In the era of big data, data processing has taken center stage. The focus often is the speeds and feeds of the organizational data supply chain. Much less thought and expertise have focused on what matters most – using analytics in the proper context. Why is context so important? Why isn’t it enough to slay the v-named data dragons – namely, volume, variety and velocity? In this webinar, you’ll learn how successful organizations apply the right analytical capabilities in the proper context. Because without context, analytics can become noise that disturbs the decision-making process instead of helping it. There is no one-size-fits-all context generator. We’ll also discuss the importance of putting data into the proper context for analytical decision making, why data is your most valuable organizational asset, and how you can apply analytics in a way that converts your data into tangible benefits.

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ANALYZE YOUR DATA, TRANSFORM YOUR BUSINESS
DAN SOCEANU, SENIOR DATA MANAGEMENT SOLUTIONS ARCHITECT, SAS

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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

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SAS DATA MANAGEMENT
AGENDA

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SAS DATA MANAGEMENT
AGENDA

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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"

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CONTEXT
DATA IS NOT INFORMATION*
Informationis 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.
Knowledgeis 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.
Wisdomis 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

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CONTEXT
CONTEXT CATEGORIES*
Computingcontext
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.

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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
.

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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

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ARTIFICIAL INTELLIGENCE
…BUT IT’S NOT HERE YET!
*Source: MIT Technology Review May/June 2013, “10 Breakthrough Technologies 2013”

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SAS DATA MANAGEMENT
AGENDA

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CHALLENGE
APPLYING CONTEXT FOR ANALYTICS IN THE FACE OF POOR QUALITY DATA AND A LACK OF STANDARDS
TOO MUCH DATA
in too many places
POOR QUALITY DATA
cannot be trusted
INCONSISTENT DATA
across multiple sources
Result: the data strategy is not able to support the business strategy

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CONTEXT
CHALLENGES IN THE DAY-TO-DAY ENTERPRISE
Tools and techniques for integrating enterprise data were primarily designed for building data repositories, notfor 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

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A DAY IN THE LIFE
AGENDA

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THE QUEST FOR ANALYTICS
EXISTINGANALYTICS DATA MANAGEMENT PROCESSDataWarehouseReporting Tools
Read
ETL
Application
3rdParty
Appliance
Transactional
Social Media
DIDataMartsAnalyticsAnalytics Data Tables Ad-hoc Data Management
ETL/ELT

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METADATA
…IS IN THE EYE OF THE BEHOLDER
BusinessMetadata
–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

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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.

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THE ANALYTICS LIFECYCLE
DATA 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

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Preparing to
solve the problem
Solving the
problem
BUSINESS
PROBLEM
BUSINESS DECISION
Preparing to
solve the problem
Solving the
problem
Innovate30%20%50%

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Copyright © 2013, SAS Institute Inc. All rights reserved.
Domain Expert
Makes Decisions
Evaluates Processes and ROI
BUSINESS
MANAGER
Model Validation
Model Deployment
Model Monitoring
Data Preparation
IT SYSTEMS /
MANAGEMENT
Data Exploration
Data Visualization
Report Creation
BUSINESS
ANALYST
Exploratory Analysis
Descriptive Segmentation
Predictive Modeling
DATA MINER /
STATISTICIAN
THE ANALYTICS LIFECYCLE
MULTIPLE PARTICIPANTS AND CONTEXTS
IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTS

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SAS DATA MANAGEMENT
AGENDA

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ANALYTICS MATURITY
GOAL: FROM REACTIVE TO PREDICTIVE
Whathappened?
Standard reports
Howmany, howoften, where?
Ad hoc reports
Whereexactly is the problem?
Query drill down
Whyis this happening?
Statistical Analysis
Whatif these trends continue?
Forecast
Whatwill happen next?
Predict
Whatis the best that can happen?
Whatactions are needed?
Alerts
Raw data
Clean data
Optimize
Competitive Advantage
Degree of Intelligence

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Copyright © 2013, SAS Institute Inc. All rights reserved.
DATA MANAGEMENT
DATA MANAGEMENT METHODOLOGY

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HOLISTIC DATA MANAGEMENT
ANALYTICS REQUIRES PROPER BUSINESS CONTEXT
Data GovernanceDataWarehouse
Source Systems
Operations
Cloud
ApplianceStatic Reporting
Read
ETLDynamic Visualization
ETL Data Management ADW
Data Governance Program Data MonitoringExplorationQualityIntegrationMDM DataMartsModel Development
Operational

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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

26. Copyright © 2012, SAS Institute Inc. All rights reserved. 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 identifiedData ManagementBetter Deployment of ResourcesImprove Quality of DecisionsIncreases IT EfficiencyImprove User/Customer SatisfactionBetter Data Sharing Between Business UnitsPrepare for New InitiativesEnableData SourceIntegrationEstablishes 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

27. Copyright © 2013, SAS Institute Inc. All rights reserved. ANALYTICS PRACTICE
ANALYSIS 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

28. Copyright © 2013, SAS Institute Inc. All rights reserved. ANALYTICS PRACTICE
ANALYSIS NEEDS A PURPOSELaw Enforcement Transportation AnalysisEffective Citizen ProgramsTax Compliance & CollectionsDetecting FraudPolicy EnforcementEducational EffectivenessCriminal 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

29. Company Confidential -For Internal Use OnlyCopyright © 2013, SAS Institute Inc. All rights reserved.
DATA MANAGEMENT
THE SAS®DATA MANAGEMENT FRAMEWORKDecision MakingCustomer FocusComplianceMandatesMergers& AcquisitionsAt-Risk ProjectsOperational EfficienciesCORPORATE DRIVERSMaster/ Reference DMData VisualizationData QualityData VirtualizationData ProfilingMetadata ManagementData ExplorationData MonitoringSOLUTIONS
Data Lifecycle
Reference and Master Data
Data Security
Data Architecture
Metadata
Data Quality
Data Administration
Data Warehousing & BI/Analytics
DATA MANAGEMENT Data Stewardship Roles & Tasks Decision-making BodiesGuiding PrinciplesProgram ObjectivesDecision RightsDATA GOVERNANCEPeopleProcessTechnologyMETHODSBusiness Data GlossaryData Integration

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SAS DATA MANAGEMENT
QUESTIONS?
THANK YOU FOR YOUR TIME TODAY!