BI Chapter 13

Out of the Vs that are used to define Big Data, in your opinion, which one is the most important? Why?

Volume, Variety, Velocity, Veracity, Variability, Value Proposition. -Value Proposition: A preconceived notion about "big" data is that it contains more patterns and interesting anomalies than "small" data. By analyzing large and feature rich data, organizations can gain greater business value that they may not have otherwise. Users can detect the patterns in small data sets using simple statistical analytics. Big analytics means greater insight and better decisions, something that every organization needs.

What are the critical success factors for Big Data analytics?

-A clear business need (alignment with the vision and the strategy) -Strong, committed sponsorship (executive champion) -Alignment between the business and IT strategy -A fact-based decision making culture -A strong data infrastructure

What is MapReduce? What does it do? How does it do it?

-A technique to distribute the processing of the very large multi-structured data files across a large cluster of machines. -Aids organizations in processing and analyzing large volumes of multi-structured data. -Reads the input file and splits it into multiple pieces. These splits are then processed by multiple map programs running in parallel on the nodes of the cluster. .Groups data in a split by the type of geometric shape Takes output from each map program, which calculates the sum of the number of different types of geometric shapes.

What is stream analytics? How does it differ from regular analytics?

-A term commonly used for extracting actionable information from continuously flowing/streaming data sources. -The science of analysis--to use data for decision making

What is Hadoop? How does it work?

-An open source framework for processing, storing, and analyzing massive amounts of distributed, unstructured data. -A client accesses unstructured and semistructured data from sources including log files, social media feeds, and internal data stores. It breaks the data up into "parts," which are then loaded into a file system made up of multiple nodes running commodity hardware.

What is Big Data analytics? How does it differ from regular analytics?

-Data that exceeds the reach of commonly used hardware environment and/or capabilities of software tools to capture, manage, and process it within a tolerable time span -The science of analysis--to use data for decision making

What are the use cases for Big Data and Hadoop?

-Data warehouse performance -Integrating data that provides business values -Interactive BI tools

What is NoSQL? How does it fit into the Big Data analytics picture?

-Not Only SQL. Processing large volumes of multi-structured data. -Serving up discrete data stored among large volumes of multi-structured data to end-users and automated Big Data applications. -Can work in conjunction with Hadoop.

What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

-Process efficiency and cost reduction -Brand management -Revenue maximization, cross-selling, and up-selling -Enhanced customer experience -Churn identification, customer recruiting -Improved customer service -Identifying new products and market opportunities -Risk management -Regulatory compliance -Enhanced security capabilities

What is special about the Big Data vendor landscape? Who are the big players?

-Vendor's are able to develop their own hadoop distributions, based on the Apache open source distribution, but with various levels of proprietary customization. -Cloudera, MapR, Hortonworks

Why is Big Data important? What has changed to put it in the center of the analytics world?

As more and more data becomes available in various forms and fashions, timely processing of the data with traditional means becomes impractical. The exponential growth, availability, and use of information, both structured and unstructured, brings Big Data to the center of the analytics world. Pushing the boundaries of data analytics uncovers new insights and opportunities for the use of Big Data.

What are the common characteristics of data scientists?

Intense curiosity, creativity, communication,/interpersonal, domain expertise, problem definition, managerial, technical skills (data manipulation, programming/hacking/scripting, internet and social media/networking)