C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

ANALYTICS IN ACTION

PAUL BACHTEAL

SENIOR DIRECTOR, GLOBAL TECHNOLOGY PRACTICE

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CAN YOU LEVERAGE OPEN SOURCE ANALYTICS?

CAN YOU SCALE YOUR DATA AND YOUR ANALYTICS?

DO YOU GROW A CULTURE OF INNOVATION?

CAN YOU ANALYZE ALL OF YOUR DATA?

CAN YOU MODERNIZE YOUR LEGACY BI

STRATEGY?

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WHAT’S TRENDING? INTERNET OF THINGS

40 terabytes/hour

1 gigabyte/second

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WHAT’S TRENDING? THE CLOUD

“Public IT cloud services spending will reach

$56.6 billion in 2014 and grow to more than

$127 billion in 2018, . . .This represents a five-

year compound annual growth rate (CAGR) of

22.8%, which is about six times the rate of

growth for the overall IT market.” November 2014

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WHAT’S TRENDING? CROWD SOURCING

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WHAT’S TRENDING? MEET THE MILLENNIALS

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Disruptive Technology

Unrivaled Processing Power

New Problem-solving Mindset

Infinite Volume and Variety of Data

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ANALYTICS IN

ACTION IT ALL STARTS WITH THE DATA…

No amount,

complexity

or pace

is insurmountable

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ANALYTICS IN

ACTION WHAT WE DO WITH DATA: DISCOVERY

Raw material

Creativity

Prototyping

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ANALYTICS IN

ACTION HOW WE OPERATIONALIZE THE RESULTS: DEPLOYMENT

Finished product

Governance

Enterprise-ready

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DISCOVERY DEPLOYMENT

Data is a Raw Material Information is a finished product

Flexible, ad hoc Established, documented process

Prototyping Governance (over data, process, technology)

Data Scientists, Analysts, Smart Creatives Engineers, DBA, IT

Open Source, “whatever works” Approved architecture

Departmental, personal Enterprise Ready

Innovative, Experimental, Groundbreaking Productionized, Scalable, Repeatable

DATA

Readily available, easy to access Detailed, not summarized or sampled

Structured & unstructured Prepared for Analytics

ANALYTICS IN

ACTION ART AND PROCESS WORKING TOGETHER

The “ Art ” The “ Process ”

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ANALYTICS IN

ACTION OBJECTIVES

Enable and Empower the New Analytics Culture

BRIDGE the gaps between Discovery, Deployment and Data

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THE NEW BI

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

THE NEW BI A BIT OF HISTORY… 20+ YEARS OF BI

DATA SOURCES

operational, transactional

INFORMATION ORGANIZED

summaries, samples, subsets

INTELLIGENCE ADDED

calculations, hierarchies, groups

ANALYTICS PERFORMED

descriptive, predictive, prescriptive

REPORTING

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

THE NEW BI ANALYTICS IN ACTION

DATA SOURCES

operational, transactional

ORGANIZE INFORMATION

ADD INTELLIGENCE

PERFORM ANALYTICS

DESIGN & DEPLOY REPORT

Access to ALL the data

Democratized Analytics

Self-Service

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THE NEW BI ACCESSING ALL THE DATA –

IMPACT ON BUSINESS USERS

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THE NEW BI ANALYTICS MATURITY ACCORDING TO GARTNER

COMPLETENESS OF VISION

AB

ILIT

Y T

O E

XE

CU

TE

2012

Business Intelligence Platforms

COMPLETENESS OF VISION

AB

ILIT

Y T

O E

XE

CU

TE

2013

Business Intelligence and Analytics Platforms

COMPLETENESS OF VISION

AB

ILIT

Y T

O E

XE

CU

TE

2014

Advanced Analytics Platforms

2015: The “Citizen Data Scientist” is named

ADDRESSING THE SCARCITY OF ANALYTICAL SKILLS

Business Analyst Data Scientist Citizen Data

Scientist

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THE NEW BI DEMOCRATIZATION OF ANALYTICS

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THE NEW BI A BEST PRACTICE:

THE 60-DAY CHALLENGE

Do not replace your legacy BI (yet)

Introduce ‘the new BI’

Enable access to all relevant data

Identify a high-value challenge

Meet twice monthly to share

Legacy BI The New BI

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ALIGNING

BUSINESS AND IT

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Ongoing

Customer

Dialogue Business Analytics Modernization Assessment

Overview:

Two and a half day on-site discovery workshop

• Line of business and IT representatives (8 hrs. over 2.5 days)

Process:

• Review current business requirements, timeframes, critical success factors, and key business metrics.

