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11/18/2014 
1 
1:00 PM - 2:00 PM 
Data science and its potential to change business as 
we know it. The Roadmap to Increas...
11/18/2014 
2 
The Age of Data 
• In the last two years we have generated more data than in 
the history of mankind 
• Dat...
11/18/2014 
3 
Entering the Age of Data 
• Data is THE central business asset: 
• “Data are an organization’s sole, non-de...
11/18/2014 
4 
Data Scientist 
“Data Scientist” 
• Data Scientist: The Sexiest Job of the 21st Century 
Harvard Business R...
11/18/2014 
5 
Big-Data 
Gartner Hype Cycle for Big Data, 2012
11/18/2014 
6 
Venn Diagram of Data Scientists
11/18/2014 
7 
Statistics vs. Data Science 
http://blog.revolutionanalytics.com/data-science/ 
Business Intelligence vs. D...
11/18/2014 
8 
Data Science as a strategic asset 
“85% of eBay’s analytic workload is new and 
unknown. We are architected...
11/18/2014 
9 
Today most big data is retrospective, why 
is there a need for real-time and predictive 
Retrospective 
Rea...
11/18/2014 
10 
Today's Cycle 
Where is Real Time?
11/18/2014 
11 
Advance Analytics 
• The time to use the output is increasingly 
getting shorter – Real Time is becoming v...
11/18/2014 
12 
Business Focus of Analytics 
Prescriptive analytics enable 
customization of patient care 
Predictive anal...
11/18/2014 
13 
Maturity of Big Data Analytics Technologies
11/18/2014 
14 
Evolving Database Support for Big Data
11/18/2014 
15 
The Growing World of Data! 
Analytics Techniques for Big Data 
http://ibmdatamag.com/2014/01/deriving-inno...
11/18/2014 
16 
Example Big Data Visualization Tool - Tableau 
Example Big Data Visualization Library – d3.js 
https://git...
11/18/2014 
17 
What is Your Organization's 
Analytics Maturity Quotient (AMQ)? 
http://www.forbes.com/sites/piyankajain/2...
11/18/2014 
18 
Internet of Things 
• A system . . . that would be able to 
instantaneously identify any kind of object. 
...
11/18/2014 
19 
Internet of Things and the Cloud 
• It is projected that there will be 24 billion devices on the 
Internet...
11/18/2014 
20 
Sensors (Things) as a Service 
Sensors as a Service 
Sensor 
Processing 
as a Service 
(could use 
MapRedu...
11/18/2014 
21 
Tapping into the Data 
• Data Storage 
• Reporting 
• Analytics 
• Advanced Analytics 
– Computing with bi...
11/18/2014 
22
11/18/2014 
23 
“Big Data” and it’s close 
relatives “Cloud Computing”, 
“Social Media” and "Mobile" 
are the new frontier...
11/18/2014 
24 
Volume 
Volume is increasing at incredible 
rates. With more people using high 
speed internet connections...
11/18/2014 
25 
Velocity 
The speed at which data enters organizations these days is 
absolutely amazing. With mega intern...
11/18/2014 
26 
But I Believe These are the Real Four
11/18/2014 
27 
What matters when dealing with Data 
Science? 
Usage 
Quality 
CCoonntteexxtt 
Streaming 
Scalability 
As ...
11/18/2014 
28
11/18/2014 
29 
Clinical Quality Measures #1 
Has Quality Measure 
Has Sub-practice Has Sub-practice 
Practice #1 Practice...
11/18/2014 
30 
Conclusion 
• The Age of Data is here 
• Data is the central business asset 
• Data generation has changed...
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Data science and its potential to change business as we know it. The Roadmap to Increased Value from Your Data.

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Data science and its potential to change business as we know it. The Roadmap to Increased Value from Your Data.

