Lecture 2: Business Motivations and Drivers for Big Data Adoption PDF

Lecture 2: Business Motivations and Drivers for Big Data Adoption DSC650: Data Technology and Future Emergence Lecturer: Dr. Khairul Anwar Sedek  DSC650 Course Contents Overview of Data Technology >> Business Motivations and Drivers for Big Data Adoption Data Storage Technology Data...

Lecture 2: Business Motivations and Drivers for Big Data Adoption DSC650: Data Technology and Future Emergence Lecturer: Dr. Khairul Anwar Sedek  DSC650 Course Contents Overview of Data Technology >> Business Motivations and Drivers for Big Data Adoption Data Storage Technology Data Munging NoSQL Searching and Indexing Big Data Big Data Technologies Trend in Data Technology Lecture 2: Business Motivations and Drivers for Big Data Adoption 2.1 Marketplace Dynamic 2.2 Business Process Management 2.3 Data Analytics and Data Science 2.4 Digitization 2.5 Internet of Everything At the end of the lecture, students should be able to; CLO1: Demonstrate an understanding on the basic concepts and practices of big data technology Business Motivations and Drivers for Big Data Adoption 2.1 MARKETPLACE DYNAMIC Marketplace Dynamics BI vs Data Analytics BI vs Data Analytics  Marketplace Dynamics Make decisions regarding corporate strategy, policy, goals and objectives Leverage this knowledge to generate priorities and actions that conform to Generate corporate measureable direction change in the experience of internal stakeholders and external customers The creation of a virtuous cycle to align an organization across layers via a feedback loop Business Motivations and Drivers for Big Data Adoption 2.2 BUSINESS PROCESS MANAGEMENT  2.3 Business Process Management Business process – description of how work is performed in an organization. All work-related activities and their relationships, aligned with the organizational actors and resources. Models of a business process are joined with -models of organizational roles and structure, business entities and their relationships, business rules and the user-interface. Business Process Management Detailed analysis could leverage customer clustering, which would assign individual customers to groups where one of the cluster dimensions is the contact method. Business Motivations and Drivers for Big Data Adoption 2.3 DATA ANALYTICS & DATA SCIENCE  The need for techniques and technologies that can extract 2.3 Data meaningful information and insights has increased. Analytics & Computational approaches, statistical techniques and data warehousing have merged, each bringing their Data Science specific techniques and tools that allow Big Data analysis.  Data Analytics & Data Science Collecting secondary data in digitised medium is important for businesses because mining this data can allow for customized marketing, automated recommendations and the development of optimized product features. Technology capable of storing and processing large quantities of diverse data has become increasingly affordable. – combination of commodity hardware and open source software Businesses are increasingly interested in incorporating publicly available datasets from social media and other external data sources. – Analysis of “voice of the customer” for better service, increase sales, targeted marketing and create new products and services. Vast collection of smart Internet-connected devices resulted in a massive increase in the number of available data streams. Leveraging cloud computing to create on premise clouds to more effectively utilize infrastructure via virtualization. – Infrastructure, storage and processing capabilities.  What is Data Science? Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.  What is a Data Analytics? A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science.  Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and Data several other related disciplines. While a data scientist is expected to Science vs. forecast the future based on past Data patterns, data analysts extract meaningful insights from various data Analytics sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions Data Analytics & Data Science The need for techniques and technologies that can extract meaningful information and insights has increased. Computational approaches, statistical techniques and data warehousing have merged, each bringing their specific techniques and tools that allow Big Data analysis. Data Analytics & Data Science Collecting secondary data in digitised medium is important for businesses because mining this data can allow for customized marketing, automated recommendations and the development of optimized product features. Technology capable of storing and processing large quantities of diverse data has become increasingly affordable. – combination of commodity hardware and open source software Businesses are increasingly interested in incorporating publicly available datasets from social media and other external data sources. – Analysis of “voice of the customer” for better service, increase sales, targeted marketing and create new products and services. Vast collection of smart Internet-connected devices resulted in a massive increase in the number of available data streams. Leveraging cloud computing to create on premise clouds to more effectively utilize infrastructure via virtualization. – Infrastructure, storage and processing capabilities. Business Process Management Analytics Architecture Data flow from left to right (in a typical organization) Business Motivations and Drivers for Big Data Adoption 2.4 DIGITIZATION  Digitization For many businesses, digital mediums have replaced physical mediums as the de facto communications and delivery mechanism. The use of digital artifacts saves both time and cost as distribution is supported by the vast pre-existing infrastructure of the Internet. As consumers connect to a business through their interaction with these digital substitutes, it leads to an opportunity to collect further “secondary” data; for example, requesting a customer to provide feedback, complete a survey, or simply providing a hook to display a relevant advertisement and tracking its click-through rate. Collecting secondary data can be important for businesses because mining this data can allow for customized marketing, automated recommendations and the development of optimized product features A visual representation of examples of digitization. Digital Transformat ion Trends Business Motivations and Drivers for Big Data Adoption 2.5 INTERNET OF EVERYTHING  Internet of Everything (IoE) The convergence of advancements in information and communications technology, marketplace dynamics, business architecture and business process management all contribute to the opportunity of what is now known as the Internet of Everything (IoE). Big Data is the heart of Internet of IoE. System of systems. IoE combines the services provided by smart connected devices of the IoT into meaningful business processes - to provide unique and differentiating value propositions. One example of IoE – precision agriculture – GPS-controlled tractors, in-field moisture and fertilization sensors, on-demand watering, fertilization, pesticide application systems and rate seeding equipment – Maximize field productivity ; minimizing cost.  Internet of Everything Taxonomy of the increasing levels of smartness for things in the Internet of Everything *BM: business model Source: David J. Langley, Jenny van Doorn, Irene C.L. Ng, Stefan Stieglitz, Alexander Lazovik, Albert Boonstra, The Internet of Everything: Smart things and their impact on business models, Journal of Business Research, Volume 122, What is IoE? IoT - Internet of Things | What is IoT? | IoT Explained in 6 Minutes | How IoT Works IoE – General Architecture The Growing of IoE https://www.techzone360.com/topics/techzone/ articles/2013/06/20/342857-cisco-internet- everything-ioe-study-says-profits-will.htm