1. Challenges and Opportunities with Big Data
Dr Hammou Messatfa
IBM Europe Government CTO
Member of the IBM Academy of Technology

2. What is big data and why is it such a popular topic and why now?
Agenda
What is big data and why is it such a popular topic and why now?
Implications on skills
Organizations are extracting value from big data
Implications on Research
IBM’s big data journey 2

3. Data is the new Oil. Data is just like crude
Data is the new Oil. Data is just like crude. It’s valuable, but if unrefined it cannot really be used.
– Clive Humby, DunnHumby
We have for the first time an economy based on a key resource [Information] that is not only renewable, but self-generating. Running out of it is not a problem, but drowning in it is.
– John Naisbitt
Oil which is the fuel for modern economy for centuries. However, Oil in its raw form has little value. It needs to be refined and separated into a large number of consumer products, from petrol and kerosene to asphalt and chemical reagents used to make plastics and pharmaceuticals. It is also used in manufacturing a wide variety of materials.
Big Data is just like oil, in it’s raw form it provide no value to enterprise, until it is processed and valuable and actionable business insights are “distilled”.
Just like the technology that made available 100 years ago to discover oil and process it to consumable products. Big Data technology is going to transform and revolutionize the way enterprise get and use. 3 3

4. 63% IQ 57% 70% BUSINESS IMPERATIVE
The number of organizations who see analytics as a competitive advantage is growing.
70%
57%
2010
2011
2012
63% IQ
In this environment, organizations using analytics are gaining real competitive advantage
-57% increase from 2010 to 2011 in respondents who say analytics creates a competitive advantage
Source:
IBM IBV/MIT Sloan Management Review Study 2011
Copyright Massachusetts Institute of Technology 2011
business initiative
BUSINESS IMPERATIVE

