How Data Science is Changing the Way Companies …download.101com.com/pub/tdwi/Files/071714 Teradata.pdfHow Data Science is Changing the Way Companies Do Business Bill Franks Chief

How Data Science is Changing the Way Companies Do Business

Colin White

BI Research

July 17, 2014

2

Sponsor

3

Speakers

Bill Franks

Chief Analytics Officer,

Teradata

Colin White

President,

BI Research

Colin White

President, BI Research

TDWI-Teradata Web Seminar

July 2014

How Data Science is Changing the Way

Companies Do Business

The Evolution of Business Intelligence

5 Copyright BI Research, 2014

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

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

6

Applies to these

types of analytics

as well

Fast Time to Business Value: Requirements

7

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


Copyright BI Research, 2014

Solution: Next Generation BI

8

New business

insights

Reduced

costs

New

technologies

Enhanced

data

management

Advanced

analytics

New

deployment

options

Next

generation

BI

DRIVERS

TECHNOLOGIES

Copyright BI Research, 2014

New Business Insights: Customer Marketing


Situational 1-to-1 Marketing – reach individual

customers with the right messages and offers

• Micro-segmentation

• Analyze all channels: web, stores, call centers,

purchases, buying patterns

• Analyze other information for influential factors:

geography, weather

Customer experience management – make all

experiences beneficial to customer/business

Customer perception management – analyze

trends in social channels and respond

appropriately

In all cases analysts need to be able to move

from analyzing past events to predicting future

outcomes

New Business Insights: Fraud Detection


New Business Insights: The Internet of Things


Further reading: GE Document - Industrial Internet: Pushing the Boundaries of Minds and Machines

http://www.ge.com/docs/chapters/Industrial_Internet.pdf

http://www.ge.com/docs/chapters/Industrial_Internet.pdf

New Technologies: eXtended Data Warehouse

Copyright BI Research, 2014 12

Traditional EDW environment

Investigative computing platform

Data refinery

Data integration platform

Operational real-time environment

RT analysis platform

Other internal & external structured & multi-structured data

Real-time streaming data

Analytic tools & applications

Operational systems

RT BI services

Two Key New XDW Components


Data refinery

Investigative computing platform

Analytic tools & applications

Investigative Computing Platform

o Used for exploring data and

developing new analyses and

analytic models

o Output used by an enterprise DW,

real-time analysis engine, or stand-

alone LOB application

Data Refinery

o Ingests raw detailed data in batch

and/or real-time into a managed

data store

o Distills the data into useful

information and distributes results

to other systems

Other internal & external data,

RT streaming data

EDW data

Operational data

EDW data & analyses

models & rules

applications

The Evolution of Open Source Software

R (commercial version available)

RapidMiner

(commercial version available)

KNIME

(commercial version available)

Apache Mahout (algorithm library)

Weka (algorithm library)

Issue: How easy is it to use these

products to bridge the gap

between BI and data science?


What is Data Science?

One person or a team of specialists?

Physical or virtual team?

Where in the organization does it report,

e.g., central IT, corporate executive, or

business unit management?

Part of, or separate from, a BI center of

expertise or data governance group?

Actual skills required?

Which skills are the most difficult to

learn or obtain?

Education, recruiting or outsourcing for

filling skill gaps?

Traditional BI/DW versus millennial

employee skills, experience and politics

Business expertise

Modeling & analysis

skills

Data engineering

skills


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

Business Analyst Data Scientist

The Role of Investigative Computing

Enables data scientists and analysts to blend new types

of data with existing information to discover ways of

improving business processes

Allows data scientists and analysts to experiment with

different types of data and analytics before committing

to a particular solution

May employ an analytic sandbox, analytic platform or a data refinery

Results may include data schemas, analyses, analytic models, business

rules, decision workflows, dashboards, LOB applications, etc.

