By Bill Schmarzo
Integrate titanic facts into enterprise to force aggressive virtue and sustainable success
Big facts MBA brings perception and services to leveraging tremendous facts in enterprise so that you can harness the facility of analytics and achieve a real enterprise virtue. in accordance with a pragmatic framework with assisting technique and hands-on workouts, this booklet is helping establish the place and the way mammoth info may help rework your corporation. you are going to make the most new assets of purchaser, product, and operational information, coupled with complex analytics and information technology, to optimize key strategies, discover monetization possibilities, and create new assets of aggressive differentiation. The dialogue contains directions for operationalizing analytics, optimum organizational constitution, and utilizing analytic insights all through your organization's consumer event to shoppers and front-end staff alike. you will learn how to “think like a knowledge scientist” as you construct upon the selections your enterprise is attempting to make, the hypotheses you must try, and the predictions you must produce.
Business stakeholders not have to relinquish regulate of information and analytics to IT. actually, they need to champion the organization's facts assortment and research efforts. This ebook is a primer at the company method of analytics, delivering the sensible knowing you want to convert information into opportunity.
- Understand where and how to leverage vast data
- Integrate analytics into daily operations
- Structure your company to force analytic insights
- Optimize tactics, discover possibilities, and stand proud of the rest
- Help enterprise stakeholders to “think like an information scientist”
- Understand acceptable enterprise software of alternative analytic techniques
If you will want facts to remodel your small business, you must know the way to place it to exploit. Big information MBA indicates you ways to enforce large facts and analytics to make greater decisions.
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Extra resources for Big Data MBA: Driving Business Strategies with Data Science
Leveraging Technology to Power Competitive Diﬀerentiation While most organizations have invested heavily in ERP-type operational systems, far fewer have been successful in leveraging data and analytics to build strategic applications that provide unique value to their customers and create competitive differentiation in the marketplace. Here are some examples of organizations that have invested in building differentiated capabilities by leveraging new sources of data and analytics: ■ Google: PageRank and Ad Serving ■ Yahoo: Behavioral Targeting and Retargeting ■ Facebook: Ad Serving and News Feed ■ Apple: iTunes ■ Netflix: Movie Recommendations ■ Amazon: “Customers Who Bought This Item,” 1-Click ordering, and Supply Chain & Logistics ■ Walmart: Demand Forecasting, Supply Chain Logistics, and Retail Link ■ Procter & Gamble: Brand and Category Management ■ Federal Express: Critical Inventory Logistics ■ American Express and Visa: Fraud Detection ■ GE: Asset Optimization and Operations Optimization (Predix) None of these organizations bought these strategic, business-differentiating applications off the shelf.
Chapter 3 introduces the big data strategy document. The big data strategy document provides a framework for helping organizations identify where and how to start their big data journey from a business perspective. Chapter 1 ■ The Big Data Business Mandate Don’t Think Business Intelligence, Think Data Science Data science is different from Business Intelligence (BI). Resist the advice to try to make these two different disciplines the same. For example: ■ Business Intelligence focuses on reporting what happened (descriptive analytics).
These four big data value drivers are: 1. Access to All of the Organization’s Transactional and Operational Data. In big data, we need to move beyond the summarized and aggregated data that is housed in the data warehouse and be prepared to store and analyze the organization’s complete history of detailed transactional and operational data. Think 25 years of detailed point of sale (POS) transactional data, not just the 13 to 25 months of aggregated POS data stored in the data warehouse. Imagine the business potential of being able to analyze each POS transaction at the individual customer level (courtesy of loyalty programs) for the past 15 to 25 years.