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Big Data Analytics

Using the information effectively

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Agenda

• Big Data• Making Sense of it all• A Framework of Understanding• Topical information• Non Topical Information• Analytics • Examples• Getting there• Q&A

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Social Network Diagram

• Contextual analytics is one of the hottest areas of interest pertaining to big data today

• Smart companies know there is tremendous value in contextual analytics. But aggregating, categorizing, summarizing, exploring and contextualizing unstructured data is a big undertaking.

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Big Data

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What is the Big Data market?

Source: “Big Data Market Size and Vendor Revenues”, Wikibon, Jeff Kelly, David Valante, David Elgyer, Feb 2013 – actual data through 2011

Acronyms: TBD = to be determined; SI = systems integrator; BPO = business process outsourcing

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Sample Industry Applications of Big Data

Telco

Call Detail Record (CDR) analytics for:• Customer service• Network planning • Regulatory

compliance

Financial Services

Transaction analytics for:• Fraud detection• Customer retention• Distribution network

planning (Branch, ATM, Call Center)

• Regulatory compliance

• Consumer card / Merchant activity

Utilities

Network / Process analytics for:• Grid monitoring /

reliability studies• Preventive

Maintenance• Power production

monitoring / planning

Retail

Product analytics for:• Market Basket analytics• SKU trending• Competitive analyses• Context-aware buying• Social indicators of

brand

Healthcare

Patient analytics for:• Cost of care

reduction• Quality of care

improvement• Claims optimization• Service provider

consistency• Outcome

diagnostics• Regulatory

compliance

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Why Big Data? Insights from Analysis

• Time college football products to win customers– WalmartLabs: social media buzz indicates when customers are

getting excited about the upcoming season and their team(s). Combined with ShopyCat app provides targeted promos on team items.

• Detecting nosocomial infections before they kill infants– Toronto hospital – Nosocomial infections can be life-threatening

to premature infants if not treated quickly. Neonatal monitoring with real-time analytics can detect heart beat patterns that identify an infection before symptoms appear.

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Wal-Mart handles more than 1 million customer transactions every hour which import into databases containing more than 2.5 petabytes

Volume Velocity Variety

1M/hour

In addition to all procedure, claims and payment systems’ structured data add unstructured data in EMRs, patient monitoring devices, publications, drug structures, social network comments, carrier health sites, post-treatment care records…

80%

Exist in the digital universe as of early 2013

1 zettabyte = 1,000 exabytes1,000,000 petabytes10^9 terabytes10^12 gigabytes

2.7zettabytes

What Drives Big Data Analytics

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EngineeringSocial/Mobile

The Big Data Ecosystem

Enterprise Systems

Customer Loyalty & Service Systems

CustomerCase Files

E-MailsAudioImagesProvisioningSystems

Variety Veracity

Velocity Volume

Analysis

Business Outcomes

Predictive AnalyticsCEP

Operational ControlSimulation

Social AnalyticsDigital Marketing

WEB Analytics

Blogs,Communities

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Hadoop and other options

• A strategy for bringing together hardware and software

• What choices are available and how do you choose the best option?

• How do I govern it?

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Big Data Toolscape

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There are Many Use Cases for a Big Data Platform

Social Media - Product/brand Sentiment analysis Brand strategy Market analysis RFID tracking & analysis Transaction analysis to create insight-

based product/service offerings

Multimodal surveillance Cyber security Fraud modeling & detection Risk modeling & management Regulatory reporting

Innovate New Productsat Speed and Scale

Know Everything about your Customer Social media customer sentiment

analysis Promotion optimization Segmentation Customer profitability Click-stream analysis CDR processing Multi-channel interaction analysis Loyalty program analytics Churn prediction

Run Zero Latency Operations

Smart Grid/meter management Distribution load forecasting Sales reporting Inventory & merchandising optimization Options trading ICU patient monitoring Disease surveillance Transportation network optimization Store performance Environmental analysis Experimental research

Instant Awareness ofRisk and Fraud

Exploit Instrumented Assets Network analytics Asset management and predictive issue resolution Website analytics IT log analysis

Back

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Processing and Archiving Strategies

• Store forever• Selective storage• Throw away after processing

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Making Sense of it all

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Making sense of it all

• Clarity of purpose• Definition of scope• Allocation of resources• Concrete result expectations• Comparative Analytical Measures (e.g.

KPIs) – Rationalization of measures into actionable

items and hierarchical groups– Defining predictive analytics workspaces

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Role of the Data Scientist

• Creating Intelligent Tagging

• Selecting tools for analysis

• Defining algorithms and data mining techniques

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A Framework of Understanding

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What is Contextualization ?

• Context is the interrelated conditions in which something exists or occurs . Helping define context is Environment, Setting, Timeline, Genre

• Why is context important?– Consistency needed in returned result sets– The context describes the internal or external “framework” – Internal contextual information is crucial– External contextual information is knowledge that which

cannot be gotten from the text of the item itself– Time and resources are wasted in searching irrelevant

and non-material information

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Problems in searching data• Voluminous• Ambiguous meanings• Inconsistent tagging• Multiple item types – text, formatted, PDF, TIFF,

graphical, blogs, mashups • Knowledge of what is wanted is required to

understand and return the proper result sets• Differentiation is necessary between

– Real-time needs (e.g. fraud detection, medical Emergency room procedures)

– Near-time needs (sometime in the near timeline)– Relaxed-time (some clearly defined future period)

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Topical information

• Topical information is generally visible in the data stream– Keywords, data ranges, etc.