5. $16 30% Analytics is Progressing from the Possible to the Proven
Helps detect life
threatening conditions
up to 24 hours sooner
$16
Billion
Reduced Improper Payment
30%
Cut serious crime by
Smarter Crime Prevention
Tax Agency
North Carolina State University
Challenge - Reduce the effort to find and match commercial partners to license university research assets.
Solution - Developed an intelligence solution that analyzed data from 1.4 million documents, websites and enterprise applications and created insight from the content. Used advanced contextual data analytics and processing to find potential research and commercialization partners.
Results - Reduced the time to discover and process a research asset from three-six months to as little as seven days. Demonstrated increase in the list of potential license partners by 300 percent.
Solution components:
IBM Content Analytics, IBM BigSheets, IBM LanguageWare
The need:
Large research institutions like North Carolina State University (NC State) have thousands of research projects—intellectual property assets—that could be used to build a smarter planet. Licensing and commercialization of assets help the university recoup its costs and provide further funding for projects and departments. It is a time-consuming process to discover which assets have the most potential and then find the public or private partnership that can bring those assets to the commercial market. The sheer volume of information inevitably results in assets that are underutilized. The NC State Office of Technology Transfer needed a way to help its small staff of 19 people—seven of them licensing professionals—to comb through more than 3,000 research assets and then find the most promising partners that could bring them to market.
The solution:
IBM built an intelligence solution for the university that uses IBM BigSheets software to process large amounts of data; IBM LanguageWare software to analyze data from 1.4 million documents, websites and enterprise applications; and IBM Cognos Content Analytics software to create insight from the content. The solution uses advanced contextual searching, including customized dictionaries for particular areas of research, to create a highly efficient process for discovering the asset profile and then narrowing down the most appropriate potential partners based on structured and unstructured data from within the institution and the public Internet.
In a pilot program, the university was able to find partnerships for two research assets that showed promise but would otherwise have been abandoned. The assets—a salmonella vaccine and a drug delivery system for domestic animals—were analyzed for keywords. Then searches were conducted that found 2,000 potential matches. Additional searches eventually led to a narrowing of potential partners to 10 to 15 prospects, a manageable number that allows office staff to maximize its efforts with a higher probability of finding a match.
What makes it smarter:
The solution goes beyond a traditional keyword search. It analyzes both the research assets and the partner pool by providing customized context. It allows the licensing staff to focus on brokering the best deals for the university without having to become experts in the subject matter for every asset.
IBM software searches a massive volume of dynamic data that would be impossible to manually contextualize, reducing the time and effort required to find the best match.
It allows the university to extend the solution and create connections with other research institutions, helping to further funding for projects and departments.
McKesson - Company leaders recognized that their operations were so complex that they no longer could be managed without analytics. You could optimize inventory and free up capital using a highly accurate view cost-to-serve by product line, transportation and carbon footprint?
Solution
Developed a supply chain model that provides a highly accurate view of its cost-to-serve—by product lines, transportation and even carbon footprint. Applied advanced analytics to optimize the physical placement of inventory within its distribution centers.
Results - Ability to assess changes in its policies and supply chains has helped it increase customer responsiveness.
Transformed supply chain thereby reducing working capital by more than US$100 million.
Memphis Police Department
Business need:
Like many cities, the crime rate in Memphis was increasing—2.5 percent from 2004 to 2005 alone. To address the issue and quell residents’ concerns, the department needed to add another 500 patrol officers. However, that goal would take nearly six years to achieve. Worse, like most US cities, the department faced shrinking or fixed budgets and resources. A solution was needed sooner rather than later.
The department’s current method of solving and preventing crimes involved searching through an array of spreadsheets and paper files. The process was not only time-consuming, but also kept officers from being on patrol where they were most needed. The Memphis Police department recognized that—to address the spike in crime and make the best use of limited resources—it needed a more innovative approach to policing. It also needed a faster, more efficient way to predict, track, and respond to crimes.
Solution implementation:
The Memphis Police department IBM Business Partner ESRI as well as the Center for Community Criminology and Research at the University of Memphis to implement a pilot program based on IBM SPSS Statistics software. The program, called Blue CRUSH (Criminal Reduction Utilizing Statistical History), combines analytics plus predictive modeling capabilities track and identify crime patterns.
The solution integrates crime data from sources that range from the department’s records management system to video cameras monitoring events on the street. A geographic information system that enables officers to analyze and visualize data in the form of charts, geographical maps and reports. Using the department’s crime data, an analytical framework was developed and used as the basis for a pilot program. The results of the pilot would help the department understand which analytical and operational approaches did and did not work.
Blue CRUSH was deployed in August 2005 within select precincts before going citywide.
Benefits:
Since implementing its predictive crime solution, the Memphis Police department has realized significant benefits:
Reduced serious crime by 30 percent, including a 36.8 percent reduction in crime in one targeted area
Reduced violent crime by 15 percent
Increased the number of cases solved in the Felony Assault Unit by fourfold, from 16 percent to nearly 70 percent
Improved ability to allocate police resource in a budget-constrained fiscal environment
The solution proved it value right from the start. In a three-day test operation, the department identified a number of crime hot spots, resulting in 70 arrests in two hours—a number made on a typical weekend—and 1,200 in total. Crimes ranged from drugs to weapons charges to prostitution and other “quality-of-life” offenses.
The system provides officers insight into crime trends and patterns at a granular level, enabling them to react more swiftly and with more agility to events. Using multilayer maps, commanders can identify crime hot spots, seeing not only current activity levels, but also any changes in activities since policing the area. Commanders can also see and understand how some factors, such as abandoned housing, can affect crime trends. For example, the solution may indicate that burglaries are down in one are, but car theft is up in another. With little notice, police can be dispatched to the area and make arrests that would have been impossible before.
Smarter Healtcare
Analytics
Smarter Revenue Management

6. Characteristics of big data
Big data characteristics
Big data embodies new data characteristics created by today’s digitized marketplace
Characteristics of big data
Source: IBM methodology 6 6

7. Organizations are evolving their big data journey
What skills and processes do I need to add or modify to be successful?
PEOPLE & PROCESS
RESEARCH 7

8. Among organizations worldwide today…
An acute shortage of skills threatens our ability to address emerging opportunities and risks
Among organizations worldwide today…
have major skill gaps in mobile, business analytics, and security
has all the skills it needs to be successful applying advanced technology* for business benefit
report a skills shortage in the ability to manage information
* Includes business analytics, mobile computing, social business, and cloud computing
Sources: IBM Tech Trends report 2012, Enterprise Strategy Group, CompTIA 8

