6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About

What is a Data Analytics Lifecycle?

Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data analytics professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics.

The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram.

However, while there are talks of the data analytics lifecycle among the experts, there is still no defined structure of the mentioned stages. You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis expert. Such ambiguity gives rise to the probability of adding extra phases (when necessary) and removing the basic steps. There is also the possibility of working for different stages at once or skipping a phase entirely.

Yet, suppose, there is ever a discussion about the stages of the data lifecycle. In that case, the below-listed phases are likely to be present, as they represent the fundamentals of almost every data analysis process. upGrad follows these basic steps to determine a data professional’s overall work and the data analysis results.

Phases of Data Analytics Lifecycle

A scientific method that helps give the data analysis process a structured framework is divided into six phases of data analytics architecture.

Phase 1: Data Discovery and Formation

Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle.

The initial stage consists of mapping out the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization benefits from the incoming data. Basically, as a data analysis expert, you’ll need to focus on enterprise requirements related to data, rather than data itself. Additionally, your work also includes assessing the tools and systems that are necessary to read, organize, and process all the incoming data.

Essential activities in this phase include structuring the business problem in the form of an analytics challenge and formulating the initial hypotheses (IHs) to test and start learning the data. The subsequent phases are then based on achieving the goal that is drawn in this stage.

Phase 2: Data Preparation and Processing

This stage consists of everything that has anything to do with data. In phase 2, the attention of experts moves from business requirements to information requirements.

The data preparation and processing step involve collecting, processing, and cleansing the accumulated data. One of the essential parts of this phase is to make sure that the data you need is actually available to you for processing. The earliest step of the data preparation phase is to collect valuable information and proceed with the data analytics lifecycle in a business ecosystem. Data is collected using the below methods:

  • Data Acquisition: Accumulating information from external sources.
  • Data Entry: Formulating recent data points using digital systems or manual data entry techniques within the enterprise.
  • Signal Reception: Capturing information from digital devices, such as control systems and the Internet of Things.

Read: Data Analytics Vs Data Science

Phase 3: Design a Model

After mapping out your business goals and collecting a glut of data (structured, unstructured, or semi-structured), it is time to build a model that utilizes the data to achieve the goal.

There are several techniques available to load data into the system and start studying it:

  • ETL (Extract, Transform, and Load) transforms the data first using a set of business rules, before loading it into a sandbox.
  • ELT (Extract, Load, and Transform) first loads raw data into the sandbox and then transform it.
  • ETLT (Extract, Transform, Load, Transform) is a mixture; it has two transformation levels.

This step also includes the teamwork to determine the methods, techniques, and workflow to build the model in the subsequent phase. The model’s building initiates with identifying the relation between data points to select the key variables and eventually find a suitable model.

Phase 4: Model Building

This step of data analytics architecture comprises developing data sets for testing, training, and production purposes. The data analytics experts meticulously build and operate the model that they had designed in the previous step. They rely on tools and several techniques like decision trees, regression techniques (logistic regression), and neural networks for building and executing the model. The experts also perform a trial run of the model to observe if the model corresponds to the datasets.

Phase 5: Result Communication and Publication

Remember the goal you had set for your business in phase 1? Now is the time to check if those criteria are met by the tests you have run in the previous phase.

The communication step starts with a collaboration with major stakeholders to determine if the project results are a success or failure. The project team is required to identify the key findings of the analysis, measure the business value associated with the result, and produce a narrative to summarise and convey the results to the stakeholders.

Phase 6: Measuring of Effectiveness

As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed report with key findings, coding, briefings, technical papers/ documents to the stakeholders.

Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment from the sandbox and monitored to observe if the results match the expected business goal. If the findings are as per the objective, the reports and the results are finalized. However, suppose the outcome deviates from the intent set out in phase 1then. You can move backward in the data analytics lifecycle to any of the previous phases to change your input and get a different output.

Also Read: Data Analytics Project Ideas

Conclusion

The data analytics lifecycle is a circular process that consists of six basic stages that define how information is created, gathered, processed, used, and analyzed for business goals. However, the ambiguity in having a standard set of phases for data analytics architecture does plague data experts in working with the information. But the first step of mapping out a business objective and working toward achieving them helps in drawing out the rest of the stages.

upGrad’s PG diploma in Data Science in association with IIIT-B and a certification in Business Analytics covers all these stages of data analytics architecture. The program offers detailed insight into the professional and industry practices and 1-on-1 mentorship with several case studies and examples. Hurry up and register now!

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