Presentation © 2012-2015 Meta S. Brown
Analytics Failure:How to Avoid It
Meta S. BrownAuthor, Data Mining for Dummies
Data Science Institute, Imperial College LondonJune 24, 2015
Presentation © 2012-2015 Meta S. Brown
What you were promised1. What causes most analytics failures
Presentation © 2012-2015 Meta S. Brown
What you were promised1. What causes darned near all analytics failures2. How to maximize your own chance of success3. Characteristics and examples of best-bet
analytics applications
Presentation © 2012-2015 Meta S. Brown
Who am I to tell you this?• Author, Data Mining for Dummies• Creator of Storytelling for Data Analysts
and Storytelling for Tech workshops• Hands-on data miner and statistician
Presentation © 2012-2015 Meta S. Brown
Part 1
What causes darned near all analytics failures
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Analytics programs often flop.
David Castillo Dominici FreeDigitalPhotos.net
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Many success stories hide a dirty secret.
David Castillo Dominici FreeDigitalPhotos.net
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Why so many failures?
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Does it takes a brain surgeon to do this stuff right?
No. FreeDigitalPhotos.net
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It takes… a plan.
pakorn FreeDigitalPhotos.net
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Analytics programs fail because they lack a viable plan for success.
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End Part 1
Presentation © 2012-2015 Meta S. Brown
Part 2
How to maximize your own chance of success
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Don’t start without a plan.
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End Part 2
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Just kidding.
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You need a problem• First, you need a problem to solve• If you don’t have a problem, what value is a solution?• Start with a small, well-defined business problem. Set a modest goal.
Presentation © 2012-2015 Meta S. Brown
Work backwards• Now that you have a goal, think backwards. • What do you need to reach the goal?• Think small! What’s the minimum that you need to reach the goal?• Data• Time• Tools and support
• Make the most of resources that you already have.
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Earn credibility• Choose low-risk projects first.• Produce value where it’s not expected.• Use your successes to build trust. • Greater trust will enable you to: • Take on bigger projects• Fail now and then without backlash• Command more resources
Presentation © 2012-2015 Meta S. Brown
Process• Bigger projects, and more projects, mean it’s time to establish serious
work processes.• Process and documentation build value, enhance credibility and
preserve your work.• Define processes and use them consistently.• You don’t get rewards for originality in work process. Just use what works.• Use your company’s established process, copy from another organization, or
create your own.• Be consistent in how you work, and document, document, document!
Presentation © 2012-2015 Meta S. Brown
No need to roll your own• No matter how you identify (data analyst, statistician, data miner,
data scientist, psychometrician, economist…), you can use an existing process, or adapt one to your needs.• Analytics work processes are always iterative, but Agile and similar
methods are a poor fit.• CRISP-DM, created and widely used in the data mining community,
can be used for any type of analytics.
Presentation © 2012-2015 Meta S. Brown
CRISP-DM has consistently been the most popular data mining process model throughout the past fifteen
years.
Presentation © 2012-2015 Meta S. Brown
Presentation © 2012-2015 Meta S. Brown
Business understandingGet a clear understanding of the problem you’re out to solve, how it impacts your organization, and your goals for addressing it
Four tasks:• Identify your business goals• Assess your situation• Define your data mining goals• Produce your project plan
Presentation © 2012-2015 Meta S. Brown
Deliverables: Identify your business goals• Background: Explain the business situation that drives the project• Business goals: Define what your organization intends to accomplish
with the project. This is usually a broader goal than you, as a data miner, can accomplish independently. For example, the business goal might be to increase sales from a holiday ad campaign by 10% year over year.• Business success criteria: Define how the results will be measured. Try
to get clearly-defined quantitative success criteria. If you must use subjective criteria (hint: words like gain insight or get a handle on imply subjective criteria), at least get agreement on who, exactly, will decide of those criteria have been met.
