Introduction to Big Data for Business Intelligence

The terms data, datasets, and databases are introduced. A relevant question, "How big is big data? is asked and answered.

Data science is defined.

The history and development of data science and the types of data are discussed.

Discussion on Data Science PROCESSES.

The 7 characteristics of big data, or 7Vs, are discussed.

Discussing market strategies for data science.

Learning outcomes are summed up.

The learning objectives of Module 2 are detailed here.

The data science life cycle is explained in this lecture.

The differences and synergies between data science and statistical methods are discussed in this lecture.

The skill sets required for a prospective data scientist are discussed.

The roles that data scientists occupy in businesses are discussed.

The roles of big data professionals in businesses are discussed.

The symbiotic relationship between big data and data science is discussed.

The value addition that big data and data science give to businesses is discussed.

The learning outcomes of module 2 are summarised.

The features of RDBMS and NoSQL and their relevance to big data are discussed.

Features of stream processing, Apache Kafka, and Apache Flink are discussed. Its applications for BI are also discussed.

This unit deals with the topics of machine learning and predictive models and their importance and impacts on businesses.

This lecture provides an overview of deep learning models, highlighting their principles, types, applications, and future directions, showcasing their profound impact on various industries and the potential for future advancements in the field of artificial intelligence.

This lecture provides an overview of graph analytics, highlighting its principles, types, applications, and future directions, and showcasing its significance in uncovering insights within interconnected data structures and networks.

This lecture provides an overview of big data frameworks, highlighting their principles, common frameworks, impact, and future directions, and showcasing their significance in empowering scalable and efficient data processing in the era of big data.

The "9S of Big Data Framework" collectively represent the foundational pillars that underpin effective big data management, processing, and utilization. These components guide the development and deployment of robust frameworks tailored to handle the challenges posed by large volumes of data, enabling organizations to derive actionable insights and drive innovation in today's data-driven world.

This lecture delves into the multifaceted roles that managers play in the realm of big data, emphasizing their techno-cultural responsibilities in steering organizations toward leveraging data as a strategic asset for growth and innovation.

The learning outcomes of this module are summarized here.

The learning objectives of Module 4 are given.

API stands for Application Programming Interface. In the context of data analytics, an API refers to a set of protocols, tools, and definitions that allow different software applications or platforms to communicate with each other and exchange data or functionalities.

Ingesting data through file transfer mechanisms like FTP (File Transfer Protocol), SCP (Secure Copy Protocol), or direct copying of files from one storage location to another There are various protocols and methods used for file transfer and copying data from one location to another. Here are some common examples of file transfer and copying protocols.

Data governance frameworks, security measures, access controls, and compliance policies are integral components to ensure data integrity, privacy, and regulatory compliance within the architecture. Data governance and security are crucial aspects of managing and protecting data within an organization. Here are examples of practices, strategies, and tools commonly used for data governance and security.

Advanced analytics tools, machine learning libraries, and visualization platforms are integrated to derive actionable insights from the stored data, aiding decision-making processes.

Ingesting data generated by Internet of Things (IoT) devices, such as sensors, wearables, or smart devices, to gather real-time telemetry, environmental, or operational data.

Storage forms the foundation of big data architecture. Distributed file systems like Hadoop Distributed File System (HDFS), cloud-based storage solutions, NoSQL databases, and data lakes store vast volumes of structured, semi-structured, and unstructured data.

Big Data architectures integrate processing engines like Apache Spark, Apache Flink, Hadoop MapReduce, and distributed computing frameworks.

In this lecture, the various salient features of big data architecture are discussed.

In this short lecture, the importance and impacts of big data architecture are briefly discussed.

This lecture briefly addresses the future directions and advancements of big data architecture.

We conclude the module by summing up the learning points.

The learning objectives of Module 5 are given.

Global markets witnessed the advent of point-of-sale (POS) scanners in the 1980s. The data gathered from POS changed the balance of power between consumer packaged goods (CPG) manufacturers like Procter & Gamble and Unilever and retailers like Walmart, Tesco, and Vons, etc. The emergence of new and detailed sources of data about product sales and customer loyalty data gave retailers unique insights into product sales, customer preferences, patterns, trends, and loyalty that were unavailable to any player in the CPG manufacturing to a retail market chain.

The big data business model provides a benchmark against which organizations can measure big data-enabled upfront opportunities. Different organizations take different pragmatic approaches to move at different paces adopting big data and advanced analytics to create competitive advantages.

The Business Insights phase enables leveraging new and unstructured data sources with advanced statistics, predictive analytics, and data mining. The new data can be integrated into key business processes. At the Business Optimization phase organizations use embedded analytics to automatically optimize parts of their business operations.

Let us remember that in this age data is money and power. This happens when organizations are in a stage to monetize data. So, the monetization phases are those at which organizations leverage big data as revenue opportunities. The ultimate stage that a business organization aims at through big data analytics is the Business Metamorphosis phase.

It is pertinent to ask how you transit to the business metamorphosis phase. For this ultimate transit to happen, the organizations need to think about moving away from a product-centric business model to a more platform- or ecosystem-centric business model.

It can be observed that the first three phases of the Big Data Business Model Maturity Index are internally focused on optimizing an organization’s internal business processes.

