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Changing,the,Compu5ng,Landscape,InfoApps Questions Collect and Normalize: Parse, tag, index,...
Transcript of Changing,the,Compu5ng,Landscape,InfoApps Questions Collect and Normalize: Parse, tag, index,...
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Synthexis
©Synthexis, LLC
Changing the Compu5ng Landscape Sue Feldman, CEO Synthexis [email protected]
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Agenda • The technology dilemma today • Five technology trends that will change the compu5ng landscape - Integrated informa5on management, access and analysis - Dynamic compu5ng for a changing informa5on landscape � Probabilis5c compu5ng � Adap5ve learning � Analy5cs and big data - Context, Conversa5on, InfoApps • Integrated informa5on, integrated technologies
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The problem
• Too much informa5on • Changing quickly • ScaLered among separate applica5ons silos • Hard to get a complete picture of business, market, compe55on, economy • Hidden surprises, opportuni5es and risks
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Trends that are Changing the Compu5ng Landscape • Integrated informa5on plaOorms • Dynamic Compu5ng - ShiP to probabilis5c compu5ng - Learning systems - Big data and analy5cs • Context, Conversa5on and InfoApps
Source: The Answer Machine by Susan Feldman. Morgan & Claypool 2012. morganclaypool.com
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Enterprise SoPware Landscape today
Databases
eDiscovery
Search
Business
intelligence
Reports
HR
ERP
Sales & Marke5ng
VoC
WCM
Archiving
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Synthexis
Collect
Organize
Question
Analyze
Discuss
Decide
Tools
Answers
S o u r c e s
In teract
Questions
Integrated Informa5on PlaOorm Processes
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Questions Answers
Prepare Data: • Normalize • Extract • Tag • Parse • Index
Analyze/Predict: • Relationships • Cause and Effect • Trends • Patterns • Anomalies
Analyze Question • Disambiguate • Expand • Hypothesize
Access and Discuss: Collaboration, Search, BI, Text
Analytics, Databases, Analytics, Workflow, Hadoop/MapReduce, etc.
Visualization
S o u r c e s
Answers
In foApps
Questions
Collect and Normalize: Parse, tag, index, extract, structure
Knowledge bases, rules engines
Role of Text Analytics
Explore, Discuss, Iterate, Collaborate
Nego;ate Ques;on
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Trend 2: Shi@ to Probabilis;c Compu;ng
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Dynamic Compu;ng
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Probabilis5c Compu5ng
Predicts the likelihood that an event, document or diagnosis matches a query, paLern, rule or model - Finds similar and exact matches - Returns best matches with confidence scores based on evidence in data - Uncovers paLerns and surprises • Scalable. Ad hoc • Search-‐like architecture Suitable for text, images, or collec3ons of varied informa3on, including structured data
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Sta5c vs. Dynamic Compu5ng Sta;c Dynamic
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Determinis;c Probabilis;c
Pre-‐defined parameters Ad hoc parameters
Exact matching Similarity/fuzzy matching
Monitor known processes or events (sales, Confidence scores
Schemas Schema-‐less
Pre-‐structured data Structured by query or clustering
Preset reports Interac5ve and itera5ve
Precise Surprises
Historical Current and future
Both are needed!
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Trend 3: Adap;ve Learning
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Dynamic Compu;ng
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Adap5ve Learning • Adapt. Learn from new data and from mistakes • May be trained using a set of examples for each paLern or category
• Categorize new data to match closest learned paLern • Scalable. Accommodate large, diverse data stores • More data improves accuracy • Detect changes and anomalies • Consistent, without human biases • Key ingredient in big data applica5ons Examples: Categoriza3on, machine transla3on, recommenda3on systems
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Dynamic Compu;ng
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Big Data and Analy5cs
A set of technologies that: - Solve complex informa5on problems economically.
- Collect, manage, mine, analyze and model large volumes or streams of informa5on. - Integrate informa5on from varied sources for deeper/broader understanding - Deliver faster and beLer understanding of phenomena: weather, diseases, customers, products, risks, marketplace trends, etc.
- Enable high velocity data capture, discovery and analysis
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Synthexis
Big Data and Analy5cs: The Informa5on Advantage
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Micro segmented data for beLer healthcare, more targeted sales, beLer search results, beLer customer support.
