Data Analytics training models are fast changing to develop an interactive medium to understand and demonstrate the importance of learning the data analysis life cycle for top industries.
Understanding the life cycle model of data analysis processes can benefit your career in a major way, especially if you are planning to build opportunities in data intensive domains like Cloud Computing, ML Ops, Automation, and Financial Services management.
Why Data Analysis?
Data analysis is a crucial part of any business function today. It involves working with different types of data that can be analyzed and used to draw useful inferences about an event or function. As we continue to work with countless varieties and versions of data, it has become extremely complex for data science teams to keep a tab on the data analytics results. That’s why we need a solid data analytics infrastructure governed by rules and policies to understand how different data analytics techniques influence the results in the short and long term future.
What Is Data Analysis Life Cycle?
Data Analysis is a logical framework used by data scientists and business analysts to determine the role of data collection, mining and analytics processes on the final outcome of the business operations. We can call it the lifeline of any operation that involves working with data. The life cycle helps us break down the various processes across the entire data management framework, without challenging the veracity of sources and their impact on the next steps.
In general, the data science life cycle involves 6 steps, which could be further broken down into another dozen or so sub-processes depending on the volume, variety, and veracity of data.
Let’s understand this one by one.
Data Discovery
If you are building a data analysis project, it’s important to outline the purpose of undertaking this endeavor. Data Discovery is the first step in the run of data analysis that helps map out the origin of data and for what purposes it would be used.
Let’s take online shopping data collected from Amazon.
The contact details of the online shopper could be used to understand how the consumer prefers to find and buy a product, in addition to formulating a strategy to target similar cohorts of buyers using online marketing and advertising tactics. This works well when business intelligence teams focus on data discovery tools essentially to collect, analyze and process all the relevant forms of data associated with the buyer’s behavior across the web, social media, email, and now, even internet TV and OTT platforms.
Data Processing
Like a recipe, you need to craft the dish using ingredients that would impart taste and flavor to your dish. Consider the data processing layer of the data analysis life cycle as the step that involves collecting all the ingredients required to make your data model more potent for the whole operation.
Data processing involves data acquisition, collection, entry, and finally, processing.
We have different ways to process any type of data. If you are doing data analytics training from a top course, you would learn single user programming or multi-user programming, in addition to Python based real-time programming that forms the center line of today’s Business Intelligence Software for Marketing, Sales, Finance, HR, Operations, and IT teams.
Data Modeling / Planning
If you are interested in the role of Machine Learning applications for data analysis, this is the step where you would learn the most. Data modeling could be understood by virtue of their layouts, which could be either Logical Data Models, Conceptual Data Models, or Physical Data Models. The role of Machine Learning is immense in today’s data analysis life cycle as these models help to formalize the sequence of tasks that are to be involved in the data processing and modeling operations.
Model Building
Model Building is an extension of the planning step. Developers go for ELT or ETL operations to manage different data resources within data management platforms more efficiently. You could be working as an enterprise data architect or ML engineer to build data models that are based on Python, R, or MATLAB languages and standards.
Communication
The results of the whole data analysis modeling and ops have to be reported to the stakeholders, such as decision makers from the board. It could be reported through data visualization or dashboards.
Reporting Effectiveness
The most important aspect of the business analysis is reporting and that’s why analysts rely on a mix of technology and innovation to refine data analysis training strategies.
