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Data-Driven Publishing (Ken Brooks at Publishers Launch Frankfurt 2013)

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Data-Driven Publishing: Using Big Data and smart analysis to make better decisions across the business -- Presented by Ken Brooks, Senior Vice President, Global Supply Chain Management, McGraw-Hill

At Publishers Launch Frankfurt, Frankfurt Book Fair, 8 October 2013

With more data from more internal and external sources available to publishers than ever before, and with ever-more powerful tools and service providers to crunch them, it is incumbent on C-level executives to build Big Data capabilities into their organizations. The possibilities, and the imperatives, will be the topic for Ken Brooks, who has held senior management positions at Bantam Doubleday Dell, Simon & Schuster, Barnes & Noble, and Cengage, and is both a master of data and experienced with all kinds of publishing.

Although there are service providers to do Big Data crunching, and any publisher might use them for some challenges, Brooks believes that learning to use available tools routinely will become a necessary skill set in most publishing houses. He says the key is to become more “data-driven” in analysis and decision-making, because data-driven decisions are possible in more ways than ever before and because publishing is particularly amenable to improvement through the skilled use of data.

Brooks also points out that routine Big Data analysis will become increasingly accurate and beneficial over time. He believes it is an emerging competitive tool of great importance and that the companies that get it soonest will gain great advantage. In this presentation, he will give publishers ideas about how to use Big Data across their enterprise: marketing, editorial, operations, and finance.

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Data-Driven Publishing (Ken Brooks at Publishers Launch Frankfurt 2013)

  1. 1. 1 Data Driven Publishing Publishers Launch, Frankfurt 2013 October 8, 2013 Ken Brooks ken.brooks@mheducation.com @kenbrooks
  2. 2. 2 The analytics / big data world.. Content Engagement Transactions Customer / User Relational databases & SQL JSON / APIs NoSQL databases Key-value Object Row-column Graph AWS, Azure, Google MapReduce / Hadoop Data visualization Statistical reasoning Algorithms Regression / Classification Parametric / non- parametric Problem types Outlier detection Recommendation Prediction Data Sources Data Management Analytics
  3. 3. 3 Start with key decisions and actions Business Unit / Function Key decisions / actions Strategic • How should I price? • What channels should be used? • What products should be offered? Editorial • Which titles to acquire? • What product features to offer? Sales • Which customers to prioritize? How? • What should an account buy? • Net or gross sales commission? • Territory allocation? • Discounts or other incentives Marketing • What promotions to run? • Where to place publicity / ads? • What loyalty programs to run? Operations • When and how much to print? • What vendors to use? • Number and location of facilities?
  4. 4. 4 Inventory planning 4 POD Offshore Domestic Forecast Demand Project Inventory Position Determine Print Quantities and Source
  5. 5. 5 So how do you do it? Business Decisions
  6. 6. 6 So how do you do it? Business Decisions Should I increase prices this year?
  7. 7. 7 So how do you do it? Business Decisions Should I increase prices this year? A price increase of 5% will lead to a 2% fall in revenue due to elasticity.
  8. 8. 8 So how do you do it? Business Knowledge Business Decisions
  9. 9. 9 So how do you do it? Business Knowledge Analytics Business Decisions
  10. 10. 10 So how do you do it? Business Knowledge Analytics Business Decisions Data visualization Statistical reasoning Algorithms Regression / Classification Parametric / non- parametric Problem types Outlier detection Recommendation Prediction
  11. 11. 11 So how do you do it? Business Knowledge Analytics Data Business Decisions
  12. 12. 12 So how do you do it? Business Knowledge Analytics Data Business Decisions Relational databases & SQL JSON / APIs NoSQL databases Key-value Object Row-column Graph AWS, Azure, Google MapReduce / Hadoop
  13. 13. 13 So how do you do it? Business Knowledge Analytics Data Business Decisions
  14. 14. 14 Data Driven Publishing Publishers Launch, Frankfurt 2013 October 8, 2013 Ken Brooks ken.brooks@mheducation.com @kenbrooks

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