9. Big data requires a broad set of skills
"By 2015, big data demand will reach 4.4 million jobs globally, but only one-third of those jobs will be filled."
Source: Gartner "Gartner's Top Predictions for IT Organizations and Users, 2013 and Beyond: Balancing Economics, Risk, Opportunity and Innovation" 19 Oct 2012
Math and
Operations Research
Expertise
Develop analytic algorithms
Data Experts
Data architecture, management,
governance, policy
Decision Making
Executive and
Management
Apply information to solve business issues
Tool Developers
Mask complexity and analytics to lower skills boundaries
Industry Vertical
Domain Expertise
Develop hypothesis, identify relevant business issues,
ask the right questions
Visualization
Expertise
Interpret data sets, determine correlations and present in meaningful ways 9

10. Sample critical job roles Data Policy is fastest growing job role! 10

11. IBM Academic Initiative
August 14, 2013
IBM and Universities Team Up to Close a 'Big Data' Skills Gap
By Lee Gardner
IBM Corporation's skills program focused on partnering with university faculty
Our mission
Partner with academic institutions to better educate millions of students for a smarter planet and more competitive IT workforce
Key offering areas
Business Analytics
Big Data
Security
Software Engineering
Mobile Development 11

12. IBM Academic Collaboration Capabilities Map
EDUCATION
Academic Events
T3 Training
Curriculum Support
Laboratories
Instructor Certification
Faculty Awards ©
Faculty Internships
Advisory Boards
Job Fairs
Student Training
Industry Mentorship
Student Projects
Student Certification
Student Contests
Internship & Hiring
Student Communities
RESEARCH
Scientific Conferences
IBM Research Visits
Joint Funding Proposals
Research Projects
Publications
Faculty Awards ®
Research Internships
Research Network
BUSINESS
Industry Events
Technology Briefings
Market Surveys
Services
Products
Entrepreneurship
Venture Capital
Industry Community
FACULTY-CENTRIC
STUDENT-CENTRIC
PARTNERSHIP MANAGEMENT
1. EDUCATION (faculty-centric): Activities which in their majority fall under the responsibility of the academic institutions, as they form part of the formal education process of the university undergraduate and graduate programs
Academic Events
Including guest & PEP talks, workshops, seminars, conferences, symposiums that are held by eminent speakers and experts, focusing on relevant technical, industry and academic areas, supported by the participation from students, academicians, industry and research organizations.
T3 Training
Covering the delivery of training classes to faculty to dig deeper into technical and industrial aspects of the concepts that are being studied as part of the educational program, the training can be on campus through visiting experts from local or international background, or it could be hosted in a industry forum or a briefing center where subject matter expertise and cutting edge technology can be directly exposed to the target faculty members.
Curriculum Support
Covering all aspects of curriculum improvement, from curriculum modernization, evaluation, content and deployment, to curriculum accreditations and curriculum exchange with other academic institutions either at the local or international level.
Laboratories
Covering the creation and delivery of practical exercises to complement the theory imbued in the academic formal education practice, laboratories may gain access to remote technical facilities that can emulate a general purpose industry production environment, or be hands-on experiments in the local university lab.
Instructor Certification
Covering initiatives that engages faculty members in a comprehensive program with focus on enabling them to become authorized instructors, allowing them to deliver formal training in the technologies or topics of scope for the center of excellence.
Faculty Awards (Curriculum)
Covering the faculty grants from industry partners interested in funding faculty members work in curriculum updates on key critical areas that are relevant in the marketplace.
Sabbaticals
Covers all activities related to allow faculty to devote concentrated time to studies, investigations, research, scholarly writing, and other projects.  Such leaves may also be used for curriculum development and other improvements in teaching practice. These faculty leaves are generally associated with research or teaching activities that will bring new concepts and ideas to the local education community.
Advisory Board
Covers activities that are related to the overall feedback from the industry to the performance and relevance of the academic programs hosted at the university. Members of the advisory boards represent important organizations in the marketplace, therefore symbolizes a subset of the target market that the university aims to permeate with fresh graduates.
_________________________
2. EDUCATION (student-centric): Activities which responsibility is shared equally between the academic and the industry community, as they support the seamlessly transfer of university graduates in to the market workforce.
Job Fairs
Covers all aspects of coordinating activities (generally in the form of career days or events) where industry and government organizations can showcase and introduce to senior university students the variety of job roles that currently exist in the local/international marketplace.
