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6 Life Cycle Phases of Data Analytics

What is Data Analytics Life Cycle?

Data is precious in today’s digital environment. It goes through several life stages, including creation, testing, processing, consumption, and reuse. These stages are mapped out in the Data Analytics Life Cycle for professionals working on data analytics initiatives. Each stage has its own significance and characteristics.

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Importance of Data Analytics Life Cycle

The data analytics Life Cycle encompasses the process of producing, collecting, processing, using, and analyzing data in order to meet corporate objectives. It offers a systematic way for managing data into useful information that can help achieve organizational or project goals; additionally, it provides guidance and strategies for extracting this information and moving in the appropriate direction in order to meet corporate objectives

Importance of Data Analytics Life Cycle

Data professionals use the circular nature of the Life Cycle to go ahead or backward with data analytics. Based on the new information, they can decide whether to continue with their current research or abandon it and redo the entire analysis. Throughout the process, they are guided by the Data Analytics Life Cycle.

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Data Analytics Life Cycle Phases

Data Analytics Life Cycle Phases

The scientific method for creating a structured framework of the data analytics life cycle involves six stages of architecture for data analytics. The framework is direct and cyclical, meaning all big data analytics-related processes must be completed sequentially.

Notably, these phases are circular; therefore they may be undertaken either forwards or backwards. Below are six data analytics phases that serve as fundamental processes in data science projects.

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Phase 1: Data Discovery and Formation

Every good journey begins with a purpose in mind. In this phase, you will identify your desired data objectives and how best to attain them through data analytics Life Cycle implementation. Evaluations and assessments should also be undertaken during this initial phase to develop a basic hypothesis capable of solving business issues or problems.

In the initial step, data will be evaluated for its potential uses and demands – such as where it comes from, what message you wish for it to send and how this incoming information benefits your business.

As a data analyst, you will need to explore case studies using similar data analytics and, most crucially, examine current company trends. Then you must evaluate all in-house infrastructure and resources, as well as time and technological needs, in order to match the previously acquired data.

Following the completion of the evaluations, the team closes this stage with hypotheses that will be tested using data later on. This is the first and most critical step in the life cycle of big data analytics.

Key takeaways:

  • The data science team investigates and learns about the challenge.
  • Create context and understanding.
  • Learn about the data sources that will be required and available for the project.
  • The team develops preliminary hypotheses that can later be tested with data.

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Phase 2: Data Preparation and Processing

Data preparation and processing involves gathering, sorting, processing and purifying collected information to make sure it can be utilized by subsequent steps of analysis. An important element of this step is making sure all necessary information is readily accessible before moving ahead with processing it further.

Following are methods of data acquisition

  • Data Collection: Draw information from external sources.
  • Data Entry: Within an organization, data entry refers to creating new points of information using either digital technologies or manual input procedures.
  • Signal Reception: Accumulating data from digital devices like the Internet of Things devices and control systems.

An analytical sandbox is essential during the data preparation stage of data analytics Life Cycle. This scalable platform is used by data analysts and scientists alike for processing their data sets; once executed, loaded, or altered it resides securely inside this sandbox for later examination and modification.

This phase of the analytical cycle does not need to take place in any particular order; rather it can take place as necessary and be repeated at later times as appropriate.

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Phase 3: Design a Model

After you’ve defined your business goals and gathered a large amount of data (formatted, unformatted, or semi-formatted), it’s time to create a model that uses the data to achieve the goal. Model planning is the name given to this stage of the data analytics process.

There are numerous methods for loading data into the system and starting to analyze it:

  • ETL (Extract, Transform, and Load) converts the information before loading it into a system using a set of business rules.
  • ELT (Extract, Load, and Transform) loads raw data into the sandbox before transforming it.
  • ETLT (Extract, Transform, Load, Transform) is a combination of two layers of transformation.

This step also involves teamwork to identify the approaches, techniques, and workflow to be used in the succeeding phase to develop the model. The process of developing a model begins with finding the relationship between data points to choose the essential variables and, subsequently, create a suitable model.

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Phase 4: Model Building

This stage of the data analytics life cycle involves creating datasets for testing, training, and production. The data analytics professionals develop and operate the model they designed in the previous stage with proper effort.

They use tools and methods, such as decision trees, regression techniques logistic regression), and neural networks to create and run the model. The experts also run the model through a trial run to see if it matches the datasets.

It assists them in determining whether the tools they now have will be enough to execute the model or if a more robust system is required for it to function successfully.

Key Takeaways:

  • The team creates datasets for use in testing, training, and production.
  • The team also examines if its present tools will serve for running the models or if a more robust environment is required for model execution.
  • Rand PL/R, Octave, and WEKA are examples of free or open-source tools.

Phase 5: Result Communication and Publication

Recall the objective you set for your company in phase 1. Now is the time to see if the tests you ran in the previous phase matched those criteria.

The communication process begins with cooperation with key stakeholders to decide whether the project’s outcomes are successful or not.

The project team is responsible for identifying the major conclusions of the analysis, calculating the business value associated with the outcome, and creating a narrative to summarize and communicate the results to stakeholders.

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Phase 6: Measuring Effectiveness

As your data analytics life cycle comes to an end, the final stage is to offer stakeholders a complete report that includes important results, coding, briefings, and technical papers or documents.

Furthermore, to assess the effectiveness of the study, the data is transported from the sandbox to a live environment and observed to see if the results match the desired business aim.

If the findings meet the objectives, the reports and outcomes are finalized. However, if the conclusion differs from the purpose stated in phase 1, then you can go back in the data analytics life cycle to any of the previous phases to adjust your input and get a different result.

Conclusion

The circular process of the Data Analytics Life Cycle consists of six key steps that govern how information is created, collected, processed, used, and evaluated. Setting company goals and working toward them will guide you through the remaining stages..

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About the Author

Head of Data Engineering & Science

As a head of Data Engineering and Science at Chargebee, Birendra is leading a team of 50+ engineers, specializing in high-scale data and ML platforms. Previously, held key roles at Razorpay and as CTO, with extensive experience in Big Data, ML, and SAAS architecture. Recognized for 85+ contributions to tech publications and patents.