Introduction to Data Analytics: A Beginner’s Guide

Are you aware that a staggering 2.5 quintillion bytes of data are generated daily? This is merely the beginning of a rapidly growing trend. With each passing year, we are witnessing an exponential increase in data production. In the current era of Big Data, information has emerged as the modern currency of wealth.

In the race for success, it’s not the superior technology that prevails, but rather the one with superior data.

In today’s competitive landscape, companies worldwide are vying to protect and capitalize on the data they’ve amassed. The significance lies in the fact that extensive datasets reveal patterns and trends crucial for informed business decisions. This explains why Data Analysts are among the most sought-after roles in the job market. The versatility of Data Analytics extends across various domains, unlocking potential applications from healthcare to cosmology.

If you’re new to the field of Data Analytics, which everybody is at some point, you’ll delve into its fundamentals and the diverse phases it encompasses. Subsequent articles will provide an in-depth exploration of each phase in the data analytics process.

Let’s begin!

What is Data Analytics?

The practice of scrutinizing datasets to identify trends and derive conclusions about the contained information is known as data analytics.

As an illustration, data collected from consumers of an e-commerce store can reveal their preferences for certain products. Utilizing this insight, the organization may decide to boost the inventory of those particular products or make strategic business decisions based on customer preferences.

Before diving into the Data Analytics process, the initial and crucial step is to clearly define the problem you aim to solve. This step is pivotal as it provides a guiding focus for the subsequent phases.

The task of defining a problem can be intricate, especially when considering various aspects for analysis. However, acknowledging and defining the problem is a significant leap forward. One effective approach is to pose a series of questions to yourself.

1. Where do I currently stand?
2. What objective am I aiming to achieve?
3. What obstacles are impeding my progress toward this goal?

These questions, when answered, should enable you to formulate a clear and concise problem statement.

Consider a scenario where a company is grappling with high attrition rates. In response, the HR personnel undertake a proactive approach by addressing the following questions:

1. Current Position: What is the current status? — A record high in resignations.
2. Goal: What objective are we striving for? — Achieving high employee satisfaction and a notable retention percentage.
3. Obstacles: What hinders the attainment of our goal? — Employee dissatisfaction.

By answering these three questions, a well-defined problem statement emerges for the analysis:

Problem Statement:
What is causing employee dissatisfaction?

This identified problem statement serves as the cornerstone for proceeding with the analysis, exemplified in this case by conducting an employee survey.

Stages in the Data Analytics Process

While there isn’t a universal set of steps for every data analytics problem, the Google Data Analytics course suggests the following six steps, which should cover a wide range of problem statements.

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

Let’s briefly explore each of them.

Ask

This stage involves engaging with stakeholders, process owners, and management to gain a comprehensive understanding of the problem at hand. In the given example, the data analyst may liaise with the HR department, Project Managers, and the Management to inquire about employee dissatisfaction and collaborate in defining the problem statement.

Prepare

During the prepare phase of the analysis, the focus is on collecting data from diverse sources. In the mentioned example, the data analyst would design an employee survey and distribute it across the organization.

Process

In the data analytics process, the real-world data often lacks the cleanliness we might expect. This phase involves cleaning, transforming, and preparing the data to achieve a useful and analysis-ready state. Typical tasks include formatting, addressing blank values, correcting typos, and similar actions.

Analyze

The analysis phase marks the commencement of the actual exploration. Once the data is prepared for analysis, data analysts delve into the dataset, unveiling patterns and trends. Notably, this stage holds the key to addressing the problem statement.

It’s worth noting that, during the analysis phase, supplementary formatting, sorting, and filtering are applied to gain a more nuanced perspective of the dataset. In the provided example, the factors contributing to attrition, such as compensation, projects, and management, are revealed in this phase.

Share

The insights derived from earlier stages are communicated to the management through visualizations such as dashboards, graphs, and charts. This phase is crucial as it requires capturing the management’s attention to effectively present the findings.

Returning to our example, issues like compensation, projects, and management are conveyed to the management through dashboards. This involves a comparison of the mentioned metrics with market standards and industry verticals for a comprehensive presentation.

Act

The ultimate purpose of these findings lies in their application. The final phase of the data analytics process involves taking action based on the insights and resolving the issues identified in the problem statement. In the given example, this would entail addressing the factors contributing to the high attrition rate.

Conclusion

In conclusion, Data Analytics serves as a potent tool for fostering business growth. I trust that you now possess a foundational understanding of key concepts and their influence on the overarching analysis process.

Wishing you a fulfilling learning journey!