Student Training
Covering all aspects of direct or indirect training directed to target students in the relevant technologies and topics focus of the center of excellence. Including Webinars, Serious Games, Instructor-Led Classroom Training, Virtual Learning and e-Learning material made available to students as part of the center of excellence student support services.
Industry Mentorship
Covering the career advisory support and subject matter expertise in areas of interest for the students engaging in one or more projects. Mentors are active members of the industrial community and understand the marketplace and can advice students on the local and international trends relevant to them in order to narrow down the focus of their studies.
Student Projects
Covering the activities that the student may assume to accompany classroom learning with hand-on-practical and often with research internship or industrial projects. Each department is offering industry sponsored project to expose their knowledge towards Real Practical problems. Innovative ideas are put to practice in many projects. Such industrial activity provides students with adequate exposure in tackling real life problems encountered in the working of an industrial entity. The companies also gain from the fresh perspective and inputs of the students as also bettering their profile among the general student community.
Student Certification
Covering initiatives that supports the students pursuit and attainment of industry-proven professional certifications, addressing the alignment of student skills with the market needs in technologies or topics that are under the scope for the center of excellence. This activities may include access to vendor-driven intellectual property resources, learning content and training sessions.
Student Contests
Covers all activities focused on incentivizing students to benchmark their performance and knowledge among their peers in a regional or international contests, that allows students to recognize their actual level and pursue the community recognition.
Internships & Hiring
Covers activities by which the student spends a summer break working in a project with the mentorship of an organization subject matter expert. The Internship usually occurs at the company location and could be paid or non-paid.
Co-Ops
Covers activities or programs by which a company hires a student for a long period of time, the student is part of a team and is trained to work on real projects inside the company. The Co-Op is hosted at the company workplace and is a full-time paid position.
Workforce Development
Covers activities and programs related to provide job-based education to students to bridge the gap between the academic theoretical education and industry specific knowledge required for students to hit the ground running when they join a particular organization.
Hiring Programs
Covers activities related to hiring programs and initiatives initiated by the university industry partners, including specific programs on commitment to hire top performing students, students with disabilities or as part of diversity or nationalization hiring programs.
Student Communities
Groups of students aligned in a community focus to evaluate and better understand the available programs, technologies and concepts brought by a specific vendor, industry or organization. The club organizes events, coordinates academic activities and holds a direct link to the target organization of the club, allowing a direct access for students to industry leading companies demand and activities, including: job postings, certification paths, events and other opportunities. It can also cover activities hosted in the university to support the alumni community and encourage an active link to foment continuity in education. Alumni can become ambassadors and supply the university with an unique feedback on the relevance of the university programs, some renown alumni sit at university advisory boards.
3. RESEARCH: Activities which in their majority fall under the responsibility of the academic institutions, as they form part of the formal academic research process of the university as a conduit for breakthrough ideas for the betterment of society.
Research Events
Conferences
Journals
Inventions
Faculty Awards
Scientists and graduate students will also pursue awards based on financial monetary contributions and/or branded qualitative physical representations of the recognition achieved by the student in the pursuit of its career degree. Some examples includes: Annual Best Student Recognition Events, Gold Medals of Achievement, Diplomas of Excellence Recognition, fix positions at renown companies, scholarships and PhD Fellowships.
Grants
Covers all activities related to
4. BUSINESS: Activities which responsibility is shared equally between the academic and the industry community, as they bind together science, technology and expertise in an applied approach to solve critical problems existing in the industry and society.
Page 12 of
19 April 2017 12

13. Organizations are evolving their big data journey
What are the key business issues or opportunities that big data can help me to address?
STRATEGY & VALUE 13