Represents a shift in the way organizations build analytic solutions:

o Increases flexibility and provides faster time to value because data does not

have to be modeled or integrated into an EDW before it can be analyzed

o Extends traditional business decision making with solutions that increase the

use and business value of analytics throughout the enterprise


Example: Teradata Aster Behavior Path Analysis


Example: Alteryx + Tableau


Example: Teradata – Identify/Retain “At Risk” Users


Hadoop captures,

stores and transforms

social, images, and call records

Aster does web

sessionization, path and basic

sentiment analysis

with multi-structured

data

Data Sources

Multi-Structured Raw

Data

Call Center Voice Records

Traditional Data Flow

Analysis + Marketing

Automation

(Customer Retention Campaign)

Capture, Retain and

Refine Layer

ETL Tools

Hadoop

Call Data

Teradata

Integrated DW

Dim

en

sio

na

l D

ata

An

aly

tic R

esu

lts

Aster Discovery Platform Raw

Sentiment

Data

SOCIAL FEEDS

POS

Web Sale

Cust & Item

Master

Mobile Sale

Surveys and Customer Feedback

WEB AND MOBILE

CLICKSTREAM

Customer Feedback

Aster pre-built operators:

sessionization, n-path, many to

many basket and affinity,

collaborative filtering for

recommendations

Source: Teradata

Gaining Business Value from Next Generation BI

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


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


Note: Several of the ideas presented on these last two slides were

summarized from information in the book “Data Science for Business”

How Data Science is Changing the Way Companies Do Business

Bill Franks Chief Analytics Officer, Teradata TDWI-Teradata Web Seminar July 2014

24 Proprietary and Confidential to Teradata and Bill Franks. Do not distribute without permission.

Leverage Analytics In Diverse Ways

Perform discovery analysis alongside confirmatory analysis to maximize benefits

Discovery Analysis

Full scope not defined

Interactively evolving hypotheses

Business problem is developing

Aim is to identify new theories

Confirmatory Analysis

Examining predefined problems

Assessing specific hypotheses

Business problem well defined

Aim is to validate a theory


Utilize New Analytic Disciplines

Statistics Forecasting

Augment traditional analytic approaches with new

approaches


Utilize New Analytic Disciplines

Statistics

Graph Analysis

Text Analysis

Geospatial

Forecasting

Augment traditional analytic approaches with new

approaches

27 Proprietary and Confidential to Teradata. Do not distribute without permission.

Teradata Aster SNAP™ Framework


Your Team Will Need To Expand & Evolve

• No single individual will likely know every analytic discipline

• Build out a team that has what you need in total

Person 1 Person 2

+ =

Total Package!


Do You Need A Chief Analytics Officer?

• What is a Chief Analytics Officer & why do you need one?

Hire Me!


Do You Need A Chief Analytics Officer?

• What is a Chief Analytics Officer & why do you need one?

• What about a Chief Data Officer?

Hire Me!


Math

and Stats

Data

Mining

Business

Intelligence

Applications

Languages

Marketing

ANALYTIC TOOLS & APPS

USERS

DISCOVERY PLATFORM

INTEGRATED DATA WAREHOUSE

ERP

SCM

CRM

Images

Audio

and Video

Machine

Logs

Text

Web and

Social

SOURCES

DATAPLATFORM

UNIFIED DATA ARCHITECTURESystem Conceptual View

Marketing

Executives

Operational

Systems

Frontline

Workers

Customers

Partners

Engineers

Data

Scientists

Business

Analysts

TERADATA DATABASE

TERADATA ASTER DATABASE

TERADATA DATABASE

HORTONWORKS

Your environment must enable any analysis against any type or volume of data at any time…

Teradata Unified Data Architecture (UDA)

LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS

Engineers

Data Scientists Business Analysts Marketing Front-Line Workers

Operational Systems Customers / Partners Executives


Spread Your Bets With The Teradata UDA!

• Who knows what the future holds?

• Don’t place all your chips on an architecture that assumes specific outcomes

• Hedge your bets with an architecture that can adapt to whatever the future holds!


Question & Answer

• Thank you!

34

Questions??

35

Contact Information

If you have further questions or comments: Colin White, BI Research [email protected]

Bill Franks, Teradata

[email protected]

Documents

How Data Science is Changing the Way Companies …download.101com.com/pub/tdwi/Files/071714 Teradata.pdfHow Data Science is Changing the Way Companies Do Business Bill Franks Chief