big data analytics enterprise and cloud computing

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Living in the Cloud: Big Data Analytics & Enterprise Cloud

ComputingSpeaker

Scott Mongeau

© Cloud Credential Council

Agenda> Introduction: Cloud Credential Council

Tristano VacondioMarketing ManagerCCC

> Living in the Cloud: Big Data Analytics & Enterprise Cloud ComputingScott MongeauSenior Business Analytics Solutions Manager at SAS, industry leader in business analytics software and services.

Presentation

© Cloud Credential Council

The Cloud Credential Council

● Vendor Neutral● International● Non Profit

CCC History in BriefProfessional Cloud Series

CCC History in Brief Con’t.

© Cloud Credential Council

Certification Scheme

© Cloud Credential Council

Accreditation Scheme

© Cloud Credential Council

Living in the Cloud: Big Data Analytics & Enterprise Cloud Computing

© Cloud Credential Council

Living in the Clouds:

Big Data Analytics and Enterprise Cloud Computing

• Why? Overview of big data cloud analytics • What? Key trends in enterprise cloud analytics • How & Who? Insights on data science skills

11

Education• PhD (ABD)

• MBA

• MA Financial Mgmt

• Cert. Finance

• GD IT Mgmt

• MA Com Tech

Experience• SAS Institute Sr. Mgr. Business Solutions• Deloitte Manager Analytics

• Nyenrode University

Lecturer Analytics• SARK7

Owner / Principal Consultant• Genentech Inc. / Roche

Principal Analyst / Sr. Mgr.

• Atradius Sr. R&D Engineer

Senior Business Solutions Manager

scott.mongeau@sas.com +31 683 703 097

• Linkedin • Twitter• Blog

Scott Allen MongeauCertified Analytics Professional (CAP)

YouTube • Introduction to Advanced Analytics• Introduction to Cognitive Analytics • TedX: Data Analytics

39 #1

13,700

9375,000+

US $ 3.09 b

23%SAS employees worldwide

of the top

100companieson the

GLOBAL500 LIST

Annual reinvestment in

R&D

Continuous RevenueGrowth since 1976

Years ofBUSINESSANALYTICS

World’s

privately heldsoftware company

LARGEST

Customer sites in 139 countries

DATAANALYTICS MARKET LEADER

DIG

ITA

L SE

RVI

CES

Data driven innovation

Analytics maturity

Big Data Data quality

Hidden insights

Machine

learning

Predictive analytics

Optimization Text analytics

I. Why? Big Data Cloud Analytics

16

DATA Analytics DRIVERS: V3C

Social and mobile Data analytics

Interactive platforms Real-Time systems

• VOLUME• VELOCITY• VARIETY• COMPLEXITY

V3C

Online in 60 Seconds…

Qmee

http://blog.qmee.com/qmee-online-in-60-seconds/

Moore’s Law: Exponential growth of computing power

19

25,000 x

Home computers

High-capacity servers

Smartphoneexplosion

Cloud, AI, IoT

2015

WORLDWIDE PUBLIC IT CLOUD SERVICES REVENUE

IDC, “Worldwide Software Predictions, 2015”, January 2015http://www.idc.com/getdoc.jsp?containerId=WC20150128

2010 2011 2012 2013 2014 2015 2016 2017

$120B$100B

$80B$60B$40B$20B$0B

$107 BILLIONIN 2017

Cloud Computing

II. What? Cloud Models

Cloud Computing DefinitionNational Institute of Standards and Technology (NIST)

• Ubiquitous, convenient, on-demand network access…• To shared pool of configurable computing resources…• That can be rapidly provisioned with minimal effort

Cloud Computing Service ModelsLayers of Cloud Services

Cloud Computing Service ModelsLayers of Cloud Services

Cloud Computing Service ModelsLayers of Cloud Analytics Services

DAaaSPre-configured dashboards, guided data analysis, self-service analytics, expert systems, etc.

Virtual machines, servers, storage, load balancing, distributed processing, etc.

Hadoop / big data clusters, dataware housing, machine learning tools, dashboarding, etc.

Cloud Models

DEPLOYMENT MODELS

Copyr igh t © 2015, SAS Ins t i tute Inc . A l l r i gh ts r es erved.