14. Big data adoption
Patterns of organizational behavior are consistent across four stages of big data adoption
Big data adoption
To better understand the big data landscape, we asked respondents to describe the level of big data activities in their organizations today. The results suggest four main stages of big data adoption and progression along a continuum that we have labeled Educate, Explore, Engage and Execute:
Educate: Building a base of knowledge (24 percent of respondents)
In the Educate stage, the primary focus is on awareness and knowledge development. Almost 25 percent of respondents indicated they are not yet using big data within their organizations. While some remain relatively unaware of the topic of big data, our interviews suggest that most organizations in this stage are studying the potential benefits of big data technologies and analytics, and trying to better understand how big data can help address important business opportunities in their own industries or markets. Within these organizations, it is mainly individuals doing the knowledge gathering as opposed to formal work groups, and their learnings are not yet being used by the organization. As a result, the potential for big data has not yet been fully understood and embraced by the business executives.
Explore: Defining the business case and roadmap (47 percent)
The focus of the Explore stage is to develop an organization’s roadmap for big data development. Almost half of respondents reported formal, ongoing discussions within their organizations about how to use big data to solve important business challenges. Key objectives of these organizations include developing a quantifiable business case and creating a big data blueprint. This strategy and roadmap takes into consideration existing data, technology and skills, and then outlines where to start and how to develop a plan aligned with the organization’s business strategy.
Engage: Embracing big data (22 percent)
In the Engage stage, organizations begin to prove the business value of big data, as well as perform an assessment of their technologies and skills. More than one in five respondent organizations is currently developing POCs to validate the requirements associated with implementing big data initiatives, as well as to articulate the expected returns. Organizations in this group are working – within a defined, limited scope – to understand and test the technologies and skills required to capitalize on new sources of data.
Execute: Implementing big data at scale (6 percent)
In the Execute stage, big data and analytics capabilities are more widely operationalized and implemented within the organization. However, only 6 percent of respondents reported that their organizations have implemented two or more big data solutions at scale – the threshold for advancing to this stage. The small number of organizations in the Execute stage is consistent with the implementations we see in the marketplace. Importantly, these leading organizations are leveraging big data to transform their businesses and thus are deriving the greatest value from their information assets. With the rate of enterprise big data adoption accelerating rapidly – as evidenced by 22 percent of respondents in the Engage stage, with either POCs or active pilots underway – we expect the percentage of organizations at this stage to more than double over the next year.
When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in organizational behaviors
Total respondents n = 1061
Totals do not equal 100% due to rounding 14 14

15. Big data is a business priority – inspiring new models and processes for organizations, and even entire industries 15 15

16. Organizations are evolving their big data journey
What are the essential analytics capabilities we need to ensure we have in place?
RESEARCH 16

17. But there’s still room for research!
Improve individual tools
Handle particular data types better
Make it easier to find entities in data
Make it easier to compose analyses from existing models
Improve the environment for exploring massive data?
Pre-integrated data sets to provide context
Powerful infrastructure for data management and analytics
Rich collection of analytics and tools for analysis
Expertise in all aspects of the process
A great user experience through automation and intelligent guidance
Leverage tools and environment to solve important problems for people, industry and the world at large 17

18. The Big Data Approach to Analytics is Different
Traditional Analytics Structured & Repeatable Structure built to store data
Big Data Analytics Iterative & Exploratory Data is the structure
Available Information
Analyzed Information
Capacity constrained down sampling of available information
Analyze ALL Available Information
Whole population analytics connects the dots
Business
Users
Determine
Questions
IT Team
Builds System
To Answer
Known Questions
IT Team
Delivers Data
On Flexible
Platform
Business
Users
Explore and
Ask Any Question
Both analytic approaches and valid and complementary
Traditional approach – Business users determine what questions to ask and IT structure the data to answer that question. This is well suited to many common business processes, such as monitoring sales by geography, product or channel; extract insight from customer surveys; cost and profitability analyses.
- Allows organization to answer questions that will be asked time and time again.
The Big Data approach – IT delivers a platform that consolidates data sources of interest and enables creative discovery. Then the business users use the platform to explore data for ideas and ask any question.
- Allows users to explore data in a more analytic way … the answers to questions spawn new questions to be asked.
Carefully cleanse a small information before any analysis
Analyzed Information
Analyze information as is & cleanse as needed & existing repeatable
Analyzed Information
1818

