Chapter 6 End of Chapter Questions

How do big data analytics typically differ from regular analytics?

. They differ from regular analytics which tend to focus on relational database technologies.

2. What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

1. Big Data could evolve at a rapid pace. 2. The buzzword "Big Data" might change to something else, but the trend toward increased computing capabilities, analytics methodologies, and data management of high volume heterogeneous information will continue.

Why have big data analytics been created?

1. This is a new paradigm; in order to keep up with the computational needs of Big Data, a number of new and innovative analytics computational techniques and platforms have been developed.

4. What are the critical success factors for Big Data analytics?

1. a clear business need, 2. strong and committed sponsorship, 3. alignment between the business and IT strategies, 4. a fact-based decision culture, 5. a strong data infrastructure, 6. the right analytics tools, 7. and personnel with advanced analytic skills.

Why is big data important?

1. important because it is there and because it is rich in information and insight that, if effectively tapped, can lead to better business decisions and improved company performance. 2. Big Data trend and how it can serve as the basis for innovation, differentiation, and growth.

Where does big data come from?

1. includes both structured and unstructured data, 2. comes from everywhere: data sources include Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, and detailed call records, to name just a few. Big data is not just about volume, but also variety, velocity, veracity, and value proposition.

What are big data analytics techniques collectively called and what does it entail?

1. techniques are collectively called high-performance computing, 2. and include : a. in-memory analytics, b. in-database analytics, c. grid computing, and appliances.

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

A. Traditional ways of capturing, storing, and analyzing data are not sufficient for Big Data. B. Major challenges are : 1. the vast amount of data volume, 2. the need for data integration to combine data of different structures in a cost-effective manner, 3. the need to process data quickly, 4. data governance issues, 5. skill availability, and solution costs.

3. What is Big Data analytics?

Big Data analytics is analytics applied to Big Data architectures.

Big Data has become a popular term for what?

Big Data has become a popular term to describe the exponential growth, availability, and use of information, both structured and unstructured.

6. What are the common business problems addressed by Big Data analytics?

Here is a list of problems that can be addressed using 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 Big Data

a. Traditionally, the term "Big Data" has been used to describe the massive volumes of data analyzed by huge organizations like Google or research science projects at NASA. b. for most businesses, it's a relative term: "Big" depends on an organization's size. c. Big Data exceeds the reach of commonly used hardware environments and/or capabilities of software tools to capture, manage, and process it within a tolerable time span for its user population.