Data Analytics for Managers

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        <span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Data Analytics for Managers</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="http://www.datapolitan.com" property="cc:attributionName" rel="cc:attributionURL">Richard Dunks</a> and <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.tinypanther.com" property="cc:attributionName" rel="cc:attributionURL">Julia Marden</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative-Commons-License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" /></a>
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![img-center-50](images/datapolitan.png)

# Data Analytics for Managers

- - -

## Instructors: Elizabeth DiLuzio and Elana Shneyer

### Follow along at: http://bit.ly/data-driven-gov

#### See the code at: http://bit.ly/data-driven-gov-code

---

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# Welcome

???
+ Facilitators introduce themselves
+ Facilitators (respectfully) assert authority to be teaching material
+ Facilitators begin creating a safe, comfortable container for participants

---

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# Data Driven Culture
![img-center-95](images/ddc_compass.png)

???
+ Facilitators briefly introduce the idea that data analysis is a team sport
+ Facilitators connect this idea to the collaborative introduction exercise that follows

---

# A Few Ground Rules
--

+ Step up, step back
--

+ Be curious and ask questions!
--

+ Assume noble regard and positive intent
--

+ Respect multiple perspectives 
--

+ Listen deeply
--

+ Be present (phone, email, social media, etc.)

---

# Introduce Yourself to Your Neighbor
+ Who are you?
+ Where do you work?
+ What has been the proudest moment in your job?

???
+ Participants engage in a collaborative introduction exercise to engage their fellow participants and begin the day talking
+ Participants relax into the container created by the facilitators

---

# What to Expect Today
+ 9:40 – Data Analytics 101
+ 10:00 - Introduction to Problem Ideation
+ 10:30 – 15 min break
+ 10:45 – Process Mapping and the Types of Analysis
+ 12:00 – Lunch
+ 1:00 – Overview of Open Data
+ 2:00 – Data Analytics Exercise
+ 2:30 – 15 min break
+ 2:45 – Data Analytics Exercise (continued)
+ 4:30 – Dismissal

???
+ Outline of activities for participants awareness

---

# Goals for This Class
--

+ Explore the elements of a data-driven culture
--

+ Learn about different types of analysis
--

+ Practice mapping out the analytics process
--

+ Get hands-on practice with NYC data
--

+ Have fun

---

# Housekeeping
--

+ We’ll have one 15 minute break in the morning
--

+ We’ll have an hour for lunch
--

+ We’ll have a 15 minute break in the afternoon
--

+ Class will start promptly after breaks
--

+ Feel free to use the bathroom if you need during class
--

+ Please take any phone conversations into the hall to not disrupt the class

???
+ Facilitators set expectations for participants

---

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# Let's talk data.

???
+ Facilitator asks participants what types of data they work with

---

# The Value of Data
--

+ Data tells a story about something that's happened
--

+ Can describe what happened directly or indirectly
--

![img-center-90](images/311data_rev.png)

???
+ Facilitators prompt students with the question "Why is data useful?" and/or "Why do we use it?"
+ Facilitators contrast this with the question "How would we make decisions without data?" to tease out the need for data to check assumptions and limited problem awareness
+ Facilitators carefully acknowledge that intuition, experience, and anecdotes can be useful but shouldn't be the sole source of knowledge and information
+ Reference: [Sensing Noise in NYC](https://www.nytimes.com/2016/11/07/nyregion/to-create-a-quieter-city-theyre-recording-the-sounds-of-new-york.html?_r=0)

---

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# Are All Data Points Created Equal?
???
+ Facilitators lead a brief discussion of the issues with data
+ Issues to be touched on include: data entry errors, biases in data, data cleaning errors, etc.
---

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> Facts do not "speak for themselves." They speak for or against competing theories. Facts divorced from theory or visions are mere isolated curiosities.

