Data Analytics Lifecycle: An Easy Overview For 2022

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.

Table of content

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 today’s digital-first 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.

Why is Data Analytics Lifecycle Essential?

The Data Analytics Lifecycle is designed to be used with significant big data projects. It is used to portray the actual project correctly; the cycle is iterative. A step-by-step technique is needed to arrange the actions and tasks involved in gathering, processing, analyzing, and reusing data to explore the various needs for assessing the information on big data. Data analysis is modifying, processing, and cleaning raw data to obtain useful, significant information that supports business decision-making.

2. Importance of Data Analytics Lifecycle

Data Analytics Lifecycle defines the roadmap of how data 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 a 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; 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 the data analytics life cycle.

This guide talks about the fundamental phases of each data analytics process. Hence, they are more likely to be present in most data analytics projects’ lifecycles. 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.

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

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 collecting, processing, and cleansing 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, and 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, identifies relations between data points to select the key variables, and eventually devises a suitable model.

Phase 4: Model Building

The team develops testing, training, and production datasets in this phase. 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 phase, 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 to boost 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 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, and 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.

Three beginner-friendly Data Analytics projects for students

Internet web show Database

The IMDb data extraction project is a great one for beginners. You can compile information about well-liked TV series, movie reviews and trivia, various stars’ heights and weights, and more. The fact that IMDb’s data is presented consistently across all of its sites makes the work much simpler.

Job portals

This is one of the best Data Analytics projects for students. Job portals often provide standard data types, and many beginners like scraping data from them. There are also a lot of online tutorials that will walk you through the process. Compile information on the jobs, employers, paychecks, locations, necessary skills, and other information. The potential for later visualization is enormous. Such as plotting skillsets against paychecks.

E-commerce sites

Another common method is to scrape information about products and prices from online stores. Extract product details for Bluetooth speakers, or gather ratings and costs for different computers and tablets. Once more, this is scalable and relatively easy to implement. This implies that once you feel confident utilizing the algorithms, you can move on to a product with higher feedback.

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 and using the same for effective HR implementations, then do check out our 3-month robust People Analytics & Digital HR Program!