WHAT IS BIG DATA ANALYTICS DIRECT QUOTES FROM

WHAT IS BIG DATA ANALYTICS? DIRECT QUOTES FROM SOURCE: TERADATA, 2017, URL: HTTP: //BIGDATA. TERADATA. COM/US/BIG-IDEAS/BIG-DATAANALYTICS/, TERADATA. PREPARED BY: CELESTE NG

WHAT IS BIG DATA ANALYTICS? • [There are] many metaphors apply. – If big data is a haystack, analytics is how you find the needle. – If it’s a huge wave, analytics is a surfboard. – If it’s noise, analytics lets you hear the signal. • … it’s best to think of big data analytics in terms of value-adding actions that actually move the business forward. • … the big data obstacles: – Companies spend too much time, effort and money on big data preparation and loading, and not nearly enough on applying analytics to find difference-making insights. – To get there, companies need to find tools to make the process of data preparation more efficient. This will greatly increase the organization’s “analytical agility. ”

MANY FORMS OF BIG DATA ANALYTICS • …. Different types of big data analytics are best used in different contexts. … it comes down to business problems and objectives. Are users seeking: – Temporal patterns or geographical views of market data? – Procedural insights from machine logs or sensor data? – Correlations of behavioral patterns for a single product, multiple products or a yet-to-be-launched product? • Big data analytics is often about predictive capabilities – – recommendation engines on ecommerce sites. – operational actions guided by market sensitivity. – Gaining deeper understanding of the structure and nature of relationships between people and processes and defining patterns that lead to user-defined outcomes.

PREDICTIVE ANALYTICS • Produces Big ROI • Yahoo! Japan applies big data analytics tools for – deep insights into customer behaviors and – tailor services and – target ads – --> leading to $100 million ROI. • You. Tube: https: //www. youtube. com/watch? v=DGXODb_Ol 8 g

BIG DATA ANALYTICS IN ACTION • Testing and Failing Faster – – R&D leaders can test their hypotheses before making big-bet investments. – For instance, pharmaceuticals can use big data analytics [to identify and prevent potential risk] … when testing new medications. • Finding “Win-Win” Alternative Treatments – By mapping broad and multi-sourced patient data sets, providers and healthcare organizations can find more effective (and cost-effective) treatments – e. g. , – pain management techniques or physical therapy versus surgery. Good for patients. Good for payers. • Richer Portraits of Customer Profitability – Beyond churn risk metrics, there is competitive advantage when marketing knows which customers are worth keeping with lavish loyalty programs vs. those high-maintenance hagglers [price bargainer 砍價] that the competition really deserve. • Modeling for Black Swans* - Insurers can apply advanced risk modeling techniques to big data – to adjust capital reserves in advance of “black swan” scenarios or – to strengthen anti-fraud capabilities by correlating their claims data. * is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized. . . …. The disproportionate role of high-profile, hard-to-predict, and rare events that are beyond the realm of normal expectations in history, science, finance, and technology ----- source: https: //en. wikipedia. org/wiki/Black_swan_theory