Biz Analytics Cluj 2015 - Daniel Pana

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THE ANALYTICS


INFORMATION MANAGEMENT CHALLENGES

• Demand on data is increasing• Complexity of data usage is growing• Expansion of the user base• Demand for quick response is growing


BIG DATA ANDANALYTICS KEY CONSIDERATIONS

DataAnalytics

Platforms


BIG DATA AND ANALYTICS KEY CONSIDERATIONS

DataAnalytics

Platforms Structured data Unstructured data Information management “Big Data”


“Data that exceeds the processing capacity of conventional database systems.”

VOLUME VELOCITY VARIETY

AnalyticsPlatformsDataBIG DATA WHAT IS IT?



DataAnalytics

Platforms

Report Analyze Predict outcomes Quantifiable benefit


ANALYTICS THE LIFECYCLE

IDENTIFY BUSINESSPROBLEM

DATAPREPARATION

DATAEXPLORATION

TRANSFORM& SELECT

ANALYTICALMODELING

VALIDATEMODELS

DEPLOYMODELS

EVALUATE /MONITORRESULTS

Providing “out of the box,” usable analytics to anyone

Approachable Analytics

User OrientedEasy-to-use with self-service capabilities, while allowing IT to manage and govern

Anywhere, Anytime

Delivering intelligence through the web, mobile intelligence

Industry Specific ExpertiseCombining the strengths of SAS solutions and technologies with industry-specific domain expertise


ANALYTICS WHERE IS ANALYTICS TODAY? CONTINUUM


ANALYTICALLYNEW

ANALYTICALLY AWARE

ANALYTICALLY INFORMED

ANALYTICALLYDRIVEN

ANALYTICALLYINNOVATIVE

LEVEL 1LEVEL 2

LEVEL 3LEVEL 4

LEVEL 5

Isolated analytics use.

Basic tools and limited or no

best practices

Predictive analytics usage is part of mission critical

applications only.

Full benefits are not understood by a

majority in the organization.

Analytics usage consists primarily of tactical and ad hoc

approaches.

Analytics dev. and deployment is

constrained, yet departments have their own experts and/or initiatives.

Analytics talent is centralized into

larger groups.

Management understands and

supports analytics for strategic value,

thus bringing business units into

alignment

Company is committed to

analytics as part of its future growth

plan.

Business units embrace their own

transformational analytical plans.

ANALYTICS USAGE

Varying Levels of Analytics Use and Expertise



DataAnalytics

Platforms The “Cloud” Grid In-Database High Performance Analytics


SAS® HIGH-PERFORMANCE

ANALYTICSKEY COMPONENTS


CAPABILITYSEGMENTATION DATA SIZE AND ANALYTIC COMPETENCE

DATA SIZE

AN

ALY

TIC

CA

PAB

ILIT

Y



DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE


CAPABILITYSEGMENTATION

DATA SIZE AND ANALYTIC COMPETENCEDIFFERENTIATING ANALYTICS

AlertsOLAPAd Hoc ReportsStandard Reports

OptimizationPredictive ModelingForecastingStatistical Analysis

REACTIVE PROACTIVE



DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE

REACTIVEAlertsOLAPAd Hoc ReportsStandard Reports

PROACTIVEOptimizationPredictive ModelingForecastingStatistical Analysis

ANALYTICS



DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS

How many products did we sell in region x last quarter?



BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS



BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS

How many people visited my web site last week and abandoned their shopping cart?



BI BIG DATA BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS



BI BIG DATA BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS How can I best optimize the marketing campaign spend across my customer base?



BI BIG DATA BI

BIG ANALYTICS

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS



BI BIG DATA BI

BIG ANALYTICS

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS How should I price my 300 mil SKU/store combinations to maximize profit?



BI BIG DATA BI

BIG ANALYTICS BIG DATAANALYTICS

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS



BIG ANALYTICS BIG DATAANALYTICS

BI BIG DATA BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS



ARCHITECTURAL CONSIDERATIONSIN-MEMORY DATABASES ARE LIMITED (SQL)

BIG ANALYTICS BIG DATA ANALYTICS

BI BIG DATA BI

DATA SIZE

IN-M

EM

OR

Y D

B

BIG DATALARGE



ANALYTICS



ARCHITECTURAL CONSIDERATIONSLASRTM ENABLES PROACTIVE ANALYTICS


BI BIG DATA BI

DATA SIZE

IN-M

EM

OR

Y D

B

BIG DATALARGE

IN-M

EM

OR

Y A

NA

LYTI

C S

ER

VE

R



ANALYTICS



ARCHITECTURAL CONSIDERATIONSLASRTM ENABLES PROACTIVE ANALYTICS


BI BIG DATA BI

DATA SIZE

IN-M

EM

OR

Y D

B

BIG DATALARGE



ANALYTICSREACTIVE

AlertsOLAPAd Hoc ReportsStandard Reports


ANALYTICS

IN-M

EM

OR

Y A

NA

LYTI

C S

ER

VE

R


SAS A LEADER IN MORE THAN 25 ANALYST REPORTS

The Forrester Wave™• Big Data Predictive Analytics Solutions• Advanced Data Visualization Platforms• Customer Analytics Solutions

Gartner Inc. Magic Quadrant• Integrated Marketing Management• Enterprise Governance, Risk and

Compliance Platforms• Marketing Resource Management

Hurwitz Victory Index• Predictive Analytics



VISUAL ANALYTICSINCLUDES PROACTIVE ANALYTICS


BI BIG DATA BI

DATA SIZE

RE

AC

TIV

EP

RO

AC

TIV

EA

NA

LYTI

C C

APA

BIL

ITY

BIG DATALARGE



ANALYTICS


BUSINESS VISUALIZATION

THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST INFORMATION

DATA VISUALIZATION ANALYTIC VISUALIZATION

EXPLORATION DISCOVERY


SAS® VISUAL ANALYTICS BUSINESS VISUALIZATION DRIVEN BY ANALYTICS

ENTERPRISE/DEPARTMENTAL

AND SELF SERVICE

VISUALIZATION POWERED BY SAS ANALYTICS REPORTING, MOBILE

COLLABORATION

• Scalable, any size data• Flexible deployment• In-memory analytics

• Analytic visualizations• “Analytics for everyone”• Text analysis

• Design once• Feature rich mobile apps• Integration with MS apps


HIGH-PERFORMANCE ANALYTICS SAS IN-MEMORY ANALYTICS

Biz Analytics Cluj 2015 - Daniel Pana

Data & Analytics

Transcript of Biz Analytics Cluj 2015 - Daniel Pana