analyze your data, transform your business

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ANALYZE YOUR DATA, TRANSFORM YOUR BUSINESS

DAN SOCEANU, SENIOR DATA MANAGEMENT SOLUTIONS ARCHITECT, SAS


INFORMATION VS. KNOWLEDGE, WISDOM?

“Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?”T.S. Eliot


SAS DATA

MANAGEMENTAGENDA


CONTEXT DEFINITION

con·text/’käntekst/

Noun

The circumstances that form the setting for an event, statement, or idea, and in

terms of which it can be fully understood and assessed.

"the decision was taken within the context of planned cuts in spending"

The parts of something written or spoken that immediately precede and follow a

word or passage and clarify its meaning.

"word processing is affected by the context in which words appear"


CONTEXT DATA IS NOT INFORMATION*

Information is data in context

Data are simply collected facts and statistics used for reference or analysis. In

computing, data are quantities, characters or symbols on which operations are

performed by a computer, or being stored and transmitted.

Knowledge is information in context

Information assets are combinations of data sources, in a system. These assets are

often subject to an Information Architecture. Examples include documents, catalogs and

taxonomies. You can have data without information, but you cannot have information

without data. Knowledge encompasses the understanding of information.

Wisdom is knowledge in context

Wisdom comes from the ability to discern inner qualities and relationships in order to

apply sound judgment to a particular course of action*Source: Enterprise Architects, EA Blog, “Data is NOT Information” by Chris Aitken


CONTEXT CONTEXT CATEGORIES*

Computing context

Examples: Connectivity, bandwidth, peripherals, networks

User context

Examples: Profile, location, emotion, proximity, activity, relation

Physical context

Examples: Audio, Video, temperature, condition, texture

Time context

Examples Time of day, week, month, year, era, period

*Source: “ B. Schilt, N. Adams, and R. Want, "Context-aware computing applications, Santa Cruz, 1994.


BUSINESS CONTEXT IN COMPUTING

• Business context is used for search, discovery and navigation. Examples of

business context include: purpose, business requirements, who uses, when

to use, how to use, use cases, special procedures, how developed and tools

& methods used for analysis

• Business context also refers to social, business, or organizational

characteristics of the deployment environment, e.g. “the company is a small

enterprise”, “the company has branches in different countries”, “customers

speak different languages”, and “the revenue trend is negative”*

*Source: “Aligning Software Configuration with Business and IT Context”, Fabiano Dalpiaz, Raian Ali and Paolo Giorgini

.


ARTIFICIAL

INTELLIGENCE*MAY SOLVE ALL OUR COMPUTING PROBLEMS…

• Computing excels at computational

speed and accuracy, but cannot currently

incorporate the human dimensions of

sight, sound, touch and smell fully

(analog + digital; biologic + machine)

• “Deep Learning” techniques are focusing

on speech and sound recognition &

understanding

• Text Analytics with Sentiment Analysis

are forging the path toward large-scale

contextual analytics

*Source: Ray Kurzweil“ The Singularity is Near: When Humans Transcend Biology“,2005


ARTIFICIAL

INTELLIGENCE…BUT IT’S NOT HERE YET!

*Source: MIT Technology Review May/June 2013, “10 Breakthrough Technologies 2013”


SAS DATA

MANAGEMENTAGENDA


CHALLENGEAPPLYING CONTEXT FOR ANALYTICS IN THE FACE OF

POOR QUALITY DATA AND A LACK OF STANDARDS

TOO MUCH DATAin too many places

POOR QUALITY DATAcannot be trusted

INCONSISTENT DATAacross multiple sources

Result: the data strategy is not able to support the business strategy


CONTEXT CHALLENGES IN THE DAY-TO-DAY ENTERPRISE

Tools and techniques for integrating enterprise data were primarily

designed for building data repositories, not for business analysis:

• Information context is inconsistent and often inaccurate

• Context often has multiple representations

• Information context is often highly interrelated, yet not available in one

source or system

• Information context can have multiple temporal (time) characteristics

• The data is often incomplete, inaccurate or not current


A DAY IN THE LIFE AGENDA


THE QUEST FOR

ANALYTICSEXISTING ANALYTICS DATA MANAGEMENT PROCESS

DataWarehouse

Reporting ToolsRead

ETL

Application

3rd Party

Appliance

Transactional

Social Media

DI

DataMarts

AnalyticsAnalytics Data

Tables

Ad

-ho

c D

ata

Ma

na

ge

me

nt

ETL/ELT


METADATA …IS IN THE EYE OF THE BEHOLDER

Business Metadata

– Business rules, Definitions, Terminology, Glossaries, Algorithms

and Lineage using business language

– Audience: Business users

Technical Metadata

– Defines Source and Target systems, their Table and Fields

structures and attributes, Derivations and Dependencies

– Audience: Specific Tool Users –BI, ETL, Profiling, Modeling

Company Confidential - For Internal Use Only

Copyright © 2013, SAS Insti tute Inc. Al l r ights reserved.

CONTEXT IN DATA MODELING DESIGN

This sample diagram represents an identifying relationship between two tables; DEPARTMENT and EMPLOYEE

• This relationship indicates that an EMPLOYEE may not exist outside of the context of a DEPARTMENT.

• In identifying relationships, the primary key of the parent table becomes part of the primary key of the child table.



THE ANALYTICS

LIFECYCLEDATA PREP & MANAGEMENT CONSUMES 80% OF THE TIME

IDENTIFY /

FORMULATE

PROBLEM

DATA PREP &

MANAGEMENT

DATA

EXPLORATION

TRANSFORM

& SELECT

BUILD

MODEL

VALIDATE

MODEL

DEPLOY

MODEL

EVALUATE /

MONITOR

RESULTS


20%80%

Preparing to

solve the problem

Solving

the

problem

BUSINESS

PROBLEM

BUSINESS

DECISION

Preparing to

solve the

problem

Solving the

problem

Innovate

30%20% 50%

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .Copyright © 2013, SAS Insti tute Inc. Al l r ights reserved.

Domain ExpertMakes DecisionsEvaluates Processes and ROI

BUSINESSMANAGER

Model ValidationModel DeploymentModel Monitoring Data Preparation

IT SYSTEMS /MANAGEMENT

Data ExplorationData VisualizationReport Creation

BUSINESSANALYST

Exploratory AnalysisDescriptive SegmentationPredictive Modeling

DATA MINER /STATISTICIAN

THE ANALYTICS

LIFECYCLEMULTIPLE PARTICIPANTS AND CONTEXTS

IDENTIFY /FORMULATE

PROBLEM

DATAPREPARATION

DATAEXPLORATION

TRANSFORM& SELECT

BUILDMODEL

VALIDATEMODEL

DEPLOYMODEL

EVALUATE /MONITORRESULTS


SAS DATA

MANAGEMENTAGENDA


ANALYTICS

MATURITY

GOAL: FROM REACTIVE TO PREDICTIVE

What happened?

Standard

reports

How many, how often, where?

Ad hoc

reports

Where exactly is the problem?Query

drill down

Why is this happening?Statistical

Analysis

What if these trends continue?Forecast

What will happen next? Predict

What is the best that can happen?

What actions are needed?Alerts

Raw

data

Clean

data

OptimizeCompetitive Advantage

Degree of Intelligence


HOLISTIC DATA

MANAGEMENTANALYTICS REQUIRES PROPER BUSINESS CONTEXT

Data Governance

DataWarehouse

Source Systems

Operations

Cloud

Appliance

Static Reporting

Read

ETL

Dynamic Visualization

ETL

Da

ta M

an

ag

em

en

t

ADW

Data Governance Program

Da

ta M

on

ito

rin

g

Exp

lora

tio

nQ

uali

tyIn

teg

rati

on

MD

M

DataMarts

Model Development

Operational


DATA MANAGEMENT ESSENTIAL CAPABILITIES FOR ANALYTIC SUCCESS

Enterprise Data Access

• Relational, File, XML, Semi-Structured / Unstructured

• Message Queues, Streaming

• Data Federation

Data Management

• Data Integration

• Data Quality

• Master Data Management

Analytics Management

• Model Management & Monitoring

• Champion / Challenger Process

• Model Deployment & Integration

Decision Management

• Rules, Decision & Analytic Services

• Optimization and Automation

• Embedding Analytics and Data at the point of interaction


ANALYTICS

PRACTICE

ANALYSIS NEEDS DATA

Outcome:

A Road Map for

embracing data

management best

practices

• Data required for analysis

must be identified, sourced,

and transformed

• Sourcing activities must fit

into IT operations and

conform to IT governance

• Critical technologies,

personnel, budgets, and

dependencies must be

identified Data Management

Better Deployment

of Resources

Improve Quality of Decisions

Increases IT

Efficiency

Improve User/Customer

Satisfaction

Better Data Sharing Between Business

Units

Prepare for New

Initiatives

EnableData

SourceIntegration

Establishes Data

Definitions

For every corporate strategy and problem, there is a corresponding data need!