##-Thomas Sowell *A Conflict of Visions*

???
+ Facilitators develop the theme of data skepticism by talking about the importance of context to understanding numbers provided in analysis
+ An example I often use is "What if I told you there's a 2% drop in crime? What do we need to know to make that number useful?"
+ Facilitator emphasizes that the goal of analysis is telling a true and compelling story, and communicating context whenever possible

---

# Data Driven Decisions Require Humans
--

![img-center-50](images/iphone_intel.jpg)

.caption[Image Credit: 100 lion, [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File%3AIphone-62.jpg)]

## [How Intel lost out on the contract of a lifetime](http://appleinsider.com/articles/13/05/16/intel-chips-could-have-powered-first-iphone-ceo-otelini-says)

???
+ Facilitators introduce story of data-driven gone awry to develop a healthy skepticism about data-driven

---

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# Data Analysis Should Drive Decision Making
#  
##

---

# Data Analysis Should Drive Decision Making

![img-center-80](images/datadriven.png)
.caption[Image Credit: Data Driven School (https://www.slideshare.net/moshiiit/data-driven-school-mis-case-study)]

---

>“We move from data to information to knowledge to wisdom. And separating one from the other… knowing the limitations and the danger of exercising one without the others, while respecting each category of intelligence, is generally what serious education is about.”

## - Toni Morrison, author [The Source of Self-Regard](https://www.powells.com/book/-9780525521037?partnerID=44711)

---

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# Data Analysis Should Drive Decision Making
# This is what it means to be "data driven"
##

---

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# Data Analysis Should Drive Decision Making
# This is what it means to be "data driven"
## And good analysis should lead to good decisions

---

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# Being data-driven doesn't mean blindly following data

![img-center-80](images/blindlyfollowdata.jpg)

.caption[Image Credit: zhouxuan12345678, [CC BY-SA 2.0](https://creativecommons.org/licenses/by/2.0/), via [Flickr](https://www.flickr.com/photos/53921113@NO2/5453212152)]

???
+ Facilitator elaborates on the theme of data skepticism with the example of a GPS leading a driver into a pond or almost off a cliff to emphasize the need to keep awareness of context

---

> Data is only as valuable as the decisions it enables.

## -[Ion Stoica](https://twitter.com/databricks/status/810190036624875520)

???
+ Facilitators emphasize data as a strategic resource for managers

---

# What is Analysis?
--

> “Analysis is simply the pursuit of understanding, usually through detailed inspection or comparison”

## - [Carter Hewgley](https://www.linkedin.com/in/carterhewgley), Senior Advisor for Family & Homeless Services, Department of Human Services, District of Columbia

---

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# What Analysis Isn't

---
# Writing a report

![img-center-45](images/datadriven_reports.png)
.caption[Image Credit: RRZEicons, [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Report.svg)]

---

# Creating a dashboard

![img-center-60](images/datadriven_dashboard.png)
.caption[Image Credit: Chabe01, [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File%3ADashboard_OS_X.svg)]

---

# Generating an alert

![img-center-60](images/datadriven_alert.png)
.caption[Image Credit: Tokyoship, [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File%3ASimple_Alert.svg)]

---
name:avp
# It's Putting Them All Together

![img-center-80](images/valuechain.png)

---
name:brainstorm
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> "If you do not know how to ask the right question, you discover nothing."

## - W. Edward Deming

---

# Our Method For Generating Ideas
--

![img-right-30](images/ideate.png)
+ **Ideate** - On your own, generate at least 3 ideas (ideally more), each on their own Post-It Note

####  
![img-center-30](images/postit_ex.jpg)
---

# Our Method For Generating Ideas
![img-right-30](images/discuss.png)
+ **Ideate** - On your own, generate at least 3 ideas (ideally more), each on their own Post-It Note
+ **Discuss** - Review the ideas generated

---

# Our Method For Generating Ideas
![img-right-30](images/ideate_discuss_decide_crop.png)
+ **Ideate** - On your own, generate at least 3 ideas (ideally more), each on their own Post-It Note
+ **Discuss** - Review the ideas generated
+ **Decide** - Come to a consensus as a group

---

# Exercise - Reducing Noise Complaints in NYC

+ Between 1 Jan and 31 Mar 2018, there were an average of 943 noise-related 311 service requests a day
--

+ The same period in 2019 had an average of 992 noise-related 311 service requests a day
--

+ You've been tasked with decreasing noise complaints in the city
--

+ .red[What questions would you ask to kick off a data analysis?]
--

+ We will ideate using a [timed](https://www.google.com/search?q=timer&rlz=1C1GCEU_enUS852US852&oq=timer&aqs=chrome..69i57j0l5.519j0j7&sourceid=chrome&ie=UTF-8) 5-5-5 structure

???

