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Designing Great Data Products
 
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Designing Great Data Products [Format Kindle]

Jeremy Howard , Margit Zwemer , Mike Loukides

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Descriptions du produit

Présentation de l'éditeur

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives.

We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.


Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 490 KB
  • Nombre de pages de l'édition imprimée : 28 pages
  • Utilisation simultanée de l'appareil : Illimité
  • Editeur : O'Reilly Media; Édition : 1 (23 mars 2012)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B007OW0SW2
  • Synthèse vocale : Activée
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Amazon.com: 3.5 étoiles sur 5  2 commentaires
2 internautes sur 2 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Brief introduction to using data for actionable outcomes 16 juillet 2012
Par Erik Gfesser - Publié sur Amazon.com
Format:Format Kindle
This succint offering from the recently published O'Reilly Strata series of texts provides a departure from the first two Big Data focused series entries that I read, entitled "Big Data Now: Current Perspectives from O'Reilly Radar" and "Planning for Big Data: A CIO's Handbook to the Changing Data Landscape", because this more generalized text looks at the design of data products. The discussion that the authors provide here revolves around what is deemed the "Drivetrain Approach" process that transformed the insurance industry, and the authors walk readers through how this process can be applied effectively in other industries. "We are entering the era of data as drivetrain, where we use data not just to generate more data (in the form of predictions), but use data to produce actionable outcomes."

"For an insurance company, policy price is the product, so an optimal pricing model is to them what the assembly line is to automobile manufacturing. Insurers have centuries of experience in prediction, but as recently as 10 years ago, the insurance companies often failed to make optimal business decisions about what price to charge each new customer. Their actuaries could build models to predict a customer's likelihood of being in an accident and the expected value of claims. But those models did not solve the pricing problem, so the insurance companies would set a price based on a combination of guesswork and market studies." A company called Optimal Decisions Group (ODG) approached this problem with a practical take on step 4 that can be applied to a wide range of problems.

The four steps of the Drivetrain Approach can be summarized as follows: (1) specify the goal, (2) specify the system inputs that can be controlled, (3) determine what new data is needed to reach the goal, and (4) create predictive models following the first three steps. After discussing applications to search engines and the insurance space, the authors discuss application to recommendation engines, followed by high level introductions to related topics optimizing lifetime customer value and best practices from physical data products. While potential readers of this brief white paper sized book should not expect to become experts at designing great data products as a result of reading what the authors have to share here, many new to this space are likely to find value as they begin their journey to systematically use data more effectively.
3.0 étoiles sur 5 Good ideas, but a bit short on detail 11 décembre 2012
Par Mac - Publié sur Amazon.com
Format:Format Kindle|Achat vérifié
This book had some really great ideas, and the authors did a good job of explaining them. I only wish they had a bit more 'substance' to the book. Perhaps more fleshed out examples?
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