6 Phases of Data Analytics Lifecycle: Explained in Detail

Have you ever wondered how businesses transform raw data into actionable insights? The Data Analytics Lifecycle provides a structured approach to this transformation, ensuring that each step is methodically executed for optimal results. From identifying objectives to final reporting, each phase is essential in the entire procedure. But ever wondered why is it important to follow a structured lifecycle? Because it ensures consistency, accuracy, and efficiency in Data Analysis. 

In this extensive blog, we will talk about the six fundamental phases of the Data Analytics Lifecycle, shedding light on how each stage contributes to solving complex business problems. Read more to unlock the secrets of successful Data Analytics.

Table of Contents 

1) What is Data Analytics Lifecycle?

2) Why is the Data Analytics Lifecycle Important?

3) Phases of Data Analytics Lifecycle

      a) Data Discovery and Formation

      b) Data Preparation and Processing

      c) Design a Model

      d) Model Building

      e) Result Communication and Publication

      f) Measuring Effectiveness

4) Conclusion

What is Data Analytics Lifecycle?

The Data Analytics lifecycle comprises six essential phases, each critical for businesses engaged in Data Analytics. This lifecycle is based on the widely adopted Cross-Industry Standard Process for Data Mining (CRISP-DM) model, an open-standard analytics framework developed by IBM. The phases include defining business objectives, cleaning data, building models, and communicating with stakeholders.

This process begins with identifying the problem to be solved, followed by testing models on sandboxed data and ultimately deploying these models on a production dataset. This approach helps uncover answers to initial questions and informs strategic business decisions.
 

Why is the Data Analytics Lifecycle Important? 

The Data Analytics Lifecycle is crucial for several reasons. Let's explore five key points, highlighting its importance:

a) It gives a systematic framework for managing and analysing data, ensuring that each step, from data collection to interpretation, is methodically executed.

b) By emphasising data cleaning and preprocessing, the lifecycle helps maintain high data quality, which is essential for accurate and reliable analysis.

c) The lifecycle transforms raw data into valuable insights, enabling organisations to make decisions that drive business success.

d) Following a defined lifecycle streamlines the Data Analysis process, reducing redundancy and improving efficiency in handling data projects.

e) The lifecycle includes stages for monitoring and maintenance, allowing organisations to continuously evaluate and improve their data strategies.

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Phases of Data Analytics Lifecycle

This scientific method is used to create a structured framework for the Data Analytics Lifecycle, which involves six stages. This framework is both direct and cyclical, meaning all big Data Analytics processes must be completed sequentially but can also be revisited in any order. Below are the six fundamental phases of the Data Analytics Lifecycle:

1) Data Discovery and Formation

This is the first and most critical step. Here, you identify your data objectives and determine how to achieve them through the Data Analytics Lifecycle. Initial evaluations and assessments help develop a hypothesis to solve business issues. Data is evaluated for its potential uses, origins, intended messages, and benefits to the business.

2) Data Preparation and Processing

This phase involves gathering, sorting, processing, and purifying collected information to ensure it is ready for analysis. Ensuring all necessary information is accessible is crucial before moving forward.

3) Design a Model

After defining your business goals and gathering data (formatted, unformatted, or semi-formatted), you create a model to achieve these goals. This stage involves planning how to use the data effectively.

By identifying key variables and relationships, you can structure your model to provide actionable insights. Regularly refining and validating the model ensures it remains accurate and relevant.

4) Model Building

In this phase, datasets are created for testing, training, and production. Data Analytics professionals develop and operate the model using tools and methods such as decision trees, regression techniques, and neural networks. They run trial tests to ensure the model matches the datasets and determine if the current tools are sufficient or if a more robust system is needed.

5) Result Communication and Publication

This process begins with collaborating with key stakeholders to evaluate the project’s outcomes. The project team identifies major conclusions, calculates the business value of the results, and creates a narrative to communicate findings to stakeholders.

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6) Measuring Effectiveness

The final stage involves providing stakeholders with a comprehensive report, including results, coding, briefings, and technical documents. The data is moved from a sandbox to a live environment to assess if the results meet the business objectives. If the findings align with the goals, the reports are finalised. If not, the cycle can be revisited to adjust inputs and achieve the desired outcome.

Conclusion 

We hope our blog has helped you unlock the secrets of the Data Analytics Lifecycle. By mastering its six phases, you can transform raw data into powerful insights besides driving innovative solutions. Start your data journey and make every byte count.

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