Data Analytics and Business IntelligenceDR. JORDAN SROUR

JORDAN.SROUR@LAU.EDU.LB

3 APRIL 2019

Activity Type Start Time (mm:ss) Distance (mi)Running Sun, Feb 15, 2015 8:35 AM 56:45:00 6.23Running Wed, Feb 11, 2015 5:59 PM 30:13:00 3.12Treadmill Running Tue, Feb 10, 2015 4:30 PM 27:00:00 3.11Running Sun, Feb 8, 2015 8:45 AM 41:31:00 4.54Track Running Thu, Feb 5, 2015 6:13 PM 9:50:00 1.01Track Running Thu, Feb 5, 2015 6:10 PM 1:43:00 0.27Track Running Thu, Feb 5, 2015 6:06 PM 1:47:00 0.24Track Running Thu, Feb 5, 2015 6:03 PM 1:43:00 0.27Track Running Thu, Feb 5, 2015 5:51 PM 10:09:00 1.04Track Running Tue, Feb 3, 2015 8:52 PM 21:21:00 2.09Track Running Mon, Feb 2, 2015 5:39 PM 21:00:00 2.31Street Running Sun, Feb 1, 2015 8:54 AM 36:34:00 4Running Thu, Jan 29, 2015 6:12 PM 29:19:00 3.11Running Tue, Jan 27, 2015 8:38 PM 9:03:00 1.01Running Mon, Jan 26, 2015 6:09 PM 27:18:00 3.11Running Sun, Jan 25, 2015 7:56 AM 30:28:00 3.15Running Wed, Jan 21, 2015 6:03 PM 29:05:00 3.12Running Mon, Jan 19, 2015 6:12 PM 28:45:00 3.11Running Sun, Jan 18, 2015 9:18 AM 28:32:00 3.12Running Tue, Jan 13, 2015 6:00 PM 28:38:00 3.11

Activity Type Start Time (mm:ss) Distance (mi) Distance (km)Running Sun, Feb 15, 2015 8:35 AM 56:45:00 6.23 10.03Running Wed, Feb 11, 2015 5:59 PM 30:13:00 3.12 5.02Treadmill Running Tue, Feb 10, 2015 4:30 PM 27:00:00 3.11 5.01Running Sun, Feb 8, 2015 8:45 AM 41:31:00 4.54 7.31Track Running Thu, Feb 5, 2015 6:13 PM 9:50:00 1.01 1.63Track Running Thu, Feb 5, 2015 6:10 PM 1:43:00 0.27 0.43Track Running Thu, Feb 5, 2015 6:06 PM 1:47:00 0.24 0.39Track Running Thu, Feb 5, 2015 6:03 PM 1:43:00 0.27 0.43Track Running Thu, Feb 5, 2015 5:51 PM 10:09:00 1.04 1.67Track Running Tue, Feb 3, 2015 8:52 PM 21:21:00 2.09 3.36Track Running Mon, Feb 2, 2015 5:39 PM 21:00:00 2.31 3.72Street Running Sun, Feb 1, 2015 8:54 AM 36:34:00 4 6.44Running Thu, Jan 29, 2015 6:12 PM 29:19:00 3.11 5.01Running Tue, Jan 27, 2015 8:38 PM 9:03:00 1.01 1.63Running Mon, Jan 26, 2015 6:09 PM 27:18:00 3.11 5.01Running Sun, Jan 25, 2015 7:56 AM 30:28:00 3.15 5.07Running Wed, Jan 21, 2015 6:03 PM 29:05:00 3.12 5.02Running Mon, Jan 19, 2015 6:12 PM 28:45:00 3.11 5.01Running Sun, Jan 18, 2015 9:18 AM 28:32:00 3.12 5.02Running Tue, Jan 13, 2015 6:00 PM 28:38:00 3.11 5.01