Data monetization is certainly the holy grail of the big data discussion. How do I leverage my vast wealth of customer, product, and operational insights that are provided by the big data analytics horizon to create new revenue-generating products and services?

Successful big data organizations continuously uncover and publish new customer, product, operational, and market insights about the business. Consequently, these business firms establish a comprehensive process so that the roles, responsibilities, and expectations of all key stakeholders can be visualized in the lifecycle diagram.

The Big Data Business Model provides a benchmark against which organizations can measure big data-enabled upfront opportunities.

The learning objectives of this module are briefed here in this session.

Problem formulation is a foundational step in the realm of decision analysis. It involves defining and structuring the decision problem in a clear, precise, and comprehensive manner. This initial phase sets the stage for the entire decision-making process, influencing subsequent steps and the ultimate quality of the decision reached.

We discuss several quantitative approaches that model different risk behaviors for making decisions involving uncertainty when no probabilities can be estimated for the outcomes.

A third approach that underlies decision-making choices for many individuals is to consider the opportunity loss associated with a decision. Opportunity loss represents the “regret” that people often feel after making a nonoptimal decision (I should have bought that stock years ago!).

When the objective is to maximize the payoff, we can still apply aggressive, conservative, and opportunity loss strategies, but we must make some key changes in the analysis. • For the aggressive strategy, the best payoff for each decision would be the largest value among all outcomes, and we would choose the decision corresponding to the largest of these, called a maximax strategy.

A useful approach to structuring a decision problem involving uncertainty is to use a graphical model called a decision tree.

The learning outcomes of this module are detailed here.

The integration of technology into the healthcare sector has sparked a transformative revolution, reshaping the delivery of medical services, patient care, and research methodologies. This essay delves into the realm of technology-driven healthcare, exploring its evolution, impact, and future prospects in revolutionizing the healthcare landscape.

Healthcare generates enormous volumes of data from electronic health records (EHRs), medical imaging, genomic sequencing, wearable devices, and more. MapReduce's parallel processing framework efficiently handles and processes these large-scale datasets, enabling faster data ingestion, transformation, and analysis.

In the era of big data, the healthcare industry faces the challenge of managing and analyzing massive volumes of information efficiently. Apache Spark, an open-source distributed computing framework, has emerged as a powerful tool revolutionizing data processing and analytics. This essay explores how Apache Spark is reshaping the landscape of healthcare by enhancing data processing, enabling real-time analytics, and fostering innovations in patient care, research, and operational efficiency.

The Aarogya Setu app emerged as a pivotal tool in India's battle against the COVID-19 pandemic, aiming to empower citizens with vital health-related information, contact tracing, and self-assessment features. This essay explores the significance, functionalities, and impact of the Aarogya Setu app in India's public health strategy during the pandemic.

The course outcome has been briefed.

Big Data is changing everything, from the way shops and banks operate to offering hospitality, ensuring healthcare for cancer patients, combating terrorism, managing avionics, and mining the secrets of the universe.

1. To understand how business intelligence leverages business output through big data capabilities in different domains such as retail business (WALMART), high-end research (CERN), transport sector (Rolls Royce), media streaming business (NETFLIX), and social media (Facebook), explained in detail.

2. The learner will be able to understand and compare the growth of very important industrial giants by embracing big data capabilities.

3. It is expected to give conceptual, cognitive, and affective behavioral changes to the learners.

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores. Its home office is in Bentonville, Arkansas. The company was founded by Sam Walton in 1962 and incorporated on October 31, 1969. Walmart is the largest retailer in the world and the world’s largest company by revenue, with over 52,396.4 crores USD (2020). Over two million employees work with it now in over 20,000 stores worldwide.

CERN is a laboratory established in 1954 that is an example of international research collaboration. At CERN, researchers explore to uncover what the universe is made of and how it works. They do it by providing a unique range of particle accelerator facilities to researchers to advance the boundaries of human knowledge. CERN operates the Large Hadron Collider (LHC), humanity’s biggest and most advanced physics experiment toward unraveling the secrets of the universe.

NETFLIX is an American visual media service provider with a home office in Los Gatos. It was founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California. The company's business is the subscription-based streaming of a library of films and television programs. As of April 2020, Netflix had over 182 million paid subscriptions worldwide, including 69 million in the United States, and an annual revenue of over 20 million USD.

Rolls-Royce Motor Cars Limited is a British luxury automobile company that was re-established in 1998 after BMW licensed the rights to the Rolls-Royce brand name and logo. Rolls-Royce Motors Cars Limited is the exclusive manufacturer of Rolls-Royce branded motor cars since 2003.

Facebook is one of the world’s biggest social networks. Millions of people every day use it to read news, interact with brands, and make buying decisions. As per a statistical report published in 2020, Facebook has 1.73 billion daily active users. The revenue they generated in the first quarter of 2020 was 17.44 billion USD. Like all of the big social networks and search engines, it’s essentially free for the end user. The company makes money from ad revenue and from the data monetization business.

We have explored how some big businesses leveraged their growth by using big data analytics. We have analyzed a broad spectrum of businesses ranging from heavy businesses to soft businesses to understand the tremendous strides achieved through robust research and application of big data analytics. We could establish that big data is the key to any business that aspires to grow.

Recent trends in AI assisted Predictive Analytics for business insights are discussed.

This lecture explains how PYTHON will be a valuable tool for data managers in embracing actionable business insights.