Data quality inhibits usage

of forecasting tools – not

able to leverage available

technology

Conflicting definitions

at the business unit

level is a data

integration issue

Dozens of data

sources with no

plan to address

data sharing needs

Changes to the

number and structure

of data sources are

major challenges

Data definitions are

unique to business

units, and there is

no automated

integration method

Advertising traffic

information is

maintained by several

systems that have

different methods for

calculating usage

Lack of a 360° view of

vendors and agencies

prevents business

units from accurately

anticipating demand

Suboptimal data

prevents analytical

approach to

advertising

deployment


ANALYTICS

PRACTICEANALYSIS NEEDS CONTEXT

Required:

A Framework for

defining and sharing

data

• Develop policies for sharing data across

the enterprise

• Define what the data represents and what

it will be labeled

• Define who can use the data and the

restrictions on how they use it

• Identify who is responsible for data

quality

• Inability to integrate citizen data from multiple

sources and channels may lead to increased

fraud

• Inaccurate view of family units can lead to

missed opportunity to assist children

• Inaccurate criminal history data leads to poor

judicial decisions for bail bond assignments

Integrated View of Citizen Effective Education

• Inability to quickly integrate/analyze

student data from multiple sources

• Lack of universal KPI’s

• Multiple overlapping LOB projects on tap

• Missed opportunities to direct resources

to at-risk students

Adapting to Budget Reduction Realities

• Difficulty integrating data from hundreds of

data centers

• Efforts at federal & statewide transformation

and consolidation hindered

Enabling Strategic Initiatives

• Without Data Governance enterprise

level transformation is not attainable

• Currently there is a needed for data

integration that cannot be met. Federal IT

integration and improving coordination

across agencies such as Homeland

Security is a major challenge.

• Unable to execute high impact analysis such

as forecasting police deployment by

neighborhood based on historical crime data

• Suboptimal data quality limits the ability to

analyze criminality patterns for strategic

investments that can limit recidivism

Working Smarter by Leveraging Analytics Meeting New Healthcare Challenges

• Inability to coordinate across many similar

healthcare programs to drive efficiency &

limit fraud

• ACA raises the bar on needing a 360° view

of patients which is unattainable without

data governance


ANALYTICS

PRACTICE

ANALYSIS NEEDS A PURPOSE

Law Enforcement

Transportation Analysis

Effective Citizen Programs

Tax Compliance & Collections

Detecting Fraud

Policy Enforcement

Educational Effectiveness

Criminal Corrections Analysis

Required:

Priorities and

Road Map for BI

and Analytic

Capabilities

• Get consensus on business

priorities

• Identify data required for

analysis

• Quantify the business impact



DATA MANAGEMENT THE SAS® DATA MANAGEMENT FRAMEWORK

Decision MakingCustomer FocusCompliance

Mandates

Mergers &

AcquisitionsAt-Risk Projects

Operational Efficiencies

CORPORATE DRIVERS

Master/ Reference DM

Data Visualization

Data QualityData

Virtualization

Data ProfilingMetadata

ManagementData

ExplorationData

Monitoring

SOLUTIONS

Data Lifecycle

Reference and Master Data

Data Security

Data Architecture

Metadata Data QualityData

Administration

Data Warehousing & BI/Analytics

DATA MANAGEMENT

Da

ta S

tew

ard

sh

ip

Ro

les

& T

as

ks

Decision-making Bodies

Guiding Principles

Program Objectives

Decision Rights

DATA GOVERNANCE

People

Process

Technology

METHODS

Business Data Glossary

Data Integration


SAS DATA

MANAGEMENT

QUESTIONS?

THANK YOU FOR YOUR TIME TODAY!

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