19. The Big Data Approach to Analytics is Different
Traditional Analytics Structured & Repeatable Structure built to store data
Big Data Analytics Iterative & Exploratory Data is the structure ?
Analyzed Information
Question
Data
Answer
Hypothesis
Start with hypothesis
Test against selected data
Data leads the way Explore all data, identify correlations
Data
Correlation
All Information
Exploration
Actionable Insight
Both analytic approaches and valid and complementary
Traditional approach – Business users determine what questions to ask and IT structure the data to answer that question. This is well suited to many common business processes, such as monitoring sales by geography, product or channel; extract insight from customer surveys; cost and profitability analyses.
- Allows organization to answer questions that will be asked time and time again.
The Big Data approach – IT delivers a platform that consolidates data sources of interest and enables creative discovery. Then the business users use the platform to explore data for ideas and ask any question.
- Allows users to explore data in a more analytic way … the answers to questions spawn new questions to be asked.
Analyze after landing…
Analyze in motion…
1919

20. Big data: This is just the beginning
9000
100
Sensors & Devices
Percentage of uncertain data
6000
Volume in Exabytes
Percent of uncertain data
You are here
Social Media 50
Volume
Veracity
VoIP
3000
Enterprise Data
Variety
2010
2015
Source: IBM Global Technology Outlook 2012
IBM source data is based on analysis done by the IBM Market Intelligence Department. IBM Market Intelligence data
is provided for illustrative purposes and is not intended to be a guarantee of future growth rates or market opportunity 20 20

21. Preparing data for analysis itself requires analytics
Q: “How exposed am I to my borrowers?”
SEC
Midas Flow
Search UI
Financial Companies & Key People
Crawl
Extract
Resolve
Map & Fuse
Temporal Analysis
FDIC
Reports
employment, director, officer
insider, 5% owner, 10% owner
holdings, transactions
issuer
bankruptcies, merger/acquisitions
job changes
subsidiaries, insider, 5%, 10% owner, banking subsidiaries
borrower, lender
Company
Loan
Person
Event
Security 21 21

22. Millions of tweets yield one company-specific fact
Sample Application – Real Time Lead Generation
Go for the best, DP- 2000
Buying a DSLR today !
Buying DSLR today!
Thrza gr8 deal on the mall
Prior Business Transactions
Entity Extraction, Fact Discovery, Intent & Sentiment
Social Data
Influencers
Intent
250M tweets/day
Millions of tweets yield one
company-specific fact
Customer ready to buy a DSLR camera today, possibly at a nearby mall
Michael’s online friends offer lots of advice
Text Analytics used to extract intent from Social Media
Married, Male, Spouse Birthdate, Gift Type, Intent to Purchase, Timeframe
Wifey’s birthday tomorrow, looking for a killer dslr
Sarcasm, Wishful Thinking
Maybe I should buy her that purple roadster, while I’m at it. ;-) lol
Intent to Purchase,
Gift Type?
Potential Locations and Activity
In NYC area this w/e, any good malls nearby?
Region & City Location, Timeframe, Intent to Shop
Resultant fact base contains billions of facts, and is incrementally updated
Fact segmentation or clustering is rapid enough to drive a business decision 22 22 22 22

23. Deriving actionable consumer insights from social media
Leverage social media and computational models to to predict intrinsic traits that influence consumer behavior

24. Leading IBM: Eras of computing
Cognitive Systems Era
Programmable Systems Era
Computer Intelligence
Tabulating Systems Era
Time

