Are Advanced Analytics Possible Without a Data Scientist?download.101com.com › pub › tdwi › Files › Tableau061914.pdf · “Data science innovation was driven initially by

Are Advanced Analytics Possible Without a Data Scientist?

Colin White

BI Research

June 19, 2014

Sponsor

3

Speakers

Colin White

President,

BI Research

Suzanne Hoffman

Senior Director of Analyst Relations,

Tableau

Copyright © BI Research, 2014

Colin White

President, BI Research

TDWI-Tableau Web Seminar

June 2014

Are Advanced Analytics Possible

Without a Data Scientist?


Harvard Business Review – October 2012

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Slashdot – March 2014

6

“After the 'Big Data' buzz cools a bit, he argues,

it will be clear to everyone that 'Data Science' is dead….”


Data Science: Another Perspective

Data Science has evolved from Data Mining

“Data science innovation was driven initially by new types of data – this

opened up possibilities for new questions to be asked and new analyses

to be run

This innovation led to enterprise technologies that can process data and

build models at scale

The required skills to work in data science are different than those for

data mining

o Strong technical skills – statistics, coding, databases – are still required

o But, there is now the need to problem solve in a way that is more

meaningful to the business since data scientists are now involved in

business strategy

o This requires the ability for data scientists to communicate their findings in

a non-technical manner to business users”

Source: interview with Michael Gold and Ryan McClarren of the Farsite Group

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Data Science vs Data Mining & Analytic Modeling

CRISP-DM Methodology

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Traditional Perception: Data Science vs BI

Type of analysis

Prescriptive What action should be taken?

Predictive What could happen?

Descriptive What is happening now?

What has happened?

Diagnostic Why did it happen?

Real-time dashboards

PDF reports via e-Mail

Behavioral analysis

Interactive BI dashboards

Predictive models

Forecasts

Rules-driven actions

Optimization

Business value

Examples of deliverables

Business question

BI

Data science

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It’s Really About More “Advanced” Analytics

Type of analysis



Descriptive What is happening now?

What has happened?

Diagnostic Why did it happen?

Real-time dashboards

PDF reports via e-Mail

Behavioral analysis

Interactive BI dashboards

Predictive models

Forecasts


Optimization

Business value


Business question

BI

Data science

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Applies to these

types of analytics

as well


Example: The Traditional Customer Life Cycle

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Customer Analytics: BI Perspective

Descriptive: How many of customers churned in the last month?

Descriptive: How many of these were profitable?

Diagnostic: Why did these profitable customers churn?

Predictive: How many profitable customers are likely to churn next

month?

Prescriptive: How can we reduce this profitable customer churn rate?

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Customer Analytics: Data Science Perspective

Business and data understanding: What is the difference between a

highly profitable customer and an average customer?

Data preparation and modeling: What are the characteristics of a

highly profitable customer?

Deployed predictive model: Will this new customer be profitable?

How much revenue is this new customer likely to generate?

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Next Generation BI = Traditional BI + Data Science

Descriptive BI

Diagnostic BI

Predictive BI

Prescriptive BI

Business requirements

Modeling

Data preparation

Model deployment

Business & data

understanding

Data warehouse

Raw data

Selected hypotheses

Improved understanding

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Next Generation BI Architecture

Traditional EDW environment

Investigative computing platform

Data refinery

Data integration platform

Analytic tools & applications

Operational real-time environment

RT analysis engine

Other internal & external structured & multi-structured data

Real-time streaming data

Operational systems

BI services

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Fast Time to Business Value: Requirements

Type of analysis



Usable by business analysts, not just data scientists –

easier to use analysis and visualization tools

Seamless extension to diagnostic and descriptive BI

Iterative development, easy to deploy and maintain, and

(where required) near real-time results

Business question

Predictive models

Forecasts


Optimization


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Vendor Approaches

Enhancements and extensions to traditional BI and data/analytic

modeling tools

New BI and data/analytic modeling tools

Analytic RDBMS platforms

Non-Relational platforms such as Hadoop

Real-time analysis platforms

Packaged (domain specific) analytic solutions

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Tableau Example: High School Project

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Source: Tableau NZ


Example: Tableau + R and Tableau + Alteryx

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Source: jenunderwood.com

Source: Alteryx


Visualization is Key: Visual Storyboards

Source: Frederico Freitas, Spatial History Project

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Visualization is Key: Visual Storyboards

Source: Frederico Freitas, Spatial History Project

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Source: Robert Kosara, Research Scientist


Tableau Story Points

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Gaining Business Value from Advanced Analytics

Managers don’t have to be data scientists, but they need to:

• Understand the fundamental principles well

enough to appreciate the business

opportunities, communicate with technologists

and evaluate proposals for data science projects

• Be willing to invest in data and experimentation

and supply the required resources

• Keep the BI and data science team on track

Understand how to gain competitive advantage (or parity) from data

science in the context of the corporate strategy and that of

competitors

Maintain momentum over competitors

Collaborate with, and examine data science projects in other

organizations

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Final Thoughts

Organizations need to build a high quality data

science team that is managed by a

knowledgeable person such as a chief analytics

officer

Keep humans in the decision making loop

Mining and analyzing personal data raises

important ethical and privacy issues that should

not be ignored

Applying BI analytics to a well-structured problem

versus exploratory data mining requires different

skills and tools, but these two approaches need

to be able to work together

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Thanks for Listening

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All rights reserved. © 2008 Tableau Software Inc.

Suzanne Hoffman

Senior Director

June 19, 2014

Breakthrough Innovation

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Analytics & statistics for everyone • Make calculations simple, direct,

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analytics toolkit” • Make advanced analytics

accessible and integrated into VizQL

• Enable people to leverage the work of others

• “Help Me”

Our products need to not only be easy to use and simple but also useful and analytically deep…

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Storytelling & presentation • Storytelling affordances • Expressiveness • Annotations • Formatting

And natively available for a MAC!

A Fundamental Break from the Past

Seattle – September 8-12

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Questions?

37

Contact Information

If you have further questions or comments:

Colin White, BI Research

[email protected]

Suzanne Hoffman, Tableau

[email protected]

Documents

Are Advanced Analytics Possible Without a Data Scientist?download.101com.com › pub › tdwi › Files › Tableau061914.pdf · “Data science innovation was driven initially by