Home > Digital Marketing > Digital Transformation for Leaders > Business Analytics and Big Data - Digital Transformation
Business Analytics and Big Data - Digital Transformation
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.
Define big data and the 4 V's of big data
Explain big data analytics and its importance
Discuss different types of business analytics
Describe the analytics process flow
Explain how companies are using their big data to obtain business benefits
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.
The 4 V's of Big Data
Let's look at the 4 V's of Big Data
Volume refers to the scale of data: The main characteristic that makes data "big" is its sheer volume. Every day, there is an exponential rise in data volumes as organizations today have multiple internal and external sources, such as transactions, social media, enterprise content, sensors, and mobile devices.
Variety refers to the different forms of data: Data is captured from multiple sources in multiple formats. It can be structured, semi-structured, and unstructured.
Structured Data refers to information with a high degree of organization so that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operations.
Semi-structured Data is a form of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables but nonetheless contains tags or other markers.
Unstructured Data (or unstructured information) is information that either does not have a predefined data model or is not organized in a pre-defined manner.
Velocity refers to the analysis of streaming data. Velocity is the frequency of incoming data that needs to be processed. Think about how many SMS messages, Facebook status updates or credit card swipes are being sent on a particular telecom carrier every minute of every day, and you'll have a good appreciation of velocity.
Veracity refers to the uncertainty or trustworthiness of data.
Big Data Technology
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:
Considered a popular platform for data analytics and data exploration and
Used to implement data mining techniques and data processing
It handles data at extreme scale and is characterized by:
Massive parallel computing to divide and conquer the workloads
Flexibility to allow unlimited data manipulation and transformation
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.
Keen about Big Data Analytics? Follow the Digital Transformation course preview here to know more!
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 Science as a Strategic Asset
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:
Ability to extract useful information from data
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:
Prescriptive Analytics
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:
Data-mining techniques
Data queries
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.
Application of Business Analytics
Earlier, credit cards essentially had uniform pricing because:
The companies did not have appropriate information systems to deal with differential pricing on a large scale.
The bank management believed that customers would not accept price discrimination.
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:
Experimented by offering different terms to different customers at random, which increased the number of bad accounts.
Changed to about 6% charge-offs from the prevailing charge-off.
Worked toward building predictive models from the data, evaluating them, and then developing them to increase profits despite continuous losses.
Eventually turned around the credit card portfolio into its most profitable operation.
Now, that you have learned the importance of data analytics, let's understand the analytics process flow.
Analytics Process Flow
This is the analytics process flow diagram. Let's understand the steps in detail.
Data Source (ERP, CRM, Excel, etc)
It acts as a primary location for the data source.
It has numerous forms, like a dataset, a program, or perhaps hard-coded data.
Organizations might leverage multiple sources of data, together with ERP, CRM, and alternative systems.
Data Acquisition (ETL)
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 (Data Warehousing)
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 Reporting and Visualization (Reports and dashboards)
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.
Need more information about the Digital Transformation course? Click to explore!
How do Companies Deal with Big Data?
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.
How do Companies Deal with their Big Data?
Amazon, a pioneer in e-commerce, puts Big Data analytics to use in two key areas of its operations:
Data Warehousing (Redshift)
Hosted Hadoop solution (Elastic MapReduce)
It uses big data analytics to:
Create a personalized recommendation system
Improve customer service operations
This has helped Amazon to serve its customers effectively and improve sales.
Now, let's summarize what you have learned.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.
Volume, Variety, Velocity, and Veracity are the four V's of big data.
Analytics is the discovery and communication of meaningful patterns in data to drive smart decisions.
To perform analysis of big data, companies use a robust analytic environment that includes descriptive analytics, predictive analytics, and prescriptive analytics
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.'