Activity Type Start Time (mm:ss) Distance (mi) Distance (km)Running Sun, Feb 15, 2015 8:35 AM 56:45:00 6.23 10.03Running Wed, Feb 11, 2015 5:59 PM 30:13:00 3.12 5.02Treadmill Running Tue, Feb 10, 2015 4:30 PM 27:00:00 3.11 5.01Running Sun, Feb 8, 2015 8:45 AM 41:31:00 4.54 7.31Track Running Thu, Feb 5, 2015 5:51 PM 25:12:00 2.83 4.55Track Running Tue, Feb 3, 2015 8:52 PM 21:21:00 2.09 3.36Track Running Mon, Feb 2, 2015 5:39 PM 21:00:00 2.31 3.72Street Running Sun, Feb 1, 2015 8:54 AM 36:34:00 4 6.44Running Thu, Jan 29, 2015 6:12 PM 29:19:00 3.11 5.01Running Tue, Jan 27, 2015 8:38 PM 9:03:00 1.01 1.63Running Mon, Jan 26, 2015 6:09 PM 27:18:00 3.11 5.01Running Sun, Jan 25, 2015 7:56 AM 30:28:00 3.15 5.07Running Wed, Jan 21, 2015 6:03 PM 29:05:00 3.12 5.02Running Mon, Jan 19, 2015 6:12 PM 28:45:00 3.11 5.01Running Sun, Jan 18, 2015 9:18 AM 28:32:00 3.12 5.02Running Tue, Jan 13, 2015 6:00 PM 28:38:00 3.11 5.01

Each additional km will add 5:35 to my run.

Insight 1: The longer I run, the longer I run.

KM Time

1 05:03.52 05:32.63 06:14.54 05:28.15 05:23.96 05:26.77 05:53.68 05:56.49 06:01.010 05:39.1

Summary 56:45:00

Whoa! What’s going on here?

KM Time Elev Gain Elev Loss

1 05:03.5 -- 502 05:32.6 32 283 06:14.5 103 424 05:28.1 65 395 05:23.9 47 726 05:26.7 36 997 05:53.6 23 258 05:56.4 37 --9 06:01.0 -- 4110 05:39.1 35 --

Summary 56:45:00 378 396Insight 2: I’m slower when

running up hills.

Start Distance (km) Time (mm:ss) Pace (min/km)Tue, Feb 10, 2015 4:30 PM 5.01 27:00:00 5:23:40Mon, Jan 26, 2015 6:09 PM 5.01 27:18:00 5:27:16Sun, Jan 18, 2015 9:18 AM 5.02 28:32:00 5:40:58Tue, Jan 13, 2015 6:00 PM 5.01 28:38:00 5:43:15Mon, Jan 19, 2015 6:12 PM 5.01 28:45:00 5:44:39Wed, Jan 21, 2015 6:03 PM 5.02 29:05:00 5:47:32Thu, Jan 29, 2015 6:12 PM 5.01 29:19:00 5:51:27Sun, Jan 25, 2015 7:56 AM 5.07 30:28:00 6:00:36Wed, Feb 11, 2015 5:59 PM 5.02 30:13:00 6:01:04

Start Distance (km) Time (mm:ss) Pace (min/km)Elevation Gain (ft)

Elevation Loss (ft)

Tue, Feb 10, 2015 4:30 PM 5.01 27:00:00 5:23:40 0 0Mon, Jan 26, 2015 6:09 PM 5.01 27:18:00 5:27:16 0 0Sun, Jan 18, 2015 9:18 AM 5.02 28:32:00 5:40:58 229 228Tue, Jan 13, 2015 6:00 PM 5.01 28:38:00 5:43:15 0 0Mon, Jan 19, 2015 6:12 PM 5.01 28:45:00 5:44:39 0 0Wed, Jan 21, 2015 6:03 PM 5.02 29:05:00 5:47:32 0 0Thu, Jan 29, 2015 6:12 PM 5.01 29:19:00 5:51:27 0 0Sun, Jan 25, 2015 7:56 AM 5.07 30:28:00 6:00:36 270 206Wed, Feb 11, 2015 5:59 PM 5.02 30:13:00 6:01:04 307 323

Insight 3: Confirming Insight 2 with other data may indicate that I have gained some insight.