+ A facilitated group ideation exercise to get them thinking about key questions and problems
+ Task to participants: (1) take 5 minutes to come up with your own questions and be prepared to share with the group (2) take 5 minutes and discuss with your partner (3) discuss the questions as a group
+ Facilitator helps scope an analytical question that will be developed throughout the morning lecture session
+ Group should have written down a series of questions to be discussed

---

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# What do you think?

???
+ Facilitator debriefs with class about what they think about the process and its benefits

---

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# 15 MIN BREAK

![img-center-45](https://imgs.xkcd.com/comics/fixing_problems.png)
[Source](https://xkcd.com/1739/)

---

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# Process Mapping (Our method)

![img-center-50](images/pm_ex.jpg)

---

# Process Mapping
--

+ Allows you to identify and strategize for key steps in your analysis
--

+ Helps sequence tasks and identify gaps in understanding
--

+ Provides a basis for documenting work
---

# Process Mapping

![img-center-100](images/Makebreakfast.gif)
.caption[By Scottsm1991 (Own work) [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0), via Wikimedia Commons]

???
+ Facilitator does the "I do" phase by describing a past project that was mapped out

---

exclude:true
# Process Map

![img-center-100](images/process_map_ex.jpg)
.caption[Analyzing pet licensing compliance in San Jose. Image Credit: Datapolitan [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)]

---

# How to Create a Process Map
--

![img-right-30](images/ideate_discuss_decide_crop.png)
+ Identify the key challenge
--

+ Identify the outcome
--

+ Identify key way to validate the outcome (outputs)<br>
--
_**How do we know we've got it right?**_

---

# Outcomes vs Outputs
--

+ Outcomes are the larger benefits and/or achievements you're trying to realize<br>
--
 (happiness, health, well-being, etc.)
--

+ Outputs are the tangible parts of your outcome<br>
--
 (survey responses, measured results, etc.)
--

+ Outputs enable us to find outcomes
--

+ Without outcomes, there is no need for outputs

---

# How to Create a Process Map
![img-right-30](images/ideate_discuss_decide_crop.png)
+ Identify the key challenge
+ Identify the outcome
+ Identify key way to validate the outcome (outputs)<br>_**How do we know we've got it right?**_
--

+ Identify the key inputs (data, partners, etc.)
--

+ Sequence the key questions to turn inputs into outputs

---

# Key Tips
![img-right-30](images/ideate_discuss_decide_crop.png)
+ Place each step on a Post-It Note

####  
![img-center-30](images/postit_ex.jpg)

---

# Key Tips
![img-right-30](images/ideate_discuss_decide_crop.png)
+ Place each step on a Post-It Note
+ Order and reorder as necessary
--

+ Some steps will need to be broken down

???
+ Facilitator identifies the key parts of the process based on the graphic presented
+ Faciliator reminds participants to follow the process that works best for them

---

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# Building the Best Banana Split

![img-right-30](images/banana_split.png)
+ Identify the outcome
+ Identify the outputs
+ Identify the key inputs
+ Identify key steps to use the inputs to achieve the outcome

![img-center-45](images/ideate_discuss_decide_horiz_crop.png)

####  
.caption[Top <a href="http://creativecommons.org/licenses/by-sa/3.0/" title="Creative Commons Attribution-Share Alike 3.0">CC BY-SA 3.0</a>, <a href="https://commons.wikimedia.org/w/index.php?curid=964364">Link</a>; Middle by <a href="https://en.wikipedia.org/wiki/User:2candle" class="extiw" title="wikipedia:User:2candle">2candle</a> at <a href="https://en.wikipedia.org/wiki/" class="extiw" title="wikipedia:">English Wikipedia</a>, <a href="http://creativecommons.org/licenses/by-sa/3.0/" title="Creative Commons Attribution-Share Alike 3.0">CC BY-SA 3.0</a>, <a href="https://commons.wikimedia.org/w/index.php?curid=6698038">Link</a>; Bottom by [Flickr user Kristin Ausk](https://www.flickr.com/photos/kristinausk/), [CC BY-SA 2.0](https://creativecommons.org/licenses/by-sa/2.0/), [Link](https://www.flickr.com/photos/kristinausk/6830445704/in/photolist-bpzPxf-nBDSBJ-nkbey1-awHNhS-nBDRNa-nBrAVL-nDs5g6-nkaBE4-nzCd8L-d5iYiE-TopM85-fFNjqk-nBn71t-nDrddF-7Up5jm-9z2RTb-cWbBBE-cWbwoS-956Wb4-2ghxXH-7KHKAH-fDiXMm-2gmZ7Q-nDra2F-nBn8bz-nkbhjj-nBEWaV-nBnXSc-gmDdW2-7KMHC3-cWbCm9-Sdbg5R-ig33dG-cWbEbq-8PHvG6-cWbqdY-dfNyUw-cWbADG-8dBA6U-8gGzb3-9f99Mr-cWbDoC-51uMvp-cWbuZA-obXW3z-51uMoF-51uNy4-ebAhe-51z23U-51uN5K)]