25. Family History Medications Findings Symptoms Patient History
Putting the pieces together at point of impact can be game changing can be life changing
difficulty swallowing
Pat. History
Family History
Medications
Findings
Symptoms
dizziness
anorexia
fever
dry mouth
thirst
frequent urination
Symptoms
Fam. History
Medications
Patient History
Diagnosis Models
Findings
Confidence
Symptoms
A 58-year-old woman presented to her primary care physician after several days of dizziness, anorexia, dry mouth, increased thirst, and frequent urination. She had also had a fever and reported that food would “get stuck” when she was swallowing. She reported no pain in her abdomen, back, or flank and no cough, shortness of breath, diarrhea, or dysuria
A urine dipstick was positive for leukocyte esterase and nitrites. The patient given a prescription fo ciprofloxacin for a urinary tract infection. 3 days later, patient reported weakness and dizziness. Her supine blood pressure was 120/80 mm Hg, and pulse was 88.
A 58-year-old woman complains of dizziness, anorexia, dry mouth, increased thirst, and frequent urination. She had also had a fever. She reported no pain in her abdomen, back, and no cough, or diarrhea.
Her medications were levothyroxine, hydroxychloroquine, pravastatin, and alendronate.
Renal Failure
no abdominal pain
no back pain
no cough
no diarrhea
UTI
Her history was notable for cutaneous lupus, hyperlipidemia, osteoporosis, frequent urinary tract infections, a left oophorectomy for a benign cyst, and primary hypothyroidism, diagnosed a year earlier
Her family history included oral and bladder cancer in her mother, Graves' disease in two sisters, hemochromatosis in one sister, and idiopathic thrombocytopenic purpura in one sister
Diabetes
Graves’ Disease
Oral cancer
Bladder cancer
Hemochromatosis
Purpura
Influenza
Family History
Hypokalemia
(Thyroid Autoimmune)
frequent UTI
cutaneous lupus
hyperlipidemia
osteoporosis
hypothyroidism
Esophagitis
Patient History
pravastatin
Alendronate
levothyroxine
hydroxychloroquine
Extract Medications
Use database of drug side-effects
Together, multiple diagnoses may best explain symptoms
Extract Findings: Confirms that UTI was present
Identify negative Symptoms
Reason with mined relations to explain away symptoms (thirst is consistent w/ UTI)
Extract Symptoms from record
Use paraphrasings mined from text to handle alternate phrasings and variants
Perform broad search for possible diagnoses
Score Confidence in each diagnosis based on evidence so far
Extract Patient History
Extract Family History
Use Medical Taxonomies to generalize medical conditions to the granularity used by the models
Most Confident Diagnosis: Esophagitis
Most Confident Diagnosis: Influenza
Most Confident Diagnosis: UTI
Most Confident Diagnosis: Diabetes
Medications
supine 120/80 mm HG
urine dipstick:
leukocyte esterase
urine culture: E. Coli
heart rate: 88 bpm
Findings 25

26. Watson enables three classes of cognitive services
Ask
Leverage vast amounts of data
Ask questions for greater insights
Natural language inquiries
e.g. - Next generation Chat
Discover
Find the rationale for given answers
Prompt for inputs to yield improved responses
Inspire considerations of new ideas
e.g. - Next generation Search  Discovery
Decide
Ingest and analyze domain sources, info models
Generate evidence based decisions with confidence
Learn with new outcomes and actions
e.g. - Next generation Apps  Probabilistic Apps 26

28. IBM Research: The World is Our Lab
Dublin
China
Zurich
Almaden
Haifa
Watson
Tokyo
India
Austin
Brazil
Melbourne
IBM Research labs
Labs added since 2010
Other IBM Research presence 28

29. Social Analytics/Consumer Insight
IBM building strength and leadership in big data and analytics
Building the most comprehensive Business Analytics & Optimization portfolio & partnerships
2013
More than $16B in Acquisitions Since 2005
More than 10,000 Technical Professionals
More than 7,500 Dedicated Consultants
Largest Math Department in Private Industry
More than 27,000 Business Partner Certifications
Partner with more than 1000 Universities on Analytics
Talent Acquisition
Social Analytics/Consumer Insight
Workload Optimized Systems
Advanced Case Management
Content Analytics
Decision Management
Stream Computing
Pervasive Content
pureScale
pureXML
Deep Compression
Developer Productivity
Autonomic Operations
2005
Innovation that Matters 29

30. 30