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Non-topical information

• Has to be retrieved outside the item– Although topic is crucial to the relevance of an

item, non-topical criteria plays an important role in the determination of relevance and significance

– The identification and use of non-content (or “context”) descriptors is necessary

– How widely agreed upon are the values of a given criterion among users (or user groups)?

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Non-topical information cont’d

– What is the degree to which an attribute-value is “public” or “private”?• How useful is each criterion for the search

tasks to be addressed by the specific query system?

• How easily can a criterion be identified and assigned to an item?

• What methods can be applied for refining and speeding retrievals?

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Descriptors - The defining of disambiguity

• Do the content descriptors correspond or relate to non-topical relevance criteria of the system’s users?

• Will users see a relationship between their relevance criteria and these descriptors, and use these descriptors in their search queries?

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Content descriptors

• Content descriptors (topical relevance criteria) – “Public” knowledge:

• People of similar cultural backgrounds would (more or less) agree on the meanings. However, context descriptors (which can function as non-topical relevance criteria) can vary widely in the degree to which their attribute-values are considered public or private.

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Public Knowledge Examples

• “Has pictures” is a criterion that could be considered “public” as most people could agree on whether or not a document “has pictures”, if given a specific document to evaluate.

• On the other hand, the criterion of “Regency Era” is highly situationally dependent - i.e. a limited subset of the public has knowledge of it -(specifically the period between 1811 and 1820, when King George III was deemed unfit to rule and his son - the Prince of Wales - ruled as his proxy as Prince Regent)

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Genres refine taxonomy• Genre is a “folk typology” • Item categories must enjoy widespread recognition by their

intended user groups to qualify as genres.– Examples: Resumes, Ballet, Music, Chemical formulae, statistical

results• Groups of people agree on and define Genres by mutual

consent (Explicitly and Implicitly)– E.g. Taxonomies (plants, accounting, medical), laws, voting, polls

• Genres give rise to sub-genres with increasing granularity– E.g. Music, classical, romantic, new age, atonal– Genres and sub-genres may contain common elements

• E.g. classical music and romantic music may have an intersection of data points

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Genre knowledge

• Genre is a type based on purpose, form and content. – E.g. The “resume” genre is for soliciting employment,

divided into sections with contextual descriptors• Knowing a particular item’s genre also infers

significant things about an item, sometimes enough to a make a judgment regarding the Item’s relevance to an information need– E.g. The phrase “Classically Trained Musician” infers

knowledge to read music and understand musical terminology along with additional shades of musical knowledge

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Analytics

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Historical Analytics

• Presentation of historical data– Dashboards, Drill-downs, interactive reports, static reports– New methods and devices– Identifying the metrics that affect key objectives– Synchronizing those metrics through an organization– Creating user tools to show effects of good (and bad)

choices – Tying the financial, operational, and sales worlds together– Analyzing to predict the future – Refining models for accuracy

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Predictive Analytics

• Manipulation of data– Dashboards, Drill-downs, interactive reports– New methods and devices– Varying the metrics that affect key objectives– Synchronizing the impact of metrics through an

organization– Creating user tools to show effects of good (and bad)

choices – Tying the financial, operational, and sales worlds together– Creating models that show potential future scenarios – Refining models for accuracy using advanced tools and

statistics

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Examples

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Examples of Harnessing Data Resources

Retailer reduces time to run queries by 80% to optimize

inventory

Stock Exchange cuts queries from 26 hours to 2 minutes

on 2 PB

Government cuts acoustic analysis from hours to

70 Milliseconds

Utility avoids power failures by analyzing

10 PB of data in minutes

Telco analyses streaming network data to reduce hardware costs by 90%

Hospital analyses streaming vitals to detect illness

24 hours earlier

Big data challenges exist in every organization today

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In Order to Realize New Opportunities, You Need to Think Beyond Traditional Sources of DataTransactional and Application Data

Machine Data Social Data

Volume Structured Throughput

Velocity Semi-structured Ingestion

Variety Highly unstructured Veracity

Enterprise Content

Variety Highly unstructured Volume

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• Data at rest – oceans• Collection of what has streamed• Web logs, emails, social media• Unstructured documents: forms, claims• Structured data from disparate systems

• Data in movement - streams• Twitter / Facebook comments• Stock market data• Sensors: Vital signs of a newly-born

Two Sample Types of Big Data

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Getting there

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Leveraging Big Data Requires Multiple Platform Capabilities

Manage & store huge volume of any data

Hadoop File SystemMapReduce

Manage streaming data Stream Computing

Analyze unstructured data Text Analytics Engine

Data WarehousingStructure and control data

Integrate and govern all data sources

Integration, Data Quality, Security, Lifecycle Management, MDM

Understand and navigate federated big data sources

Federated Discovery and Navigation

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Outcomes Utilizing Big Data Capabilities

To Analyze Any Big Data Type

With Unique CapabilitiesAchieve Breakthrough Outcomes

Content

Transactional / Application Data

Machine Data

Social Media Data

Visualization and DiscoveryKnow Everything

About Your Customers

Run Zero-latency Operations

Innovate new products at Speed and Scale

Instant Awareness of Fraud and Risk

Exploit Instrumented Assets

Hadoop

Data Warehousing

Stream Computing

Integration and Governance

Text Analytics

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Big Data Platform and Entry Points

2 – Analyze Raw Rata

5 – Analyze Streaming Data

1 – Unlock Big Data

3 – Simplify your warehouse

4 – Reduce costs with Hadoop

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Q & A

ContactRichard Gristak, Senior Director of Business Intelligence – rgristak@ciber.com

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Thank you