???
+ A simple "We do" example to get the participants started with the process
+ Participants verbalize with facilitator the steps as necessary to achieve the result
+ Facilitator models the steps where necessary to demonstrate the desired results

---

# Reducing Noise Complaints
![img-right-25](images/ideate_discuss_decide_crop.png)
--

+ Identify the key question/challenge
--

+ Identify the key outcome
--

+ Identify the outcome measures
--

+ Identify the key inputs (data, partners, etc.)
--

+ Sequence the key questions to turn inputs into outputs

![img-center-25](images/unnecessary_noise.jpg)

.caption[[Image](https://www.flickr.com/photos/jackdorsey/272873611/) by [Flickr user Jack Dorsey](https://www.flickr.com/photos/jackdorsey/) [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/)]
???
+ The more involved "We do" phase where we do this together

---

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# Value of Good Operational Analysis

![img-center-55](images/cincinnatilatefees.jpg)
.caption[Image Credit: Money by Andrew Magill, [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/), via Flickr]

## City of Cincinnati saved $130,000 in late fees to utility company
[Learn more](http://www.charlotteobserver.com/news/business/article32617293.html)

---

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# Tracking Hurricane Sandy Relief Funds

![img-center-70](images/sandytracker.png)
.caption[Image Credit: The City of New York, via [NYC.Gov](http://www1.nyc.gov/sandytracker/#2891665754)]

---
exclude:true
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# Discussion: What kind of analysis does your office do?

---

# What did you notice about this process?
--

+ Knowing the problem and sequencing the steps can be harder than working with data
--

+ Having these answers can make the analysis much easier
--

+ This is a process you can do with any challenge, no matter how big
--

+ Everyone has something to add, no matter how technical (or non-technical) they are

???
+ Facilitator reflects with participants on the process mapping exercise
---

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# WRAP-UP

---
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# LUNCH
![img-center-55](https://imgs.xkcd.com/comics/90s_flowchart.png)

[Source](https://xkcd.com/210/)
---

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# WELCOME BACK!

---

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# MTA Improves Purchase Options

![img-center-75](images/mtacard.jpg)
.caption[Image Credit: Richard Dunks (own work), [CC BY-SA 4.0 International](http://creativecommons.org/licenses/by-sa/4.0/)]

Source: http://iquantny.tumblr.com/post/109078777129/2230-might-just-be-the-new-1905-for-metrocards

---
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# 6 Analytical Steps
![img-center-90](images/sixphases_1.jpg)

---

exclude:true
# 1. Problem Formulation
+ What question or need am I trying to answer?
+ What’s my organization’s mission and goals?
+ How can I best apply data to this task?

---

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# 2. Data Gathering/Preliminary Analysis
+ What data do I think I’m going to need?
+ What condition is it in?
+ Does it tell me what I need?
+ What other data might I need?
+ How much work do I need to put into the data?

---

exclude:true

# 3. Data Cleaning
+ Make the data usable and compatible
+ Takes up the most amount of time
+ May require more sophisticated tools depending on the state and size of the data

---

exclude:true

# 4. Hypothesis Testing
+ Am I getting the results I’d hoped for?
+ What other questions come up?

---

exclude:true

# 5. Verification
+ Do my results make sense?
+ Did I make a simple mistake?
+ Check twice and you’ll sleep easier

---

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# Spreadsheet Errors Have Consequences

![img-center-80](images/athensprotest.jpg)
.caption[Image Credit: [http://underclassrising.net](https://www.flickr.com/photos/0742/4163565280), [CC BY-SA 2.0](https://creativecommons.org/licenses/by-sa/2.0/)]

---

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# To Err is Human

> Since 1995, 88% of the 113 spreadsheets audited in 7 studies ... have contained errors.

