This is a Big Data tutorial offered by Simplilearn. The tutorial is part of the Digital Transformation course and will help understand the basics of Big Data Analytics with examples and learn its importance.
At the end of this lesson, you will be able to:
Disney has been creating magical guest experiences for over 90 years now.
However, in the 2000s, as ticket prices climbed and the customer base increased, the sparkle on Disney’s customer experience began to fade.
In response, Disney created the Next Generation Experience project aimed at creating personalized experiences for its guests. Disney launched MyMagic+, which allows guests to book ride times, restaurant reservations, and shows in advance using the website. The mobile app allows guests to change reservations in real time as needed.
The MagicBands given to the guests act as admission tickets. These can also be connected to their credit cards for payments across the parks and resorts.
What did you infer from the instance?
Disney’s success story gives us insights into how technology proves very important in improving customer experience and in retaining customers.
As Bob Iger, Chairman & CEO, The Walt Disney Company rightly said:
“Technology is lifting the limits of creativity and transforming the possibilities for entertainment and leisure.”
In Spite of the big customer base resulting in huge data, Disney was able to extract outcomes to create magic again. Without analyzing the data it had, it would have been difficult to provide any solution to the problem. From this example, you would have understood how crucial it is for businesses to manage their data flow and gain insights to improve their business using various technologies. Large organizations have a huge customer base, resulting in huge incoming data, also known as big data.
The term big data represents extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations. It describes any voluminous amount of structured, semi-structured, and unstructured data that has the potential to be mined for information.
Big data is defined by four important features also known as the 4 V’s.
Let’s look at the 4 V’s of Big Data
Every business now has access to a huge amount of data, both structured and unstructured, that can be increasingly utilized with big data analytical tools for harvesting insights and supporting business decisions.
Big data technologies are:
It handles data at extreme scale and is characterized by:
Big Data technologies allow the organizations to make new kinds of predictions, uncover patterns in business activities, and unlock new sources of value. The scientific term is called Data science.
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Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
Data has today become a strategic asset to be mined for business intelligence and differentiate products in the market.
With respect to Data Science, the key strategic assets are:
Many businesses use data analytics to get value from the existing data and to consider if the business has the right analytical talent generally. Incorrect data and unsuitable Data Science talent result in poor business decisions.
Data Analytics is the discovery and communication of meaningful patterns in data to drive smart decisions. Companies are turning to different types of analytics solutions to uncover hidden patterns, meaning, and other insights from the huge volumes of data to improve decision making.
To perform analysis of big data, companies use a robust analytic environment that includes:
Descriptive Analytics uses tools like data aggregation and data mining. These are used to summarize the results.
Predictive Analytics uses statistical models and simulation, which help in making educated guesses at likely results.
Prescriptive Analytics uses tools like optimization models and Heuristics that help to derive important, complex, and sensitive decisions.
Now, let’s understand the techniques used in these data analytics versions.
It is regarded as the first stage in the analysis of data, which involves consolidation and summarization of data for further analysis. Descriptive Analytics includes techniques that explain what has happened in the past. These techniques include:
The data that is received from descriptive analytics is further used for predictive analytics, where the objective is to predict future unforeseen events. Predictive Analytics includes techniques that use models created from past data to predict the future or determine the impact of one variable on another. For instance:
A company can create a mathematical model for predicting the future sales by using past data on product sales.
A food manufacturing company can estimate the measurement and quantity of unit sales by using the point-of-sale scanner data from retail outlets.
Prescriptive Analytics specifies the best course of action for a business activity in the form of the output of a prescriptive model. The models used in this analytics are called optimization models. They are used mostly in:
The airline industry: Help to fetch the best pricing strategy across flights through the analysis of revenue management models and past purchasing data
Finance: Used to decide the mix of investments through the analysis of portfolio models that utilize historical investment return data
Let’s understand the application of business analysis through an example.
Earlier, credit cards essentially had uniform pricing because:
Richard Fairbanks and Nigel Morris approached big banks to offer predictive modeling consulting. They finally got Signet Bank, a regional Virginia bank, to agree and convinced the bank that modeling profitability was the right strategy to get ahead.
However, the bank had data only to model profitability for the terms it had offered in the past and types of customers who were offered credit.
Using predictive modeling, the bank:
Now, that you have learned the importance of data analytics, let’s understand the analytics process flow.
This is the analytics process flow diagram. Let’s understand the steps in detail.
It involves Extract, Load, and Rework. it is an information integration method for transferring raw data from supply systems to a target information and making the data ready for downstream uses, primarily in business intelligence and analytics applications.
Data Storage is the method of archiving data in electromagnetic or alternative forms by a laptop or associate degree device.
A data warehouse is regarded as a central repository of integrated information from one or additional disparate sources. It is a system used for reportage and information analysis and considered one of the core element of business intelligence.
Data Analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting the decision-making process.
Data Visualization is the procedure of exhibiting and presenting data in a graphical format.
It is used as a measure to deliver visual reports to users. It gives insights relating to performance, operations, or general statistics of an application.
Let’s understand how companies deal with their big data to draw insights and how it has helped them.
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Rolls-Royce has put Big Data analytics to use in three key areas of its operations:
Adopting this Big Data-driven approach has helped the company to diagnose faults, correct them, and prevent them from occurring again. It has also helped in taking the right business decisions, resulting in significantly reduced costs.
Amazon, a pioneer in e-commerce, puts Big Data analytics to use in two key areas of its operations:
It uses big data analytics to:
This has helped Amazon to serve its customers effectively and improve sales.
Now, let’s summarize what you have learned.
With this, we come to the end of the ‘Business Analytics and Big Data’ tutorial. In the next chapter, we will discuss 'Artificial Intelligence and Machine Learning.'
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