  1. 1. 11/18/2014 1 1:00 PM - 2:00 PM Data science and its potential to change business as we know it. The Roadmap to Increased Value from Your Data. Room: Main Stage Theater Speaker: David Smith Managing data in an excel spreadsheet is a far cry from being able to strategically make decisions in real time using your big data stores. Analytics capabilities range from simple reporting to predictive analytics to competing on analytics. Where does your organization fall on the curve? Data science is the study of the generalizable extraction of knowledge from data. It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. In this session, we will examine how organizations increase in their data science maturity, and correspondingly in their capabilities. We will explore the increased value to be gained as capabilities increase and we will discuss how the Data science vary across different industries, both public and private. Data science and its potential to change business as we know it. The Roadmap to Increased Value from Your Data. David Smith President dsmith@socialcare.com linkedin.com/in/davidsmithaustin
  2. 2. 11/18/2014 2 The Age of Data • In the last two years we have generated more data than in the history of mankind • Data is expected to double in size every two years through 2020, exceeding 40 zettabytes (40 trillion gigabytes) 2020 2012 - 2014 The Beginning – 2011 The Economist: digital information increases10 times/5 years! Business Problem More than half of business and IT executives, 56 percent, report they feel overwhelmed by the amount of data their company manages. Many report they are often delayed in making important decisions as a result of too much information. Surprisingly, 62 percent of C-level respondents – whose time is considered the most valuable in most organizations – report being frequently interrupted by irrelevant incoming data.
  3. 3. 11/18/2014 3 Entering the Age of Data • Data is THE central business asset: • “Data are an organization’s sole, non-depletable, non-degrading, durable asset. Engineered right, data’s value increases over time because the added dimensions of time, geography, and precision.” (Peter Aitken) • Data generation has changed forever • Instrumentation of All businesses, people, machines • Data is born digitally and flows constantly • “All things are flowing..” (Heraclitus, 500 BC) Emergence of a Fourth Research Paradigm: Data Science • Thousand years ago – • Experimental Science Description of natural phenomena • Last few hundred years – • Theoretical Science Newton’s Laws, Maxwell’s Equations… • Last few decades – • Computational Science Simulation of complex phenomena • Today – • Data-Intensive Science Scientists overwhelmed with data!
  4. 4. 11/18/2014 4 Data Scientist “Data Scientist” • Data Scientist: The Sexiest Job of the 21st Century Harvard Business Review, October 2012 • The “Hot new gig in town” O’Reilly report • The next sexy job in next 10 years will be statistician” – Hal Varian, Google Chief Economist • Geek Chic – Wall Street Journal – new cool kids on campus • The future belongs to the companies and people that turn data into products • “The human expertise to capture and analyze big data is both the most expensive and the most constraining factor for most organizations pursuing big data initiatives” – Thomas Davenport
  5. 5. 11/18/2014 5 Big-Data Gartner Hype Cycle for Big Data, 2012
  6. 6. 11/18/2014 6 Venn Diagram of Data Scientists
  7. 7. 11/18/2014 7 Statistics vs. Data Science http://blog.revolutionanalytics.com/data-science/ Business Intelligence vs. Data Science http://blog.revolutionanalytics.com/data-science/
  8. 8. 11/18/2014 8 Data Science as a strategic asset “85% of eBay’s analytic workload is new and unknown. We are architected for the unknown.” Oliver Ratzesberger, eBay • Data exploration – data as the new oil  The exploration for data, rather than the exploration of data  Uncovering pockets of untapped data  Processing the whole data set, without sampling  eBay’s Singularity platform combines transactional data with behavioral data, enabled identification of top sellers, driving increased revenue from those sellers 15 Data Science as a strategic asset “Groupon will not be the first or last organization to compete and win on the power of data. It’s happening everywhere.” Reid Hoffman and James Slavet Greylock Partners Data harnessing – data as renewable energy  Harnessing naturally occurring data streams  Like harnessing raw energy to be converted into usable energy  Conversion of raw data into usable data 16
  9. 9. 11/18/2014 9 Today most big data is retrospective, why is there a need for real-time and predictive Retrospective Real-time Predictive
  10. 10. 11/18/2014 10 Today's Cycle Where is Real Time?