## - Raymond Panko, "[What We Know About Spreadsheet Errors](http://panko.shidler.hawaii.edu/My%20Publications/Whatknow.htm)"

---
exclude:true

# 6. Visualization
+ “A picture is worth a thousand words”
+ Communicate results clearly and concisely
+ Help to better understand your data
+ The eyes have a much higher bandwidth into the brain

---

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# 6 Analytical Steps
![img-center-90](images/sixphases_1.jpg)

---

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# Link to NYC Open Data Portal for Exercise
## <a href="https://opendata.cityofnewyork.us/" target="_blank">NYC Open Data Portal</a>

---

# Data for Exercise
![img-center-100](images/od_exercise.png)

#### Click to download if you have problems: [2018 Q1 data](data/2018_311_Noise_Complaints.xlsx) and [2019 Q1 data](data/2019_311_Noise_Complaints.xlsx)

---

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![img-center-100](images/human_pivot.png)

---

# 5 Data Analytics Tasks
--

1. Sorting
--

2. Filtering
--

3. Aggregating (PivotTable)
--

4. Manipulating
--

5. Visualizing

---

# 1. Sorting
--

+ Reorganize rows in a dataset based on the values in a column
--

+ Can sort on multiple columns

---

# Sorting by Date

![img-center-100](images/sort_overview.png)
--

![img-center-100](images/sort1.png)

---

# Sorting by Date
![img-center-65](images/sort4a.png)

![img-center-65](images/sort4b.png)

---

# Sorting by Date
![img-center-60](images/sort4.png)

![img-center-60](images/sort_final.png)

---

# 2. Filtering
--

+ Only show rows that contain some value
--

+ Can filter by multiple values
--

+ Can filter by values in multiple columns

---

# Filtering by Agency Name
![img-center-100](images/filtering1.png)

---

# Filtering by Agency Name
![img-center-40](images/filtering2.png)

---

# 3. Aggregating Data 
--

+ Trends only become clear in aggregate
--

+ Often where you discover the "so what"
--

+ Aggregating data meaningfully can be tricky

---
# PivotTables
--

+ A data summarization tool
--

+ Useful to quickly understand data
--

+ Can use to graph data totals
--

![img-center-100](images/pt1.png)

---

# Creating a PivotTable
![img-center-60](images/pt2.png)
--

+ Should default to all your data .red[unless you have any cells selected]
--

+ Should default to a new worksheet

---

# Creating a PivotTable
![img-right-40](images/pt3.png)
## Drag and drop fields to visualize
+ Row labels 
+ Values
+ Filter
+ Column Labels

---

# Creating a PivotTable
![img-center-100](images/pt4.png)

---

# 4. Manipulating Data (In a good way...)
--

+ Sometimes available categories don't make sense
--

+ Values may not be in the format you need (or have mistakes)
--

+ You always want to have a clean copy of the data to go back to
--

+ Best to keep track of what you've done

---

# Extracting Hour From Timestamp
--

![img-center-100](images/hour1.png)

---
class:middle,center

[![img-center-100](images/hour_def_box.png)](https://support.office.com/en-us/article/HOUR-function-e2833b50-0db0-499b-acc5-e9ae03de8fbb)

---

# Extracting Hour From Timestamp

![img-center-100](images/hour2.png)

---

# Extracting Hour From Timestamp

![img-center-100](images/hour3.png)

---

# Extracting Hour From Timestamp

![img-center-100](images/hour4.png)

---

# Extracting Hour From Timestamp
![img-center-100](images/hour5.png)

---

# Extracting Hour From Timestamp
![img-center-100](images/hour6.png)

---

# Extracting Hour From Timestamp
![img-center-100](images/hour7.png)

---

# 5. Visualizing Data
--

+ Quickly communicate information
--

+ Tell a clearer story
--

+ A picture is worth a thousands words

---
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# When Are Noise Complaints Received?
![img-center-90](images/311data_rev.png)

---

# Charting Noise Complaints by Hour
![img-right-60](images/hour_chart1.png)
+ Refresh PivotTable

---

# Charting Noise Complaints by Hour
![img-right-40](images/hour_chart2.png)
+ Refresh PivotTable
+ Find `Hour` and add it to "Rows"

