As the years pass on the volume of data which needs to explode had been increased to unimagined levels. For example, each and every business or any company involves several processes to complete the projects. It involves the huge amount of data to be processed ie all the data related to the company. It includes the user’s interaction, social media, business deals etc.

in order to access the large sea of data very effectively and collectively there emerged the big data analytics with different advanced methods. Big data analytics include the collection of large amounts of data and merging them in a more appropriate manner so that it can be consumed by the analysts and finally deliver the products to the stakeholders. This process of collecting the raw data from different sources and making them useful for the benefit of an organisation can be termed as big data analytics.

A traditional big data lifecycle:

In order to organize the data from an organization, you need to create a framework with different stages of the life cycle for the big data analytics. All the stages in the data lifecycle are connected to one another and can be distinguished into traditional and statistical methods.

CRISP-DM Methodology:

CRISP-DM stands for Cross Industry Standard Process for Data Mining Methodology. It is a commonly used approach where the data mining experts use to tackle the problems. There are several stages involved in the life cycle of the CRISP-DM. The traditional big data life cycle stages are as follows.

  • Business Understanding: It is the initial phase of the life cycle. It involves the collection of project objectives and requirements from the business perspectives. You need to define problem definition here.
  • Data Understanding: It starts with data collection so as to form the initial step to the project. You need to describe the subsets required to form the complete data collection.
  • Data Preparation: In this phase, you need to construct the final dataset from the initial raw data. You need to perform the tasks simultaneously.
  • Modelling: In this phase, various modelling techniques are used and applied to the parameters to attain optimal values.
  • Evaluation: In this phase, you need to build a high-quality data analysis before deployment. This is a very important step and you need to cross check twice before deploying your project.
  • Deployment: In this step, a model is created for your data and then you need to organize the data in a presentable manner to the customers.

There is also another methodology named SEMMA i.e Sample, Explore, Modify, Model and Assess the data. SEMMA focus on the modelling part whereas CRISP DM focus on the all the stages of the big data lifecycle. In some of the approaches, you can find some incomplete data. There are various stages in the big data analytics cycle which can be described as follows.

Big Data Life Cycle:

The big data analytics life cycle involves the following stages. They are:

  • Business Problem Definition: You need to define the problem with potential gain and costs for the project.
  • Research: You need to analyze the data and look for the solutions that are worthy when compared with other company projects. Try to find out what the other company using resources so as to solve the same problem. Simply learn from other experts in that fields.
  • Human resource Assessment: Once your problem is defined then you need to check with your human resource team whether they can give you the outstanding success in the specified project.
  • Data Acquisition: Collecting the raw data and organize it in a meaningful manner.
  • Data Munging: It helps in getting high standard and quality product deliverable.
  • Data Storage: Once the data is collected and the processes it needs to be stored in some locations to get accessed.
  • Data modelling and Assessments: It involves the production of several datasets and trying different data models so as to get accurate results.
  • Implementation: As the next step you need to implement the data product developed with some validation schema.

In big data analytics there is no unique methodology to define the problems. Once the problem is defined it requires the time to design the methodology for the issue and then take appropriate measures to handle it. In this field, you need to keep the data clean, preprocesses and available for modelling etc. You need to come up with the perfect key stakeholders for your business.

In Big data analytics, the key importance is given to the data analyst and data scientist. As the analyst will perform better with his programming knowledge in order to analyze the key requirements and solution for the problem. The data scientists come up with accurate ideas to be implemented in their big data projects for success.

If you are using big data analytics approaches to find a solution to your problem, then you will definitely get an outstanding report. As it Spotify every input, issue and makes you clear with results. It also simplifies your working process as all the data is placed in different stages of the life cycle. You can have a quick look and analyze your project theme and enhancements in many advances.