  11. 11. 11/18/2014 11 Advance Analytics • The time to use the output is increasingly getting shorter – Real Time is becoming very common • Limited available human resources, and performance is often unreliable due to human fatigue and distraction. Therefore, automated real-time sensor processing techniques are required to reliably detect and discriminate targets of interest • Limited automated processing and tagging tools • – Still NOT enough Goal of Data Science 22 To bring the Right information to the Right people at the Right time to enable the Right action!
  12. 12. 11/18/2014 12 Business Focus of Analytics Prescriptive analytics enable customization of patient care Predictive analytics enable better diagnoses of patients Suggestive analytics enable optimization alerts at point of care Analytics enable internal process optimization and waste reduction Analytics enable compliance to changing regulatory reporting Analytics enable monitoring of internal processes of organization Data standardized to enable efficient development of analytics Data collected to enable analytics across data from multiple systems Analytics limited to department level monitoring Adapted from “The Healthcare Analytics Adoption Model: A Framework and Roadmap”, Health Catalyst White Paper, http://www.healthcatalyst.com/wp-content/uploads/2013/11/analytics-adoption-model-Nov-2013.pdf SocialCare Confidential and Proprietary, Distribution Restricted Data Freshness for Analytics Data updated within minutes Data updated within one hour Data updated within one day Data updated within one week Data updated within one month Data updated within one month Data updated within one month Data updated within one month Data not available or many versions of same data 24 Healthcare Analytics Adoption Model
  13. 13. 11/18/2014 13 Maturity of Big Data Analytics Technologies
  14. 14. 11/18/2014 14 Evolving Database Support for Big Data
  15. 15. 11/18/2014 15 The Growing World of Data! Analytics Techniques for Big Data http://ibmdatamag.com/2014/01/deriving-innovation-from-a-data-driven-mind-set-part- 1/
  16. 16. 11/18/2014 16 Example Big Data Visualization Tool - Tableau Example Big Data Visualization Library – d3.js https://github.com/mbostock/d3/wiki/Gallery
  17. 17. 11/18/2014 17 What is Your Organization's Analytics Maturity Quotient (AMQ)? http://www.forbes.com/sites/piyankajain/2012/06/22/what-is-your-organizations-analytics-maturity/
  18. 18. 11/18/2014 18 Internet of Things • A system . . . that would be able to instantaneously identify any kind of object. • Network of objects . . • One major next step in this development of the Internet, which is to progressively evolve from a network of interconnected computers to a network of interconnected objects … • From communicating people (Internet) ... to communicating items … • From human triggered communication … ... to event triggered communication
  19. 19. 11/18/2014 19 Internet of Things and the Cloud • It is projected that there will be 24 billion devices on the Internet by 2020. Most will be small sensors that send streams of information into the cloud where it will be processed and integrated with other streams and turned into knowledge that will help our lives in a multitude of small and big ways. • The cloud will become increasing important as a controller of and resource provider for the Internet of Things. • As well as today’s use for smart phone and gaming console support, “Intelligent River” “smart homes” and “ubiquitous cities” build on this vision and we could expect a growth in cloud supported/controlled robotics. • Some of these “things” will be supporting science • Natural parallelism over “things” • “Things” are distributed and so form a Grid 37 Data available from “Internet of Things”
  20. 20. 11/18/2014 20 Sensors (Things) as a Service Sensors as a Service Sensor Processing as a Service (could use MapReduce) Output Sensor A larger sensor ……… https://sites.google.com/site/opensourceiotcloud/ Open Source Sensor (IoT) Cloud
  21. 21. 11/18/2014 21 Tapping into the Data • Data Storage • Reporting • Analytics • Advanced Analytics – Computing with big datasets is a fundamentally different challenge than doing “big compute” over a small dataset Utilized data Unutilized data that can be available to business Business, Knowledge, and Innovation Landscape • Typically 80% of the key knowledge (and value) is held by 20% of the people – we need to get it to the right people • Only 20% of the knowledge in an organization is typically used (the rest being undiscovered or under-utilized) • 80-90% of the products and services today will be obsolete in 10 years – companies need to innovate & invent faster
  22. 22. 11/18/2014 22
  23. 23. 11/18/2014 23 “Big Data” and it’s close relatives “Cloud Computing”, “Social Media” and "Mobile" are the new frontier of innovation. Driven by Data Science and Advance Analytics Volume Variety Velocity ………..