---

# Charting Noise Complaints by Hour
+ Refresh PivotTable
+ Find `Hour` and add it to "Rows"
+ Style the chart

![img-center-80](images/311data_rev.png)

---

exclude:true
# Finding Context

![img-center-80](images/context.png)

---

# Exploratory Data Analysis
--

+ Goal -> Discover patterns in the data
--

+ Understand the context
--

+ Summarize fields
--

+ Use graphical representations of the data
--

+ Explore outliers

####Tukey, J.W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley

---

# Exploring the Data
???
+ Facilitator releases participants to explore the data on their own
+ Facilitator helps participants with any issues analyzing the data
+ Participants practice presentation skills and articulating their analysis to a group
+ Facilitators emphasize telling a story with data
--

+ Take a moment to find something interesting in the data
--

+ Work in pairs, groups, or on your own
--

+ Be prepared to share what you found and the steps you took to find it
--

+ We'll be around to help

---

class:middle,center
# Gallery Walk
![img-center-45](images/collab.jpg)

---

class:middle,center
# 15 MIN BREAK
![img-center-95](https://imgs.xkcd.com/comics/self_description.png)
[Source](https://xkcd.com/688/)

---

name:nola
class:center,middle
# Types of Analysis
[![img-center-90](images/nolytics_header.png)](https://datadriven.nola.gov/home/)
[![img-center-90](images/nolytics_footer.png)](https://datadriven.nola.gov/home/)

Based on the work of the [City of New Orleans, Office of Performance and Accountability](https://datadriven.nola.gov/home/)

???
+ Facilitator goes through the types of analysis with participants to introduce key ideas
+ Facilitator connects each type to either the process map they've created or to the their own work

---

class:center,middle
![img-center-90](images/nolytics_type_stacked.png "Copyright: Harvard University Ash Center https://ash.harvard.edu and used with permission")

---

# Finding a Needle in Haystack
![img-right-15](images/needle1_rev.png)
--

+ Challenge: Services target a small number of individuals (either those at high-risk, or most likely to be non-compliers) — and locating them is challenging. 
--

+ Opportunity: Predictive modeling helps to identify those who are likely to need services based on existing data

---

# New Orleans Distributes Smoke Alarms

![img-center-40](images/neworleansfire.jpeg)
.caption[Image Credit: Michael Barnett [CC BY-SA 2.5](http://creativecommons.org/licenses/by-sa/2.5), via Wikimedia Commons]

---

# Targeted Outreach Saves Lives

![img-center-90](images/nolasmokealarm.png)
.caption[Image Credit: City of New Orleans, via [nola.gov](http://nola.gov/performance-and-accountability/nolalytics/files/full-report-on-analytics-informed-smoke-alarm-outr/)]

---

# Targeted Outreach Saves Lives

![img-center-90](images/nolaimpact.png)
.caption[Image Credit: City of New Orleans, via [nola.gov](http://nola.gov/performance-and-accountability/nolalytics/files/full-report-on-analytics-informed-smoke-alarm-outr/)]

---

# Prioritizing Work for Impact
![img-right-15](images/prior1.png)
--

+ Challenge: Work is currently assigned un-strategically and without regard for the complexity or urgency of a case. Instead, it's on the basis of first-come-first-served, randomly, or based on constituent complaints.
--

+ Opportunity: Data analyses that compare information about current cases to the information and severity of previous cases helps to instantly prioritize services.
---

# NYC Restaurant Inspectors Save Time

![img-center-65](images/greasetrap_csessums.jpg)

.caption[Image Credit: Grease Trap Cover Asphalt by [Christopher Sessums](https://www.flickr.com/photos/csessums/4740455231), [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/)]

.center[Source: http://www.nyc.gov/html/dep/html/press_releases/12-71pr.shtml#.W6v2nRNKhZo]

---

# Early Warning Tools
![img-right-15](images/early1.png)
--

+ Challenge: Resources are overly focused on reactive instead of preventive services; service providers are often surprised by spikes in need and have difficultly responding
--

+ Opportunity: Developing tools to predict need based on historic patterns

---

# Using Data to Fight Fires

![img-center-90](images/fires.png)
.caption[Image Credit: [NYC Mayor's Office of Data Analytics](http://www1.nyc.gov/site/analytics/index.page), [2013 Annual Report](https://assets.documentcloud.org/documents/1173791/moda-annual-report-2013.pdf)]