• Review data sources to support business priorities.

• Review analytical priorities, strategy, process, and gaps.

Deliverables:

• Roadmap to optimize IT-enabled analytical process and projected business benefit

Track record:

• Approximately 90 workshops completed in all industries.

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Implementing Analytics in Action

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

The ‘Analytics’ folks

All data, de-

normalized,

agile insight

and never stop

adjusting

Storage efficiency,

governance,

repeatability &

delivered projects

The ‘IT’ folks

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Teamwork Well governed data and IT

architecture drives more

agile insight across the

analytics lifecycle!

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

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BI for the people!

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Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

TRADITIONAL

ANALYTICS

STRATEGY

WHAT’S WRONG WITH THE STATUS QUO?

DATA CENTER

CONNECTED Sensor

Readings

Edge of

Network

Operations Apps

Operations Data Store

Enterprise Data Warehouse

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A NEW

DEPLOYMENT

STRATEGY

ANALYTICS AT THE EDGE

Centralized Data Storage

Centralized Analytics Platform

Exploratory Exercise

Application Development

Databases

CONNECTED Sensor

Readings

Edge of

Network

Operations Apps

Operations Data Store

Enterprise Data Warehouse

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

STREAMING

ANALYTICS MARKET POTENTIAL

Potential economic

impact of IoT in 2025

$11 Trillion

11% of world

economy

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Edge

Analytics

In-Motion

Analytics

At-Rest

Analytics

Network Systems, Surveillance

Monitor equipment on the

platform for failures and safety

issues, and take action.

Identify fraudulent

transactions and be

alerted in real-time.

Intelligently integrate customer

information with real-time

streaming data

Strategic Data Integration Transactions, Logs, Clickstreams

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

6. “Open source analytics are valuable to us, don’t discount them, work to make them better!”

“P.S. The Millennials in my team love them, but they freak me out a bit!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SAS and Open Source Analytics

Generally cleaned, ‘ready’ data

Purpose is to create a

prototype, prove a

theory, or answer a

single question

Fewer models and

modelers collaborating

Medium size, single file

Data Transformations needed

Models run often and

deployed in a

production

environment

Teams of developers,

large number of models

Big data, 1000’s of files

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

WORKFLOW MANAGEMENT (METADATA)

• Open Source Analytics can leverage SAS enterprise capabilities

• Data Access and Preparation

• Data Dictionary

• Lineage

• Resource Management

• Deployment

• Model Assessment

DATA MANAGEMENT MODEL DEVELOPMENT MODEL DEPLOYMENT

SAS and Open Source Analytics

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

6. “Open source analytics are valuable to us, don’t discount them, work to make them better!”

“P.S. The Millennials in my team love them, but they freak me out a bit!”

5. “Make it easy for us to prove the value of emerging architectures and capabilities (especially Hadoop)!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

THE ANALYTICS FAST

TRACK™ FOR SAS®

High-Performance Platform Intel® Xeon® processor E7 v3 servers – 72 cores

3 TB of RAM

Intel® SSD disks – 25 TB of storage

Multiple Virtual Machines with: Linux servers with:

SAS Software to support business use cases

Hadoop distribution

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ALSO, IN THE CLOUD THE ANALYTICS FAST

TRACK™ FOR SAS®

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

6. “Open source analytics are valuable to us, don’t discount them, work to make them better!”

“P.S. The Millennials in my team love them, but they freak me out a bit!”

5. “Make it easy for us to prove the value of emerging architectures and capabilities (especially Hadoop)!”

4. “A Hadoop Big Data Lake is dangerous without good data!”

3. “Hadoop, as cheap storage, is great, but analytics are the key to unlocking value!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

WHY HADOOP?