• More data and data types è more clues, beLer targe5ng • Beyond canned reports è ad hoc discovery • Finds paLerns in data across sources
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Synthexis
From Sta5c to Dynamic: A Spectrum of Uses
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Monitor social media Predict healthcare
treatment outcomes
Climate modeling & predic5on
Voice of the customer Detect/ predict
investment trends
Fraud detec5on & predic5on Web search
Government intelligence
Monitor and respond to sensor data
Past Future Present
eCommerce recommenda5ons
Report
Drug discovery Innova5on support
Monitor, Find, Analyze Predict
Churn preven5on
Plant management
eDiscovery
Emergency services planning and support
Early product warning Traffic management
Sales and marke5ng reports
Decision support Financial reports
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Trend 5: Context, Conversa;on, and InfoApps
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Role of Context User
Trends, Alerts, Analyses decision support,
Answers
Context: task, personal profile, permissions,
Adap5ve learning systems
Databases
Content analy5cs: text, speech,
image Business
intelligence
Predic5ve analy5cs
Search Apps: CRM, VoC, Web Analy;cs, CM PLM, ERP, Finance, Sales, Marke;ng
Rules, Models
Knowledge bases
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Context, Conversa5on, InfoApps Create specialized, integrated informa5on work environment specific to a process or task • Contextually aware • Conversa5onal and stateful • Integrate mul5ple informa5on sources, mul5ple technologies • Trained on knowledge bases for terminology, processes • Intui5ve UI hides complexity of underlying plaOorm - Interac5ve charts, graphs, maps, visualiza5ons - Implicit queries - Natural user interac5on: voice, typing, clicking, gestures - Mul5ple devices with appropriate interfaces - Remembers what you were doing no maLer which device you use
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Cars for Techies
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Do users have to be mechanics?
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Cars for Transporta5on
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Synthexis
Examples
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Number of reported flu cases over last 5 years with type of flu, mortality. Data sources:
• Social media discussions
• News media Text extrac3ons of loca3on, 3me, disease, sen3ment, social network analysis
Past Future Present
BI/Repor:ng
Data sources: • Hospital records • Physician reports • Mortality records • News
Search & Pa?ern Detec:on
Predic:ve Modeling & Analy:cs
Emergency services planning and support
Discover emerging disease trends Alert to new reports
Plan for epidemic management
Predict spread of disease
Data sources: • Historical sources • Epidemiology models • Physician reports • Mortality records • Healthcare guidelines
Example: Tracking the Flu
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Example: eCommerce
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Report on brand popularity, customer opinion
Data sources: • Customer data • Social network analysis • Social media discussions • News media
Past Future Present
BI/Repor:ng
Data sources: • Transac5ons • Web analy5cs • Social media
Search & Pa?ern Detec:on
Predic:ve Modeling & Analy:cs
Product planning, inventory management BeLer search and
browsing based on current buying trends, seasonal informa5on Recommenda5ons
Predict customer churn
Data sources: • Customer data • Click analysis • Weather correlated
with purchasing
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Synthexis
Google Disease Alert Map Presents data for quick understanding
Google: Map as Context
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Synthexis
Text box is Calibri 24
IBM Watson: Big Data, Conversa5on and Context
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Synthexis
Key Points • Gather everything • Filter by context: purpose, user, task, 5me, loca5on • Know what you want: Reports? Alerts? Predic5ons? PaLerns? • Avoid building in biases and preconcep5ons. Use adap5ve learning to reveal paLerns in the data • Discovery – of surprises, paLerns, trends, and rela5onships among people, products, and events-‐-‐is a key element • Successful organiza5ons use analy5cs to out-‐compete their peers • Big Data and cogni5ve compu5ng techniques make organiza5ons more nimble and flexible • More informa5on can be an advantage
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Synthexis
©Synthexis, LLC
Contact informa5on:
Sue Feldman, CEO Synthexis, LLC [email protected] TwiLer: @susanfeldman
Sue Feldman, CEO Synthexis, LLC [email protected] TwiLer: @susanfeldman 617-‐651-‐0275
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