Section 6.3 Fundamentals of big data Analytics

What is meant by big data analytics should have a clear business need?

business investments should be made for the good of the business, not for the sake of more technology advancements. The main driver for Big Data analytics should be the needs of the business, at any level- strategic, tactical, and operations.

What are In-database analytics?

placing analytic procedures close to where data is stored

What are in-memory analytics?

storing and processing the complete data set in RAM

What is meant by big data analytics should have a fact-based decision-making culture

the numbers rather than intuition, gut feeling, or supposition drive decision making -also a culture of experimentation to see what works and what doesnt

What is Grid computing and MPP?

use of many machines and processors in parallel MPP-massively parallel processing

what are the 10 business problems addressed by big data analytics

1.Process efficiency and cost reduction 2.Brand management 3.Revenue maximization, cross-selling/up-selling 4. Enhanced customer experience 5. Churn identification, customer recruiting 6.Improved customer service 7. Identifying new products and market opportunities 8. Risk management 9. Regulatory compliance 10. Enhanced security capabilities

T/F Big data by itself is very valuable.

- False, - Big data by itself regardless of the size, type, or speed is worthless.

What are the critical success factors for big data analytics (7)?

- a clear business need (alignment with vision and the strategy) - strong, committed sponsorship (executive champion) - alignment between the busines and IT strategy -a fact based decision making culture - a strong data infrastructure - the right analytics tools - right people with right skills

With the value proposition, big data also brought about what big challenges?

- effictively and efficiently capturing, storing, and analyzing big data could no longer be done using traditional means - new breed of technologies needed to be (developed or purchased or hired or outsourced) in order to deal with the big data challenge

What is meant by big data analytics should have strong, committed sponsorship.

- if you dont have strong, committed executive sponsorship, it is difficult to succeed - if the target is enterprise- wide organizational transformation, which is often the case for big data initiatives, sponsorship needs to be at the highest levels and oranization wide

what is meant by big data analytics should have alightment between the business and IT strategy

- it is essential to make sure that the analytics work is always supporting the business strategy - analytics should play the enabling role in successfully executing the business strategy

Under what circumstances should firms consider taking on a Big Data journey?

- you cant process the amount of data that you want to b/c of the limitations of your current platform -you cant include new/ contemporary data sources b/c it does not comply with the data storage schema - you need to (or want to) integrate data as quickly as possible to be current on your analysis. - you want to work with a schema-on-demand data storage paradigm b/c of the variety of data types involved. - the data is arriving so fast at your organizations doorstep that your traditional analytics platform cannot handle it

What is meant by big data analytics should have a strong data infrastrucuture

-data warehouses have provided the data infrastructure for analytics. This infrastructure is changing and being enhanced in the big data era with new technologies. Success requires marrying the old with the new for a holistic infrastructure that works synergistically.

Enablers of big data analytics: in order to keep up with the computational needs of Big Data, a number of new and innovative computational techniques and platforms have been developed, These techniques are collectively called High performance computing, which includes the following:

-in-memory analytics - in-database analytics -grid computing -appliances

What are the 6 challenges of Big Data Analytics?

1. Data volume *The ability to capture, store, and process the huge volume of data in a timely manner 2. Data integration *The ability to combine data that is not similar in structure or source quickly and at reasonable cost 3. Processing capabilities *The ability to process the data quickly, as it is captured (i.e., stream analytics) 4. Data governance (... security, privacy, ownership,access and quality issues of big data) 5. Skill availability (... shortage of data scientist who can do the job ) 6. Solution cost (ROI)