MARC details
| 000 -LEADER |
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| fixed length control field |
04377 a2200169 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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131018b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978-1-4221-8725-8 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.4033 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
DAVENPORT, THOMAS H.: KIM, JINHO |
| 9 (RLIN) |
11189 |
| 245 ## - TITLE STATEMENT |
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| Title |
KEEPING UP WITH THE QUANTS: YOUR GUIDE TO UNDERSTANDING + USING ANALYTICS |
| Statement of responsibility, etc |
DAVENPORT, THOMAS H. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
HARVARD BUSINESS REVIEW PRESS |
| Place of publication, distribution, etc |
BOSTON, MASSACHUSETTS |
| Date of publication, distribution, etc |
2013 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
228 |
| Other physical details |
HARD |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
<br/><br/>“I don’t know why we didn’t get the mortgages off our books,” a senior quantitative analyst at a large U.S. bank told me a few years ago. “I had a model strongly indicating that a lot of them wouldn’t be repaid, and I sent it to the head of our mortgage business.”<br/><br/>When I asked the leader of the mortgage business why he’d ignored the advice, he said, “If the analyst showed me a model, it wasn’t in terms I could make sense of. I didn’t even know his group was working on repayment probabilities.” The bank ended up losing billions in bad loans.<br/><br/>We live in an era of big data. Whether you work in financial services, consumer goods, travel and transportation, or industrial products, analytics are becoming a competitive necessity for your organization. But as the banking example shows, having big data—and even people who can manipulate it successfully—is not enough. Companies need general managers who can partner effectively with “quants” to ensure that their work yields better strategic and tactical decisions.<br/><br/>For people fluent in analytics—such as Gary Loveman of Caesars Entertainment (with a PhD from MIT), Jeff Bezos of Amazon (an electrical engineering and computer science major from Princeton), or Sergey Brin and Larry Page of Google (computer science PhD dropouts from Stanford)—there’s no problem. But if you’re a typical executive, your math and statistics background probably amounts to a college class or two. You might be adept at using spreadsheets and know your way around a bar graph or a pie chart, but when it comes to analytics, you often feel quantitatively challenged.<br/><br/>So what does the shift toward data-driven decision making mean for you? How do you avoid the fate of the loss-making mortgage bank head and instead lead your company into the analytical revolution, or at least become a good foot soldier in it? This article—a primer for non-quants—is based on extensive interviews with executives, including some with whom I’ve worked as a teacher or a consultant.<br/>You, the Consumer<br/><br/>Start by thinking of yourself as a consumer of analytics. The producers are the quants whose analyses and models you’ll integrate with your business experience and intuition as you make decisions. Producers are, of course, good at gathering the available data and making predictions about the future. But most lack sufficient knowledge to identify hypotheses and relevant variables and to know when the ground beneath an organization is shifting. Your job as a data consumer—to generate hypotheses and determine whether results and recommendations make sense in a changing business environment—is therefore critically important. That means accepting a few key responsibilities. Some require only changes in attitude and perspective; others demand a bit of study.<br/><br/>Learn a little about analytics. If you remember the content of your college-level statistics course, you may be fine. If not, bone up on the basics of regression analysis, statistical inference, and experimental design. You need to understand the process for making analytical decisions, including when you should step in as a consumer, and you must recognize that every analytical model is built on assumptions that producers ought to explain and defend. (See the sidebar “Analytics-Based Decision Making—in Six Key Steps.”) As the famous statistician George Box noted, “All models are wrong, but some are useful.” In other words, models intentionally simplify our complex world.<br/>Analytics-Based Decision Making—in Six Key Steps<br/><br/>To become more data literate, enroll in an executive education program in statistics, take an online course, or learn from the quants in your organization by working closely with them on one or more projects.<br/><br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
MATHEMATICAL MODELS |
| 9 (RLIN) |
11190 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
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Book |
