Big Data is a growing field in IT, which is exponentially developing within organizations. With large chunks of data, specific methods and tools should be elaborated to split and aggregate it. Large datasets go through the specific lifecycle from ingestion to data visualization where finally, the data is cleaned, reduced, and processed for further use. Without a full understanding of different big data methods, the situation might get out of control, that is why one should make decisions rationally before the data is processed and visualized to avoid any inconsistencies.
The most common challenge arising within organizations is the problem that sometimes the data is gathered incorrectly because of the wrong methods used or when it is not smoothly processed during its usual lifecycle. It might happen when people handling big data made mistakes during the metrics process or they do not have enough experience at providing data veracity and ultimately, value. In this article, we would underline the most common big data practices, which play a vital role in keeping business afloat.
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.
Check the validity of your data on time before ingesting it into the system is essential to avoid any extra work, return to the initial process, and correct things over and over again. It is important to check the collected information and gain more insights during the project.
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.
Start from a small pilot project and focus on the areas, which might go wrong. To avoid any problems, establish a method if any problem arises. One of the most common techniques is an Agile approach, which implies breaking project on phases and adopting new client’s changes during the process of development. In this case, data big analysts might test the data several times per week to ensure it is a right for further computing.
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.
Choosing a technology depends on the method you will apply. Therefore, in the case of real-time processing, you might go for Apache Spark, as it computes all data in RAM in an efficient way. If you deal with batch processing, you can enjoy the benefits of Hadoop, which is a highly scalable platform for processing data controlled by cheap servers.
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.
Data privacy is another aspect, which requires to pay more attention to those who have access to corporate data and which one should be strictly accessed by a particular group of people. One should define which data should be kept in the public cloud and which one – 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.