SAS & Cloud

WHERE WOULD YOU LIKE TO RUN SAS?

Private Cloud

Public Cloud

SAS Cloud

for 250+ customers

from 73 countries

500+ Systems

Cloud Analytics

Credentials

© Cloud Credential Council

Leading PublicCloud Vendors

https://www.gartner.com/doc/reprints?id=1-2IH2LGI&ct=150626&st=sb

Magic Quadrant for Public Cloud Storage Services, Worldwide25 June 2015 | ID:G00268914Analyst(s): Arun Chandrasekaran, Raj Bala

Major Public Cloud ServicesCloud Solution

Amazon Microsoft Google Oracle HP IBM

STORAGE

Big Data Amazon S3 HDFS Cloud Storage (GFS) Oracle Big Data Appliance X3-2 HP StoreAll Storage IBM SmartCloud

NoSQL DynamoDB Table Storage API AppEngine Datastore Oracle NoSQL DB ACID (C + A) compliant Sqoop DB2 broker

Relational MySQL or Oracle SQL Azure Cloud SQL Oracle RDB SAP HANA IBM DB2

PRO

CESSING

Hosting Amazon EC2 Azure Compute AppEngine Amazon Elastic Compute Cloud (EC2)

HP Enterprise Cloud Services

IBM SmartCloud Enterprise+

Big Data Analytics Elastic MapReduce Hadoop on Azure BigQuery Oracle Analytics HAVEn Big Data BigInsights /

IBM Bus. Analytics

30

Cloud Usage

• Financials and accounting: 37.8%• Customer Relationship Management

(CRM): 33.8%• Sales Force Automation (SFA): 31.9%• Data warehouse/analytics: 29.5%• Supply Chain Management (SCM):

27.0%

http://www2.deloitte.com/us/en/pages/deloitte-growth-enterprise-services/articles/disruption-in-the-mid-market-how-technology-is-fueling-growth.html

III. How? Data Analytics

Analytics Trends

• Influencing business strategy and operational priorities: 34.4%

• Providing metrics, information and tools needed for sound business decisions: 34.0%

• Forecasting and reporting business results: 20.6%

• Predictive client, customer, or business behavior analysis: 8.4%

http://www2.deloitte.com/us/en/pages/deloitte-growth-enterprise-services/articles/disruption-in-the-mid-market-how-technology-is-fueling-growth.html

VALUE

SOPH

ISTI

CAT

ION

DESCRIBE

PREDICT

OPTIMIZE

What happened?

What are trends?

What to do?Data AnalyticsFunctional

VALUE

SOPH

ISTI

CAT

ION

DESCRIBE

PREDICT

OPTIMIZE

BusinessIntelligence (BI)

Econometrics Forecasting

Machine Learning

Operations Management

Data AnalyticsHistorical

Descriptive / Exploratory analytics CLUSTERING

EXPLORATORY & PREDICTIVE - CLASSIFICATIONDescriptive & Predictive analytics

Descriptive & Predictive analytics PREDICTIVE - REGRESSION ANALYSIS

Descriptive & Predictive analytics ANOMALY DETECTION

Descriptive & Predictive analytics CONDITIONAL / CONTEXTUAL ANOMALIES

• An instance may be anomalous within a context / condition

The Analytics Ice Cream Shop

How do we make complex sets of data available to non-technical users so they can seek patterns and discover new insights?

Visualization

How do we make complex sets of data available to non-technical users so they can seek patterns and discover new insights?

Data Quality

How do we efficiently collect, store, transform, and retrieve the data we need while ensuring quality?

AMOUNT OF EFFORT

VALU

E TO

BU

SIN

ESS

Descriptive

IV. Who? Data Analytics Skills

Calvin.Andrus (2012) http://en.wikipedia.org/wiki/File:DataScienceDisciplines.png

Data Science Skillset

Calvin.Andrus (2012) http://en.wikipedia.org/wiki/File:DataScienceDisciplines.png

Data Science Skillset

48

DATA SCIENCE TECHNOLOGIES

49

DATA SCIENCE TECHNOLOGIES

50

DATA SCIENCE TECHNOLOGIES

SUMMARY

Thank You!

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