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7 ideas on encouraging advanced analytics

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Mark Tabladillo
Mark Tabladillo

Many companies are starting or expanding their use of data mining and machine learning. This presentation covers seven practical ideas for encouraging advanced analytics in your organization.

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7 Ideas on Encouraging Advanced Analytics Mark Tabladillo Ph.D. Microsoft MVP, SAS Expert; Trainer & Consultant, SolidQ July 17, 2014
Abstract Many companies are starting or expanding their use of data mining and machine learning. This presentation covers seven practical ideas for encouraging advanced analytics in your organization.
MarkTab Microsoft MVP SAS Expert Trainer & Consultant Data Scientist Associate Faculty – University of Phoenix (School of Advanced Studies) @marktabnet Linked In http://marktab.net
Premise Advanced Analytics promises to handle the common challenges facing organizations How do we respond to: Volume, Velocity, Variety How do we achieve rapid analytics How do we develop technology How do we obtain more skilled analysts and data scientists How do we tell stories
Scientific Method Baseline = Null Hypothesis Alternative = Alternative Hypothesis Questions: Is there evidence to reject the null hypothesis? How do you know that? So what? Epistemology: Science relies on presuppositions
Seven Areas Advanced Analytics promises to handle the common challenges facing organizations 1-3 How do we respond to: Volume, Velocity, Variety 4 How do we achieve rapid analytics 5 How do we develop technology 6 How do we obtain more skilled analysts and data scientists 7 How do we tell stories
Volume Baseline: Ignore it Alternative: Technology (flat files, tape, CSV  Hadoop) Sampling
Velocity Baseline: Ignore it Alternative Streaming (StreamInsight) Sampling
Variety Baseline: Ignore it Alternative: Different database types SQL NoSQL: Excel, Power Pivot, OLAP, Graph, HDInsight, Hadoop Sampling
Achieving Rapid Analytics Baseline: IT (Information Technology) produces, business units consume Alternative: Business Units share production and consumption with Information Technology Approach: Learn the business, work on better data
Developing Technology Baseline: Let the vendors do it Alternative Build it Virtual Machines: Cloud, On Premise, Hybrid Development Environments D = Development R&D = Research and Development
Obtaining Talent Baseline: Ignore the issue Alternatives Buy Rent Create Lead
Stories Baseline 1: internal focus because we’re just like everyone else Baseline 2: the whole world is unique with no unifying patterns Alternative Technology conferences Industry conferences Benchmarking Eric Siegel: Predictive Analytics
One Book to Read Thomas Kuhn: The Structure of Scientific Revolutions
Tommy Lasorda, Manager LA Dodgers You can make it happen You can let it happen Or you can wonder, what happened?

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7 ideas on encouraging advanced analytics

  • 1. 7 Ideas on Encouraging Advanced Analytics Mark Tabladillo Ph.D. Microsoft MVP, SAS Expert; Trainer & Consultant, SolidQ July 17, 2014
  • 2. Abstract Many companies are starting or expanding their use of data mining and machine learning. This presentation covers seven practical ideas for encouraging advanced analytics in your organization.
  • 3. MarkTab Microsoft MVP SAS Expert Trainer & Consultant Data Scientist Associate Faculty – University of Phoenix (School of Advanced Studies) @marktabnet Linked In http://marktab.net
  • 4. Premise Advanced Analytics promises to handle the common challenges facing organizations How do we respond to: Volume, Velocity, Variety How do we achieve rapid analytics How do we develop technology How do we obtain more skilled analysts and data scientists How do we tell stories
  • 5. Scientific Method Baseline = Null Hypothesis Alternative = Alternative Hypothesis Questions: Is there evidence to reject the null hypothesis? How do you know that? So what? Epistemology: Science relies on presuppositions
  • 6. Seven Areas Advanced Analytics promises to handle the common challenges facing organizations 1-3 How do we respond to: Volume, Velocity, Variety 4 How do we achieve rapid analytics 5 How do we develop technology 6 How do we obtain more skilled analysts and data scientists 7 How do we tell stories
  • 7. Volume Baseline: Ignore it Alternative: Technology (flat files, tape, CSV  Hadoop) Sampling
  • 9. Variety Baseline: Ignore it Alternative: Different database types SQL NoSQL: Excel, Power Pivot, OLAP, Graph, HDInsight, Hadoop Sampling
  • 10. Achieving Rapid Analytics Baseline: IT (Information Technology) produces, business units consume Alternative: Business Units share production and consumption with Information Technology Approach: Learn the business, work on better data
  • 11. Developing Technology Baseline: Let the vendors do it Alternative Build it Virtual Machines: Cloud, On Premise, Hybrid Development Environments D = Development R&D = Research and Development
  • 12. Obtaining Talent Baseline: Ignore the issue Alternatives Buy Rent Create Lead
  • 13. Stories Baseline 1: internal focus because we’re just like everyone else Baseline 2: the whole world is unique with no unifying patterns Alternative Technology conferences Industry conferences Benchmarking Eric Siegel: Predictive Analytics
  • 14. One Book to Read Thomas Kuhn: The Structure of Scientific Revolutions
  • 15. Tommy Lasorda, Manager LA Dodgers You can make it happen You can let it happen Or you can wonder, what happened?