Introduction

When we talk about learning and implementing Data Science and Big Data, we often come across the term Data Analytics Life Cycle in Big Data and Data Science. In this guide, we’ll have a Data Analytic Lifecycle overview, learn why it’s essential, know in detail about different phases of the Data Analytics Life Cycle, and finally go through a Data Analytics lifecycle example.

  1. What Is Data Analytics Lifecycle?
  2. Importance of Data Analytics Lifecycle
  3. Data Analytics Lifecycle Phases
  4. Data Analytics Lifecycle Example

1. What Is Data Analytics Lifecycle?

In the current digital world, data is of immense importance. It undergoes various stages throughout its life, during its creation, testing, processing, consumption, and reuse. Data Analytics Lifecycle maps out these stages for professionals working on data analytics projects. These phases are arranged in a circular structure that forms a Data Analytics Lifecycle. Each step has its significance and characteristics.

2. Importance of Data Analytics Lifecycle

Data Analytics Lifecycle defines the roadmap of how information is generated, collected, processed, used, and analyzed to achieve business goals. It offers a systematic way to manage data for converting it into information that can be used to fulfill organizational and project goals. The process provides the direction and methods to extract information from the data and proceed in the right direction to accomplish business goals.

Data professionals use the lifecycle’s circular form to proceed with data analytics in either forward or backward direction. Based on the newly received insights, they can decide whether to proceed with their existing research or scrap it and redo the complete analysis. The Data Analytics lifecycle guides them throughout this process.

3. Data Analytics Lifecycle Phases

There’s no defined structure of the phases in the life cycle of Data Analytics, and thus, there may not be uniformity in these steps. There can be some data professionals that follow additional steps, while there may be some who skip some stages altogether or work on different phases simultaneously. Let us discuss the various phases of data analytics life cycle.

This guide talks about the phases that are fundamental to each data analytics process. Hence, they are more likely to be present in most data analytics projects’ lifecycle. The Data Analytics lifecycle primarily consists of 6 phases.

Phase 1: Data Discovery and Formation

This phase is all about defining the data’s purpose and how to achieve it by the end of the data analytics lifecycle. The stage consists of identifying critical objectives a business is trying to discover by mapping out the data. During this process, the team learns about the business domain and checks whether the business unit or organization has worked on similar projects to refer to any learnings.

In this phase, the team also evaluates technology, people, data, and time. For example, while dealing with a small dataset, the team can use Excel. However, heftier tasks demand more rigid tools for data preparation and exploration. In such scenarios, the team will need to use Python, R, Tableau Desktop or Tableau Prep, and other data cleaning tools.

This phase’s critical activities include framing the business problem, formulating initial hypotheses to test, and beginning data-learning.

Phase 2: Data Preparation and Processing

In this phase, the experts’ focus shifts from business requirements to information requirements. One of the essential aspects of this phase is ensuring data availability for processing. The stage encompasses the collection, processing, and cleansing of the accumulated data.

During this phase’s initial stage, the team gathers valuable information and proceeds with the business ecosystem’s lifecycle. Various data collection methods are used for this purpose, such as

o   Data Entry – Collecting recent data using manual data entry techniques or digital systems within the organization

o   Data Acquisition – Gathering data from external sources

o   Signal Reception – Capturing data from digital devices, including the Internet of Things and control systems.

Phase 3: Design a Model

This phase needs the availability of an analytic sandbox for the team to work with data and perform analytics throughout the project duration. The team can load data in several ways.

o   Extract, Transform, Load (ETL) – It transforms the data based on a set of business rules before loading it into the sandbox.

o   Extract, Load, Transform (ELT) – It loads the data into the sandbox and then transforms it based on a set of business rules.

o   Extract, Transform, Load, Transform (ETLT) – It’s the combination of ETL and ELT and has two transformation levels.

The team identifies variables for categorizing data, identifies and amends data errors. Data errors can be anything, including missing data, illogical values, duplicates, and spelling errors. For example, the team imputes the average data score for categories for missing values. It enables more efficient data processing without skewing the data.

After cleaning the data, the team determines the techniques, methods, and workflow for building a model in the next phase. The team explores the data and identifies relations between data points to select the key variables and eventually devises a suitable model.

Phase 4: Model Building

In this phase, the team develops testing, training, and production datasets. Further, the team builds and executes models meticulously as planned during the model planning phase. They test data and try to find out answers to the given objectives. They use various statistical modeling methods such as regression techniques, decision trees, random forest modeling, and neural networks and perform a trial run to determine whether it corresponds to the datasets.

Phase 5: Result Communication and Publication

This phase aims to determine whether the project results are a success or failure and start collaborating with significant stakeholders. The team identifies the vital findings of their analysis, measures the associated business value, and creates a summarized narrative to convey the stakeholders’ results.

Phase 6: Measuring of Effectiveness

In this final step, the team presents an in-depth report with coding, briefing, key findings, and technical documents and papers to the stakeholders. Besides this, the data is moved to a live environment and monitored to measure the analysis’s effectiveness. If the findings are in line with the objective, the results and reports are finalized. On the other hand, if they deviate from the set intent, the team moves backward in the lifecycle to any previous phase to change the input and get a different outcome.

4. Data Analytics Lifecycle Example

Consider an example of a retail store chain that wants to optimize its products’ prices for boosting its revenue. The store chain has thousands of products over hundreds of outlets, making it a highly complex scenario. Once you identify the store chain’s objective, you find the data you need, prepare it, and go through the Data Analytics lifecycle process.

You observe different types of customers, such as ordinary customers and customers like contractors who buy in bulk. According to you, treating various types of customers differently can give you the solution. However, you don’t have enough information about it and need to discuss this with the client team.

In this case, you need to get the definition, find data, and conduct the hypothesis testing to check whether various customer types impact the model results and get the right output. Once you are convinced with the model results, you can deploy the model, integrate it into the business, and you are all set to deploy the prices you think are the most optimal across the outlets of the store.

Conclusion

The Data Analytics lifecycle’s circular process consists of 6 primary stages that dictate how information is created, collected, processed, used, and analyzed. Mapping out business objectives and striving towards achieving them will guide you through the rest of the stages. If you are interested in learning more about Data Analytics in the business context, our 10-month Integrated Program in Business Analytics, in collaboration with IIM Indore is perfect for you!

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