Introduction to Predictive
Analytics and Data collection 402
Introduction to Strategic Data Assets and Tools for
Predictive Analytics
Generic slides from this are used in Video 1 – The assignments and dates are
examples for WINTER 2014 SECTIONS of MY CLASSES (SEC55,58,63). For
assignment dates for your class, your faculty will provide them
Sam - Nethra Sambamoorthi, PhD
Lead Faculty - 402
Washington Post article
reports…
• Digital related
• Ubiquitous related
• So(power of people – truly democratic) Lo (dynamics) Mo (every
person is a broadcasting station)
• Data related
• Analytics related
• Information products/cooperatives/democratic
• All systems in real time
• That will remove inefficiencies in knowledge generation and sharing,
remove producer-consumer distance, provide every person their
voice, and more and more entrepreneurial aspirations will become
easier to accomplish and grow. Innovations supported digital
principles and platforms has so much tapped, invented, and
integrated in all walks of life…
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
• 65% of the jobs are not yet known that will be normal in 10 years
• If not 100% by 10 years, we are moving in the direction of jobs that
are going to be
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The IT sector is likely to need: (more than 50% of titles are related to analytics)
In other disciplines, there will be a need for analytics too…
engineers of all kinds,
accountants,
lawyers,
financial advisers,
project managers,
specialist doctors,
nurses, pharmacists,
physical therapists,
veterinarians,
psychologists,
health services managers,
schoolteachers,
market research analysts,
sales reps and construction workers (particularly bricklayers and carpenters).
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
information security analysts,
big data analysts,
artificial intelligence and robotics specialists,
applications developers for mobile devices,
web developers,
database administrators,
business intelligence analysts,
gamification designers,
business/systems analysts and ethicists.
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Who can predict?
IT and CIO offices are going to
change
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• McKinsey estimates that we need globally 150,000 analysts
and another 50,000 managers who are talented in analytics by
end of 2018
• In April 2012, White House allocated $200MM for Big data
initiatives to fund leadership work in Big Data opportunities
• Reference: http://predictivemodels.blogspot.com/2013/06/the-famous-mckinsey-studyon-big-data.html
The New World Opportunity
Predictive Sciences and BI
Opportunity
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• SAP sponsored BIG data opportunity study by Sand-Hill Group and
Microsoft sponsored BIG data opportunity study by IDC both point
to enormous amount of investment and developments in BIG data
and commensurate revenue in the next 3-5 years
• $1.1 trillion revenue expected in the next three years due to BIG
data
• $40 Billion venture capital money flow and 1.3 million new jobs in
the next three years
• Predictive Analytics is also part of this opportunity
The New World Opportunity
The BIG Data Opportunity
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• http://www.mckinsey.com/insights/high_tech_telecoms_inter
net/the_internet_of_things
• Listen to the 11 minute audio discussion on the opportunities
All, tools, equipment, assets, and interactions are
interconnected and recorded
Internet of Things – McKinsey
Report
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Data, Data, Everywhere
However, you slice and
dice, big data and
analytics will be at
least $250 billion in the
next 13 years. Most of
which will have to be
automated work
The most certain of all these is data intelligence
and knowledge work
$33 Trillion Technology Payoff by 2025
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Top 10 Concepts, Ideas, Tools
1. Meaning of analytics, why it matters, and how different companies are using them (using
case studies)
2. Different stages of analytical competition and how to get your management's attention to
lead them to the next level
4. Understanding strategic metrics, and the critical components of bringing out Moneyball
phenomenon in your organization
5. Key Performance Indicators and Key Leverage Indicators and the relationship among Strategic
metric, KPIs, and KLIs, and how to create an engaging dashboard
6. Building an analytics team and how to integrate it within an organization
7. What is an information strategy and how to create one for your organization
Learning Goals
3. Identifying analytical methods for internal processes and external processes
8. Understand data management, data quality, and missing values for analytical processes
9. Four ways of collecting data and how to use sample surveys effectively and understanding
how bias needs to be addressed
10. Big data and Big data analytics
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Books and References
• Required:
• Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning.
Boston, MA: Harvard Business School.
• [ISBN-13: 978-1422103326]
• Recommended
Thomas Miller (2015), Modeling Techniques in Predictive Analytics with Python and R:
A Guide to Data Science, Pearson Publications, ISBN-13: 978-0-13-389211-6
• Franks, B. (2012). Taming the big data tidal wave: Finding opportunities in huge data
streams with advanced analytics. Hoboken, NJ: Wiley. [ISBN-13: 978-1-118-20878-6]
• Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or
die. Hoboken, NJ: Wiley. [ISBN-13: 978-0470465462]
•
• Moneyball movie - http://www.moneyball-movie.com/site/ This is available in our course library
for free viewing to collect your summary as the first assignment
• My blog: http://blog.crmportals.com/
References and tools
• Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland,
Calif.: Analytics Press. [ISBN-13: 978-0970601988]
• Groves, R.M., Fowler, F. Jr., Couper, M.P., Lepkowski, J.M., Singer E., & Tourangeau, R. (2009).
