Section 6.3 Review Questions
What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?
1. Traditional ways of capturing, storing, and analyzing data are not sufficient for Big Data. 2. Major challenges are : a. the vast amount of data volume, b. the need for data integration to combine data of different structures in a cost-effective manner, c. the need to process data quickly, d. data governance issues, e. skill availability, f. and solution costs.
What is Big Data analytics?
A. Big Data analytics is analytics applied to Big Data architectures. 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.
What are the critical success factors for Big Data analytics?
Critical factors include a 1. 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.
How does big data analytics differ from regular analytics?
a. 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. b. These techniques are collectively called high-performance computing, and include in-memory analytics, in-database analytics, grid computing, and appliances. c. They differ from regular analytics which tend to focus on relational database technologies.
What are the common business problems addressed by 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