Big Data MBA: Driving Business Strategies with Data Science by Bill Schmarzo

By Bill Schmarzo

Integrate massive info into enterprise to force aggressive virtue and sustainable success

Big info MBA brings perception and services to leveraging substantial information in enterprise so that you can harness the ability of analytics and achieve a real enterprise virtue. in keeping with a realistic framework with aiding method and hands-on routines, this ebook is helping establish the place and the way sizeable info can assist remodel your enterprise. you are going to how one can take advantage of new assets of purchaser, product, and operational info, coupled with complex analytics and information technological know-how, to optimize key techniques, discover monetization possibilities, and create new assets of aggressive differentiation. The dialogue contains guidance for operationalizing analytics, optimum organizational constitution, and utilizing analytic insights all through your organization's person event to shoppers and front-end staff alike. you are going to discover ways to “think like a knowledge scientist” as you construct upon the choices your small business is attempting to make, the hypotheses you must try out, and the predictions you want to produce.

Business stakeholders now not have to relinquish keep watch over of information and analytics to IT. actually, they need to champion the organization's information assortment and research efforts. This publication is a primer at the enterprise method of analytics, delivering the sensible realizing you must convert information into opportunity.

  • Understand where and how to leverage sizeable data
  • Integrate analytics into daily operations
  • Structure your company to force analytic insights
  • Optimize approaches, discover possibilities, and stand proud of the rest
  • Help company stakeholders to “think like a knowledge scientist”
  • Understand acceptable company program of alternative analytic techniques

If you will have info to remodel your small business, you must understand how to place it to take advantage of. Big information MBA indicates you ways to enforce giant facts and analytics to make higher decisions.

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Extra resources for Big Data MBA: Driving Business Strategies with Data Science

Example text

Phase 2: Business Insights. The Business Insights phase is about leveraging predictive analytics to uncover customer, product, and operational insights buried in the growing wealth of internal and external data sources. In this phase, organizations aggressively expand their data acquisition efforts by coupling all of their detailed transactional and operational data with internal data such as consumer comments, e-mail conversations, and technician notes, as well as external and publicly available data such as social media, weather, traffic, economic, demographics, home values, and local events data.

Data science deals with schema on query in which the data scientists custom design the data schema based on the hypothesis they want to test or the prediction that they want to make. Organizations that try to “extend” their Business Intelligence capabilities to encompass big data will fail. That’s like stating that you’re going to the moon, then climbing a tree and declaring that you are closer. Unfortunately, you can’t get to the moon from the top of a tree. Data science is a new discipline that offers compelling, business-differentiating capabilities, especially when coupled with Business Intelligence.

Unfortunately, you can’t get to the moon from the top of a tree. Data science is a new discipline that offers compelling, business-differentiating capabilities, especially when coupled with Business Intelligence. CROSSREFERENCE Chapter 5 (“Differences Between Business Intelligence and Data Science”) discusses the differences between Business Intelligence and data science and how data science can complement your Business Intelligence organization. Chapter 6 (“Data Science 101”) reviews several different analytic algorithms that your data science team might use and discusses the business situations in which the different algorithms might be most appropriate.

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