analytics failure how to avoid it

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


What you were promised1. What causes most analytics failures


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


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


Part 1

What causes darned near all analytics failures


Analytics programs often flop.

David Castillo Dominici FreeDigitalPhotos.net

http://www.freedigitalphotos.net/


Many success stories hide a dirty secret.

David Castillo Dominici FreeDigitalPhotos.net



Why so many failures?


Does it takes a brain surgeon to do this stuff right?

No. FreeDigitalPhotos.net



It takes… a plan.

pakorn FreeDigitalPhotos.net



Analytics programs fail because they lack a viable plan for success.


End Part 1


Part 2

How to maximize your own chance of success


Don’t start without a plan.


End Part 2


Just kidding.


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.


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.


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


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!


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.


CRISP-DM has consistently been the most popular data mining process model throughout the past fifteen

years.


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


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.


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


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

http://ibm.co/1LcFIeT

http://ibm.co/1LcFIeT

http://www.socdm.org/


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

http://www.socdm.org/


End Part 2


Really.


Part 3

Characteristics and examples of best-bet analytics applications


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


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


Do I need…

Big Data?


On the contrary, you should use the smallest data you can reasonably manage.


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


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


How much computing power is needed for a trip to space?


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.


End Part 3


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!


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

http://www.booksellers.org.uk/bookshopsearch

http://bit.ly/1ruU9n0

http://bit.ly/1qFLkQG

http://bit.ly/1qFLAz8

http://bit.ly/1qFLAz8


Meta S. Brownhttp://www.metabrown.com

[email protected](312) 286-6735

@metabrown312

Connect with me on LinkedIn!

mailto:[email protected]

mailto:[email protected]


Questions?Images: FreeDigitalPhotos.net


analytics failure how to avoid it

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