Survey methodology (2nd ed.). Hoboken, NJ: Wiley - [ISBN-13: 978-0470465462]
• Gert H. N. Laursen, Jesper Thorlund (2010), Business Analytics for Managers: Taking Business
Intelligence Beyond Reporting ISBN: 978-0-470-89061-5
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MSPA - Predict 402 Section 55 - Intro to Pred. Analytics and Data Collection
• Understanding strategic metric of an organization and KLI™ (Key
Leverage Indicators) and KPIs (Key Performance Indicators) to
leverage daily activities of an organization, using real life story of
Moneyball.
• Evaluating maturity levels of organizations on their analytical
maturity levels, using Davenport and Harris Model
• Identifying analytical methods for external and internal processes of
organizations
• Creating engaging dashboards that connects strategic metrics, KLIs,
and KPIs
• Creating a sample survey design proposal, along with a
questionnaire, analytical plan, and executive summary
• Third party data sets
• APAstyle of document preparation and Microsoft graphical objects
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
Tools, Data Assets, and
Analytical Strategies
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• (1) what drove the new business model, (2) what strategy was
developed in hiring and training under the new approach, (3)
how long it took to start seeing the results, (4) what team
dynamics started happening between analyst, scouts, and
management, and (5) your opinion of whether this real life
story bears truth in different industry verticals and whether
analytics can help redeem their company.
• Write approximately 3 to 5 sentences for each of the takeaway
points and do not write more than 2 pages
• Create the two page summary, use a title page in the spirit of
APA style, and name the file as
FirstName_LastName_402_WI2014_SECxx_MB.docx
The XX is your section number. For SECTION 55, it is 55, for example.
Submission date: Sunday, 12JAN14, 11: 55 PM
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
Moneyball Lessons – First Assignment
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• Review big data and big data analytics videos http://blog.crmportals.com/top-videos-on-learning-bigdata-science/
• Write a critical summary for a total of maximum of 5 pages.
• Create the summary using APAstyle (title, abstract, main body,
conclusion, references), name the file as
FirstName_LastName_402_WI2014_SECXX_BD.docx. XX
refers to your section number.
• Submit before 19JAN14, 11:55PM CST. 100 points
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
Big Data Review
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• Select two methodologies and write a maximum of 3 pages
on each method. Adding a small example will help clarify the
methods and cover the number of pages.
• Create the file using APAstyle, and name the file as
FirstName_LastName_402_WI2014_SECXX_IE.docx. XX
refers to your section number.
• Submit it before 26JAN14, 11:55PM CST
• This is not scored
Pred 402 Section 55 - Intro to Pred.
Analytics and Data Collection
Internal vs. External Analytical Process and Methods
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• Pick one of the application areas discussed in the book or an industry (vertical) that interests you
and identify 5 cases.
For each of the cases (1) profile the company detailing on where it is located, what is their core
competency, their products/services (2) how predictive analytics helped. Do not spend a lot of time
here. We are trying to get the useful list that interests you.
Demonstrate your ability to draw upon Northwestern University library resources by utilizing
relevant peer-reviewed articles from journals like the Harvard Business Review, MIT Sloan
Management Journal, and publications from SAS, Oracle, IBM, SAP where success stories are
published at their sites. Google "analytics" AND "success stories" AND "IBM", for example, to filter
your searches.
Preparatory works for Final Case Studies List
Predictive Analytics Applications and
Identifying Case Studies List
Total number of pages expected is around 5 not counting the title, and references. Make sure you
include the title, abstract, introduction, main body, conclusion, and references sections.
• The completed document is due before 2FEB14.
• This theme and case studies list will be used for the final case studies report. Additional details are
available in the assignment section details of "case studies final" for final paper submission that is
due on 2MAR14.
The name of the file to be submitted should be FirstName_LastName_402_WI2014_SECXX_CL.docx
(The system accepts only filenames with upto 50 characters). XX is your section number.
The name of the file to be submitted should be FirstName_LastName_402_WI2014_SECxx_CL.docx
(The system accepts only filenames with upto 50 characters). 50 Points
Submission date: Sunday, 2FEB14, 11: 55 PM
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• Use best practices for developing dashboard
• One screen, drill-down design, that captures and makes available
the current status (almost in real time) with insights for a number
of well understood KPIs/KLIs, and trending customer input on any
thing and everything about the company
• This is all about designing the dashboard; so you need some
sharp skills in Microsoft graphical objects and graphical outputs
as options
• The screen shot design and the executive summary together as
one document should be named
How to create an engaging and leveraging
dashboard
Third Assignment – Design a
Dashboard
FirstName_LastName_402_WI2014_SEC60_DB.docx. 100 points.
