Data Analytics for Business Empowerment

What is Data Analytics?

Data analytics is the science of methodologically analyzing raw data to derive insightful conclusions. It is a fast-moving practice that studies data sets to help business executives to make informed decisions and plan business activities. The process of analyzing raw data to identify trends and answer questions is the definition of data analytics, which encompasses the field’s enormous scope, with a wide range of tactics. It is increasingly trending in the banking, healthcare, retail, and hospitality sectors, where data is a regular commodity.

Data Analytics Revolution

Nowadays, we experience the transformative effects of big data and the internet of things (IoT) in Retail, digital marketing, and healthcare, where they’ve had a significant impact. However, with so much data at our disposal, it can be challenging to figure out how to use it better. 

Companies utilize data analytics to drive their decisions, while data scientists and analysts use it in their research. Data analysis may help companies understand their customers better, analyze their advertising efforts, tailor content, develop content strategies, and develop new products, resulting increase in business profitability and performance.

How Data Analytics help to improve Businesses?

For a long time, data analytics has been considered an essential tool for businesses looking to improve their marketing and communication activities. As we gain a better understanding of how data can be used in an organization, we’re becoming more creative with the different ways it can assist management teams. Data is vital in both the public and private sectors. Thanks to the ever-evolving collection and analytics capabilities, agencies and businesses may use data to improve operations, discover fraud, and much more.

Data analytics helps to improve businesses in,

  • Better Decision-Making (Data-Driven Decision-Making)
  • More Effective Marketing
  • Improved Customer Service
  • Customer Experience Optimization And Improvement
  • Risk And Fraud Mitigation
  • Relevant Product Delivery To Relevant Customers
  • Personalization And Service
  • Security Enhancement
  • More Efficient Operations

A tip: Do you know that Root Cause Analysis (RCA) is one of the first and most prominently used standards in analytical problem-solving? Any problem can be approached by asking the “Five Whys” and will it bring you to the standard Root Cause Analysis?

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Figure 1: 5 Whys RCA

Company Success Stories about using Data Analytics

The Coca-Cola corporation collects client data to optimize current consumption and upsell new items, creating a more effective operation with fewer costs and more revenues. Customers may submit product feedback via social media, phone, or email, allowing the firm to tailor its strategy to better suit their needs. The information gathered by the company is used to improve the brand’s experience and increase client loyalty.

Amazon appears to know you better than you know yourself regarding purchasing recommendations. Using Data Analytics, Amazon tracks when you make purchases, how you rate them, and what other customers with similar buying patterns are buying. For example, Amazon noticed that when consumers buy televisions, they also buy a TV mount, which gave them the idea to upsell and market the things that were purchased together.

Netflix’s recommendation algorithm is based on the massive amount of data collected on its more than 58 million members and has been found to be quite successful at predicting what people would watch. “The next Netflix series is being produced not because a producer had a heavenly vision or a moment of clarity,” argues Enrique Dans, an IE Business School professor who teaches innovation. This puts the company ahead of the competition, which includes large entertainment conglomerates such as Disney and streaming services such as Hulu.

Google, Coca-Cola, Amazon, Marriott Hotels, Starbucks, McDonald’s, and Netflix are just a few examples of big businesses that use data analytics to improve their operations. In addition, the practical applications of analytics-driven processes are increasingly used in insurance valuations, developing sensible manufacturer warranties, and improving the standard of medical treatments. Data Analytics empowers manufacturers to design beneficial products for customers, manage marketing initiatives for companies, utility firms to benefit from promoting sensible energy consumption.

The Data Analytics Process

Now let’s discuss some theoretical parts of data analytics. The data analytics process is a simple process with simple steps.

  1. Ask: Pose inquiries and identify the issue. The problem, the goal, and the question in business.
  1. Prepare: Collecting or utilizing data relevant to the problem you seek to address is what data preparation entails.
  1. Process: Data processing is the process of identifying and eliminating inaccuracies, errors, and inconsistencies in data such that our key business problem is not harmed.
  1. Analyze: Analyze data to look for patterns, connections, and trends. Data exploration, visualization, and analysis are all part of the process.
  1. Share: You can achieve this with the help of visualization, as presenting information in an image can make it easier for people to understand the analysis.
  1. Act: Take action based on the data and the analysis findings. Putting your knowledge to good use to solve the problem.
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Figure 2: Data Analytics Thought Process

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Figure 3: Data Analytics Process – Big Picture

The use of facts, measurements, and data to influence strategic business decisions that correspond with your goals, objectives, and projects is known as data-driven decision-making. When companies see the full value of their data, everyone, whether a business analyst, a sales manager or a human resource professional, is empowered to make better data-driven choices on a daily basis. This, however, cannot be accomplished just by selecting the right analytical tool to uncover the next strategic opportunity. Today, every sector aspires to be data-driven. Most professionals are aware that, among other things, data bias and erroneous assumptions may impair judgment and contribute to poor decision-making. Despite this, 58 per cent of respondents in a recent study claimed that their organizations make at least half of their routine business choices based on gut feel or intuition rather than evidence.

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Figure 4: Analytical Thinking of Data-driven Decision-making

Data Analytics Tools

In recent times job requirements have arisen as a result of the increased demand for the data analytics skills in the industry. Finding the best data analytics tools on the other hand is a little more difficult because free source solutions are more popular, user-friendly, and performance-oriented than premium versions. Many free source technologies, such as R programming in data mining and Tableau public, Python in data visualization, don’t require much/any coding and offer better results than premium versions. Based on their popularity, learning, and performance, the following is a list of the top 10 data analytics tools, both free source and paid versions.

Data Analytics Tool Category
R-StudioStatistical analysis tool
PythonPropose programming language
SQLStructured query language
Microsoft ExcelSpreadsheet application
Power BiBusiness intelligence tool
TableauBusiness intelligence tool
datapineBusiness intelligence tool
MySQL WorkbenchSQL console
SASStandalone predictions analytics tool
TalendETL tool
RapidMinerData science platform
OpenRefineData cleaning tool
Apache SparkData Analytics engine
JenkinsAutomation tool

Documentaries and movies about Data Analytics

Here you will find some of the best data-driven documentaries and films.

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