Understanding Data Analytics Lifecycle in 2021

Where is data analytics heading in 2021? It’s reckoned that data analytics will hit through the roof with thousands of projects underway at the top data analytics course in Bangalore.

Data is part of our lives in more ways than we can ever think of. We are producing data at a massive volume, which leads many analysts to believe that without data, we will extinct. The data that exists in our life controls what we see and what we interact with on our gadgets. According to a recent report, globally we will produce close to 80 zettabytes of data in 2021, which will double its rate of production by 2024 -- something that can only be called astronomical in the modern context. However, it’s important to understand how data works and how various techniques and approaches influence the data analytics lifecycle.

In this article, we will discuss the data analytics lifecycle and how this influences the final outcomes of any Big Data project.

But, first what is the data analytics lifecycle?

Data Analytics is the scientific approach to Big Data projects that represent or simulate the actual parts of a project. These address the various aspects of data collection, management, analysis, and repurposing of data at various levels of operation.

Here are the various steps that we will discuss here:

Data Discovery

Data discovery is an important and fastest emerging Business Intelligence component. It helps BI teams to automatically prepare and organize enterprise data in a meaningful manner.

Data Discovery is often confused for a tool, rather than a process. Actually, data discovery entails the use of various analytical tools that iteratively work to guide a data analytics process. From augmenting data to automatically understanding patterns, data discovery is critical to be successful with any modern data analytics course in Bangalore.

Data Preparation

Prior to utilizing data for analytics in advanced modules, it is important to clean the raw data for meaningful context. Data preparation involves cleaning up raw data and combing it to extract patterns before they are actually analyzed using tools.

There are many data preparation tools that help BI analysts to discover, process, blend, refine, enrich and transform data. A good data preparation tool would ensure that data cleansing yields better analytical results in the long run.

Model Planning

After preparation comes to another stage -- model planning which involves learning from a data analytics course related to establishing a relationship between variables and constants.

BI teams are engrossed in training, testing, and production purposes using advanced tools like MATLAB and Statistica. The next step involves Model Building which provides the BI team with adequate insights into how models will run the various advanced stages of data management and operationalize results based on requirements.

Data Communication and Operationalization

When you work in advanced analytics, it’s important to coordinate the process with other team members and managers. Using tools like WEKA, SQL, and Octave, you can influence the operations for a workbench, especially if you are building open-source AI ML platform.

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