  24. 24. 11/18/2014 24 Volume Volume is increasing at incredible rates. With more people using high speed internet connections than ever, plus these people becoming more proficient at creating content and just more people in general contributing information are combined forces that are causing this tremendous increase in Volume. Variety Next in breaking down Big Data into easily digestible bite-size chunks is the concept of Variety. Take your personal experience and think about how much information you create and contribute in your daily routine. Your voicemails, your e-mails, your file shares, your TV viewing habits, your Facebook updates, your LinkedIn activity, your credit card transactions, etc. Whether you consciously think about it or not the Variety of information you personally create on a daily basis which is being collected and analyzed is simply overwhelming.
  25. 25. 11/18/2014 25 Velocity The speed at which data enters organizations these days is absolutely amazing. With mega internet bandwidth nearly being common place anymore in conjunction with the proliferation of mobile devices, this simply gives people more opportunity than ever to contribute content to storage systems. VELOCITY CRM Data GPS Demand Inventory Speed VOLUME Velocity VARIETY Transactions Opportunities Service Calls Customer Sales Orders Emails Tweets Planning Things Mobile Instant Messages Worldwide digital content will double in 18 months, and every 18 months thereafter. In 2005, humankind created 150 exabytes of information. In 2011, over 1,200 exabytes was created. 80% of enterprise data will be unstructured, spanning traditional and non traditional sources.
  26. 26. 11/18/2014 26 But I Believe These are the Real Four
  27. 27. 11/18/2014 27 What matters when dealing with Data Science? Usage Quality CCoonntteexxtt Streaming Scalability As the world gets smarter, infrastructure demands will grow Smart traffic systems Smart water management Smart energy grids Smart healthcare Smart food systems Smart oil field technologies Smart regions Smart weather Smart countries Smart supply chains Smart retail Smart cities
  28. 28. 11/18/2014 28
  29. 29. 11/18/2014 29 Clinical Quality Measures #1 Has Quality Measure Has Sub-practice Has Sub-practice Practice #1 Practice #2 Works In Physician #1 Patient #6 Practice #3 Lab #1 Physician #3 Xray #1 Logical ID = 1 Version ID = 3 Physician #2 Patient #3 Subject Received Outgoing Referral Observation #1 SOAP Note #1 Continuity Of Care #1 Continuity Of Care #2 Export CCD Import CCD Hospital #1 Is Primary Care Physician For Had Test Work In Associated With Document Store Made Observation Had Observation Annotated Document Xray #1 Logical ID = 1 Version ID = 2 Xray #1 Logical ID = 1 Version ID = 1 Made Referral Patient #9 (Remote) Patient Registry Lab Request #7 Lab Response #8 Provider Registry Requestor Response Subject For Source Physician #10 (Remote) Incoming Referral Made Referral Received Referral Referral Subject SocialCare Example Objects and Relationships
  30. 30. 11/18/2014 30 Conclusion • The Age of Data is here • Data is the central business asset • Data generation has changed forever • The World is moving to Real Time • Data Science is the Key • Your legacy analytic software WILL fail in the Age of Data • Crisis of software that scales to meet demand • Advanced Analytics Must be embedded in the collectors and sensors • Think about where the data comes from • Attempt to capture and analyze any data that might be relevant, regardless of where it resides • Maturity Models have potential for success • To be successful, they must be understood at - and mapped to - the application level of the enterprise processes • Data Science is changing how data is: • Collected, discovered, analyzed, used, acted upon …

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