Is this an example of data analytics?It includes data and analysis, so it must be data analytics, right?

Wrong!

Data analytics is a process by which important questions are framed and answered through the discovery, interpretation, and communication of meaningful patterns in data.

Framing a Question for Analytics

What is the response time for ambulances in our community?

How will we define this? From the time the call comes in to the time the ambulance arrives at the patient? Or from the time the call is answered until the ambulance arrives at the patient?

Do we want an average? Over what period of time? Does is matter if the emergency is at night or during the day? What about the type of the emergency?

Is this just a neighborhood? A city? A region including several cities?

Just ambulances? Or other patient transport vehicles as well?

Being as specific as possible when framing the question will help in identifying the source, structure, and quantity of data needed.

Once we have a well framed question and a clean dataset, what next?

From: http://www.gurobi.com/resources/prescriptive-analytics

Descriptive AnalyticsExample Questions:◦ What is the response time for ambulances in an

urban environment? (Pre-Hospital Care Metrics as Reflected in Lebanese Red Cross Ambulance Response Data, Fourth Quarter, 2016, F. Jordan Srour, Nabih Jabr, 2017 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2915386)

◦ Which countries have the highest population growth? (https://population.un.org/wpp/)

◦ Where do more people live – urban or rural environments? (https://population.un.org/wpp/)

◦ What occupations do people in rural communities hold? (https://www.ilo.org/global/research/lang--en/index.htm)

A word about dashboards…

More on visualizations…

Predictive AnalyticsExample Questions:◦ When a new call comes to an emergency dispatch center, what

is the expected response time for that call? (Ambulance Emergency Response Optimization in Developing Countries, Justin J. Boutilier, Timothy C.Y. Chan, 2018, https://arxiv.org/abs/1801.05402)

◦ What is the likelihood that someone will drop out of school?(Keeping Children in School: Effects of Household and Context Characteristics on School Dropout in 363 Districts of 30 Developing Countries, Janine Huisman, Jeroen Smits, 2015, https://doi.org/10.1177%2F2158244015609666)

◦ What is the likelihood of somebody being exposed to/contracting a certain virus? (Future directions in analytics for infectious disease intelligence: Toward an integrated warning system for emerging pathogens, Barbara A Han, John M Drake, 2016, http://embor.embopress.org/content/17/6/785.full)

One Predictive Analytics StrategyGather a set of data that includes predictors and a response variable.Build a model using ~70% of that data.Test the model on the remaining ~30% of the data.Model building strategies fall in the domain of machine learning

https://vas3k.com/blog/machine_learning/

https://christophm.github.io/interpretable-ml-book/

Prescriptive AnalyticsExample Questions:◦ When a new call comes to an emergency dispatch center, which ambulance

should be assigned to the emergency? When there is idle time in an emergency response system, what is the best location to position an ambulance? (Ambulance Emergency Response Optimization in Developing Countries, Justin J. Boutilier, Timothy C.Y. Chan, 2018, https://arxiv.org/abs/1801.05402)

◦ What allocation of resources will have the highest impact in soliciting donations for charity? (Harvest Hope Food Bank Optimizes Its Promotional Strategy to Raise Donations Using Integer Programming, Sanjay L. Ahire, Pelin Pekgün, 2018, https://doi.org/10.1287/inte.2018.0944)

◦ Where should humanitarian relief goods be stored to ensure the fastest response to a humanitarian disaster? (Pre-Positioning of Emergency Items for CARE International, Serhan Duran, Marco A. Gutierrez, Pinar Keskinocak, 2011, https://pubsonline.informs.org/doi/abs/10.1287/inte.1100.0526)

Prescriptive Analytics StrategyPrescriptive analytics strategies are highly domain specific.Includes optimization models (linear, non-linear, integer, or stochastic programming), heuristics, matheuristics, metaheuristics, rule-based schemes, etc

Some Interesting ReadsWeapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’NeilPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric SiegelBusiness unIntelligence: Insight and Innovation beyond Analytics and Big Data by Dr. Barry Devlin