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Submission date: Sunday, 16FEB14, 11: 55 PM
• Submit Case Studies Final: Enhancing the previous
submissions with insights from MB,BD,IE write ups. Enhance
the whole write up on why they are success stories - cite
projects, analytical methods, results, and economic impacts
• Depth and width of the discussion is what is expected here
whereas in the first stage the attempt is to provide input to
organize your thoughts
• This may include interviews of the company executives but
references should be provided
• Case Study Collection Final Assignment is due Sunday,
2Mar14, at 11:55 p.m (CST) – 100 Points
• File name should be
FirstName_LastName_402_WI2014_SECXX_CF.docx
Maturity level of an organization in its class of
activities
Final Theme Based Case Studies Submission
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• Survey Design and Implementation Topic file name should be
FirstName_LastName_402_SU2014_SECxx_ST.docx Due
23FEB14
• This is to get an understanding and input from your lecturer
on how to complete your desired topic as a detailed proposal
• The final completed document, prepared in APA style for
Survey Design and Implementation should be maximum of 15
pages and the file name should be
FirstName_LastName_402_WI2014_SECxx_SF.docx. 150
Points
Submission date for sampling topic: Sunday, 23FEB14, 11: 55 PM
Submission date for final sampling proposal: Sunday, 9MAR14, 11: 55 PM
Developing a Survey Design and Implementation
Assignment: Survey
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Etiquettes of Interaction and benefitting by
active Engagement
Discussion Board
• The purpose of the discussion boards is to allow students to freely
exchange ideas.
• It is imperative to remain respectful of all viewpoints and positions and,
when necessary, agree to respectfully disagree.
• While active and frequent participation is encouraged, cluttering a
discussion board with inappropriate, irrelevant, or insignificant material
will not earn additional points and may result in receiving less than full
credit.
• Frequency is not unimportant, but content of the message is paramount.
Please remember to cite all sources—when relevant—in order to avoid
plagiarism.
• I will be looking for grammar and sentence construction for direct, active,
simple and respectful dialogues for complete scores
• 1 posting stating your position per guiding question is required to get full
10 points, for clarity and content.
• Always provide your view point as a separate start of a discussion for
every guiding question posted each week, and provide input to others or
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post questions to other people’s point of views subsequently.
Evaluation of Successful
Learning
Grading Scale
•
•
•
•
•
•
•
•
Big data Review(100 points)
Case Study List (50 pts.)
Sync Session
Dashboard and Executive Summary
(100 pts.)
Case Study Collection Final (100
pts.)
Sync Session
Survey Design and Implementation
(150 pts.)
Discussion Board Participation (100
pts., 10 pts. per session)
• Total Points: 600 pts.
• A = 93%–100%
• A- = 90%–92.9%
• B+ = 87%–89.9%
• B = 83%–86.9%
• B- = 80%–82.9%
• C+ = 77%–79.9%
Grading Method
Evaluation Method
• C = 73%–76.9%
• C- = 70%–72.9%
• F = 0%–69.9%
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Attendance and Participation
• This course will not meet at a particular time each week.
• All course goals, session learning objectives, and assessments
are supported through classroom elements that can be
accessed at any time.
• To measure class participation (or attendance), your
participation in threaded discussion boards is required,
graded, and paramount to your success in this class.
• Please note that any scheduled synchronous or “live”
meetings are considered supplemental and optional. While
your attendance is highly encouraged, it is not required and
you will not be graded on your attendance or participation.
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Late Work
• Unless otherwise noted, all work is due on the assigned day by
11:55 p.m. (central time). This includes exams and
participation in the discussions. Late work is not accepted.
• One more piece of advice—do not fall behind. We cover a lot
of material in this course, and falling behind is the primary
reason why folks fail. To that end, you have below the due
dates for the entire course. It is much, much better to be
ahead than behind.
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Discussion Board Evaluations
• Respond to you with in 48 hours in email; most of the times with in 24 hours
• A large of collection of office hours
• While I will read every one of your comments and responses in discussion
boards, to let your creativity in articulation, discussion, and interpretation, I
will respond to specific question posted to me either directly in a personal
email or in the discussion board, or when I see there are confusing or
inconsistent statements are posted in the discussions that do not get any
one else’s response
• Post evaluations of discussions before the following Sunday
• Make every effort to provide you an expanded and critical evaluation
needed for this course that would help you right away in your daily,
organizational, and professional work.
• I value your input from you all and it is my honor to expand the horizons of
your vistas in management of predictive analytics opportunities in concepts,
areas of applications, and data assets
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Utilizing Library Facilities
Ms. Qiana Johnson - Librarian
• Qiana Johnson is the Distance Learning Librarian at the
Northwestern University Libraries and the liaison to the School of
Continuing Studies. She has presented and published in a number of
areas including working with nontraditional graduate students and
library collections. At the end of tonight’s session, students will be
able to locate articles about companies and their strengths and/or
weaknesses in their use of analytics using library resources.
Listen to this pre-recorded version of her presentation. If the link
does not work, use the following to copy and paste in a newly opened
browser - http://nwuniversity.adobeconnect.com/p5mg1vo1zdi/
• Students are encouraged to contact Qiana at 312.503.6617, qjohnson@northwestern.edu, or through the IM widget at the
Predictive Analytics Research Guide page,
http://libguides.northwestern.edu/predictiveanalytics.
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