Presentation © 2012-2015 Meta S. Brown
Deliverables: Business understanding phase• Task: Identify your business goals (3 reports)• Task: Assess your situation (5 in-depth reports)• Task: Define your data mining goals (2 reports)• Task: Produce your project plan (2 reports)
12 reports in this phase, and this is just to understand the problem and define goals
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Learn more about CRISP-DM• CRISP-DM 1.0 Step-by-step data mining guide
http://ibm.co/1LcFIeT75 pages, small type.
• Data Mining for DummiesAsk your library to get it! ISBN: 978-1-118-89317-3CRISP-DM section: 25 pages, big type. Plus 383 pages of other good stuff.
• Society of Data Minerssocdm.org
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The Society of Data Miners
Our Mission
Increase the benefits of data mining to society
by raising awareness and understanding of the nature and benefits of analytics
and forming analytics practitioners into a true profession.
www.socdm.org
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End Part 2
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Really.
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Part 3
Characteristics and examples of best-bet analytics applications
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Predictive analytics works best when…• Appropriate data• Relevant• High-quality• Sufficient quantity
• Many opportunities to predict• Benefits for correct predictions, no big loss for incorrect
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Examples• Buy or don’t buy• Close account or not• Share post or not• Spend how much?• Match document to query• Route inquiry to proper department
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Do I need…
Big Data?
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On the contrary, you should use the smallest data you can reasonably manage.
Presentation © 2012-2015 Meta S. Brown
How big is it?• Seminal article by Doug Laney describes a data problem• A lot of it• More collecting fast• Diverse forms
• Big Data is not necessarily relevant or of good quality• Resources invested in data management are not available for analysis,
secondary research, or action
Presentation © 2012-2015 Meta S. Brown
Let’s think BIG
Photo © 2010 Meta S. Brown
Presentation © 2012-2015 Meta S. Brown
Data needs for astronauts• Astronauts’ physical condition and medical information
• Geodesy (spacecraft location) and gravitational fields
• Meteorology – cloud cover and radiation balance
• Atmospheric physics
• Air density from drag and non-gravitation forces
• Ionospheric physics
• Magnetic fields
• Cosmic rays and trapped radiation
• Electromagnetic radiation (UV, X-ray and gamma)
• Interplanetary medium
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How much computing power is needed for a trip to space?
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What’s good about Big?• Big Data that provides actionable individual detail can be valuable.• Imagine that you could observe individuals one by one. You’d know
more about their habits, likes and wants.• Use that individual detail to provide individualized services, offers and
so on.• Don’t use it all for exploration or modeling! Use only as much as you
really need.
Presentation © 2012-2015 Meta S. Brown
End Part 3
Presentation © 2012-2015 Meta S. Brown
In summary• The primary cause of analytics failure is the lack of any plan for success. You
have the power to sidestep that problem.• You can maximize your chance of analytics success by making a plan. Begins
with a well-defined business problem and goal to address it, then work backward to outline your plan.• Define a work process, and use it consistently. Document the details!• Reap the benefits: • wine• women/men• song• maybe even money!
Presentation © 2012-2015 Meta S. Brown
Data Mining for Dummies• Ask your local library to get it. ISBN: 978-1-118-89317-3• Buy - UK• Your favorite independent bookseller (find one on Indiebound
http://www.booksellers.org.uk/bookshopsearch)• Amazon http://amzn.to/1C5Q1ft
• Buy - US• Your favorite independent bookseller (find one on Indiebound
http://bit.ly/1ruU9n0)• Powell’s City of Books http://bit.ly/1qFLkQG• Barnes and Noble http://bit.ly/1qFLAz8
Presentation © 2012-2015 Meta S. Brown
Meta S. Brownhttp://www.metabrown.com
[email protected](312) 286-6735
@metabrown312
Connect with me on LinkedIn!
Presentation © 2012-2015 Meta S. Brown
Questions?Images: FreeDigitalPhotos.net
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