Hadoop will soon become a replacement complement to:

Business Intelligence

Data Warehousing

Data Integration

Analytics

SOURCE: 10 Myths About Hadoop - TDWI Best Practices Report

HADOOP IN PRODUCTION:

#1 reason to go for Hadoop: Analytics (71%)

Challenges to Hadoop adoption:

Hadoop has no analytic functions built in

Cost: hefty payroll due to intensive hand coding

YES

< 12

MONTHS

< 24

MONTHS

< 36

MONTHS

3+

YEARS

NEVER

10%

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IMPLEMENTING

ANALYTICS IN

ACTION

How can we make Hadoop more valuable?

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

IMPLEMENTING

ANALYTICS IN

ACTION

How can we make Hadoop more valuable?

Controller

Client

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

6. “Open source analytics are valuable to us, don’t discount them, work to make them better!”

“P.S. The Millennials in my team love them, but they freak me out a bit!”

5. “Make it easy for us to prove the value of emerging architectures and capabilities (especially Hadoop)!”

4. “A Hadoop Big Data Lake is dangerous without good data!”

3. “Hadoop, as cheap storage, is great, but analytics are the key to unlocking value!”

2. “People and Process are our biggest concern, as Analytics move to the front stage!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

THE REALITY OF

MANAGING BIG

DATA

THE SITUATION TODAY

BUSINESS

PROBLEM

BUSINESS

DECISION

20% 80%

Preparing

to

solve the problem

Solving

the

problem

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

FLIPPING THE

SCRIPT HOW CAN YOU CHANGE THE EQUATION?

BUSINESS

PROBLEM

BUSINESS

DECISION

20% 80%

Preparing

to solve the

problem

Solving

the

problem

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

Implementing Analytics in Action

10. “Analytics are from Venus and IT is from Mars!”

9. “Neither IT folks, nor Analytics folks, are inherently bad people!”

8. “Business Intelligence is dead, long live Business Intelligence!”

7. “Speed isn’t the only important metric!”

6. “Open source analytics are valuable to us, don’t discount them, work to make them better!”

“P.S. The Millennials in my team love them, but they freak me out a bit!”

5. “Make it easy for us to prove the value of emerging architectures and capabilities (especially Hadoop)!”

4. “A Hadoop Big Data Lake is dangerous without good data!”

3. “Hadoop, as cheap storage, is great, but analytics are the key to unlocking value!”

2. “People and Process are our biggest concern, as Analytics move to the front stage!”

1. “Tell us how we should be doing things, don’t pussyfoot around!”

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SAS® SAMPLE

ARCHITECTURE

WITH INTEL INSIDE Business and IT Benefits

• Tested, proven, and pre-configured to

ease deployment

• Massive SAS performance boost via in-

memory and grid computing

capabilities

• Greatly simplified resource

management

• Increased data governance

• Optimized parallel data movement

• Fit to task infrastructure for tackling all

SAS analytical tasks

Enterprise Analytics Architecture

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

SAS® SAMPLE

ARCHITECTURE

WITH INTEL INSIDE

CUSTOM GUIDANCE FOR EACH SITUATION

Sample Architectures

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SUMMARY AND

NEXT STEPS

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

innovative

experimental

“art” of prediction

problem-solving

scalable

repeatable

process-oriented

production

detailed

accessible complex

unstructured

ANALYTICS IN

ACTION SUMMARY

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

ANALYTICS IN

ACTION SUGGESTED NEXT STEPS . . .

• Challenge your established Decisioning activities

Take a new look through the lenses of [Big] Data,

Discovery and Deployment

What could you change to add more value?

• Put in place a parallel track where creativity and

innovation are encouraged and rewarded

Identify a small team of “smart creatives” from business

and IT – including your best storytellers

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ANALYTICS IN

ACTION SUGGESTED NEXT STEPS . . .

Identify one high-value challenge – expressed in

business terms

Gather larger amounts of existing data

Create a prototype and seek to generate a business

breakthrough within one month

Share your story with your colleagues – the what, not

the how

GET STARTED!

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THANK YOU