Translating advanced analytics for business users11 Nov 20152015 H2O WorldConor JensenZurich North America

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1. Identify the analytics consumer

2. Hire the right talent

3. Develop the analytics collaboratively

4. Tell a story with your data

Developing analytics for business users

© 2014 Zurich American Insurance Company. All rights reserved.

Who will be the end user?

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Analytics for machines: computers execute on the outputs of the analytics

Analytics for humans: humans – often non-data scientists – execute on the outputs

Source: HBR, The Question to Ask Before Hiring a Data Scientist, Michael Li, August 6, 2014

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Finding the right analytics talent is key

“Traditional” view of the Data Scientist continuum:

Researcher/Mathematician

Applied scientist/Statistician

Data Engineer/Data Analyst

Computer Scientist

Data Scienti

st

This is an extremely important and helpful frame of reference…

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Talent must be tailored to your organization

• Understand what kind of talent your organization needs and where your gaps are

• Don’t focus on hiring the unicorn, hire people with experience developing models for humans

• The most important language any analyst should know is that of your business

However this omits the most important dimension

Computer Science

Stat

isti

cs

Busin

ess

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An amazing analytics team isn’t enough

Which of these sound like your organization?

“In God we trust, all others bring data”-The Elements of Statistical Learning

“There are lies, damn lies, and statistics”-The Autobiography of Mark Twain

Or

A good analytics team is a necessary, but not sufficient condition for creating a data driven organization

© 2015 Zurich American Insurance Company. All rights reserved.

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Collaborate throughout the process

• Determine what problems need to be solved

• Define the problem you’re trying to solve

• Create multiple solutions and discuss results with end users

• Iterate frequently with the end users to make sure the solution satisfies the problem

• Create a robust feedback loop for models in production

Data Preparation

Insight Generation

Solution Design

Prioritization

Develop Analytics

Plan

Solution Design

Implemen-tation

Lifecycle Managemen

t

Prioritization

Illustrative analytics process

Examples where you should be collaborating:

© 2015 Zurich American Insurance Company. All rights reserved.

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Be mindful of trade offs during development

Accuracy

Stability (fitting) Speed

Clarity

While these aren’t necessarily mutually exclusive goals, you need to prioritize which are most important for any given project

There are many potentially competing priorities

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Data science isn't about cool graphics and visualizations; it's about telling �� ��a story

There’s more than one way to tell a story

The story should be tailored for the audience

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1. You don’t have to explain to the user how the model works… …however you must explain how users can best use the model

2. Iterate frequently with selected end users during the model development, and actively solicit feedback after models are deployed

3. Tailor the message and the visualization to the audience

4. Explicitly acknowledge outliers and explain how users should evaluate them so they don’t erode confidence in the model

5. Be explicit about where the data behind the model is thin and the users need to apply more judgment

6. Though very important, use visualizations only when they support the story effectively

Tips for effective storytelling

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Visualizations should be intuitive

“Graphical elegance is often found in simplicity of design and complexity of data”

The Visual Display of Quantitative Information, Edward Tufte

$2-5B

$750Mto 1B

$1-2B

240

49

81

55

55

W&RT

261

39

75

74

73

FI

322

73

104

71

74

T&A

148

26

38

34

50

T&C

205

36

44

68

13

39

33

4057

MFG

$5B+

Other

510

123

167

139

81

F&A

125

Cust

omer

rev

enue

ba

nd

Industry group

Bad examples

© 2015 Zurich American Insurance Company. All rights reserved.

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Three steps to effectively develop advanced analytics for business users once you’ve identified the right audience:1. Hire and develop analytics professionals with business knowledge2. Build your analytics in collaboration with the end users3. Present the data in a way that users can consume

The role of the business user is to interpret and apply the model results; so they must know where to trust the model, and where to rely on their own judgment

Always remember, the models are just models and it is our responsibility to educate the users

Finally, predictive analytics that are inputs into a decision making process must be developed for, with, and ultimately understood by the people making the decision

Parting thoughts

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Discussion

Q & A