropriately or it is full of bugs. There is always a learning curve to strive for better and take more challenging projects.

4. Select the appropriate technology tools based on the data scope and methods

In the world of raw data, as a data scientist, you are not only responsible for selecting the right tool, but also for adopting the right technology needed for further analysis. You may choose either SQL or NoSQL based on the scope of your data warehouse.

5. Opt for cloud solutions and comply with GDPR for higher security

You might use a cloud service to send and prototype the environment for data computations. As a lot of data should be processed and tested, you may opt for different cloud services like Google BigQuery or Amazon EMR. You might choose any data cloud tools developed by Amazon or Microsoft, the choice of which usually depends on the data scope and project itself. It takes a couple of hours to set an environment for prototyping and further, integrate it into the testing platform. One more positive aspect of cloud tool is the fact that you can store all data there rather than saving it on-premises.


Big data specialists should be interested not only in the type of technology they choose but also the flow and dynamics of business processes. Visualizing a roadmap and defining business goals before analytics is important to automate the working processes and achieve efficiency. Along with that, teams should work cohesively in a way to apply the best approach and strategy they would follow. The agile approach works the best in breaking work into pieces and validating it. After, choose the best technology based on your data scope, store your data on the cloud, and ensure compliance with GDPR. By understanding the business processes related to big data management, you can extract great value and reach more accurate outcomes.



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