---

# Better, Quicker Decisions
![img-right-15](images/decisions1.png)
--

+ Challenge: Decisions are made that require significant judgement and could use more structure for the decision-making. Instead, resources are at risk of delay or mis-deployment because decision-makers do not have important information in a usable format.
--

+ Opportunity: Developing tools for collecting only relevant information and creating recommendation tools for operational decisions

---

# Cincinnati Targets Urban Blight

![img-center-40](images/cincinnatiurbanblight.jpg)
.caption[Image Credit: Wholtone, via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Abandoned-Over-the-Rhine-building.jpg)]

[Learn more](https://dssg.uchicago.edu/2015/08/20/blight-prevention-building-strong-and-healthy-neighborhoods-in-cincinnati/)

---

# Optimizing Resource Allocation
![img-right-15](images/optimize1.png)
--

+ Challenge: Department resources have been scheduled or deployed in the same way for a long time, despite changing patterns of need.
--

+ Opportunity: Use data to drive decisions on the deployment of resources

---

# Shortening Public School Bus Rides
![img-center-80](images/boston-public-bus.jpg)

Image Credit: [Patrick Hudepohl](https://www.flickr.com/photos/patrick-hudepohl/), used under [Creative Commons BY-NC-SA 2.0 license](https://creativecommons.org/licenses/by-nc-sa/2.0/).

[Source](http://www.datakind.org/blog/datakind-and-sas-to-help-boston-public-schools-shorten-school-bus-rides-lower-costs)
---

# Experimenting for What Works
![img-right-15](images/experiment1.png)
--

+ Challenge: Low-cost engagement tools such as letters, texts, and reminder calls are used but ineffective.
--

+ Opportunity: Experimental testing and improvement of service options

---

# Redesign of NYC Summonses
![img-center-65](images/summons.png)

Image credit [City of New York](https://www1.nyc.gov/site/criminaljustice/work/summons_reform.page). [Click for more information](http://www.ideas42.org/blog/new-behaviorally-informed-nyc-summonses-hit-streets/)

---

class:center,middle
# Let's get back to our Process Map

---

exclude:true
# Question-Driven Analysis
+ Goal -> Answer a specific problem or concern
+ Have a question or problem in mind when analyzing data
+ “I need to know X”
+ Problem-focused discovery with the data

---

exclude:true
# Exercise - Vision Zero (dB)
+ Given 311 noise complaint data, assist enforcement efforts by identifying community districts that have a high volume of noise complaints and the time frame enforcement resources should be deployed to combat the noise issue at its peak
+ Identify the prevalent types of noise complaints in these areas to guide enforcement in each community district
-->
---

# Let's Work Our Plan
<iframe src="https://giphy.com/embed/xUySTOigOUHucl3rfW" width="680" height="460" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/filmeditor-home-alone-christmas-movies-xUySTOigOUHucl3rfW">via GIPHY</a></p>

---

# NYC Community Districts

![img-center-65](images/cd_map.png)
.caption[Image Credit: Datapolitan [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)]

---

exclude:true
# Homelessness in NYC
<iframe width="100%" height="520" frameborder="0" src="https://richard-datapolitan.carto.com/viz/143c4e52-34e7-11e7-91e8-0ef7f98ade21/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>

---

# Links to Other Open Data Portals
+ New York City Census - https://popfactfinder.planning.nyc.gov/
+ New York State - https://data.ny.gov
+ US Federal Government - https://www.data.gov/

---
name:ddc
class:center,middle
# BUILDING A DATA-DRIVEN CULTURE

---

# Data Driven Culture
![img-center-95](images/ddc_compass.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_goals.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_data_literacy.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_sharing.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_leadership.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_questions.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_learning.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_testing.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_self_service.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass_culture.png)

---

# Data Driven Culture
![img-center-95](images/ddc_compass.png)

---

# Data Driven Culture
> “Do you have data to back that up?” should be a question that no one is afraid to ask (and everyone is prepared to answer)

## - Julie Arsenault

[Source](https://www.pagerduty.com/blog/how-to-create-a-data-driven-culture/)

---
name:wrap-up
class:center,middle
# WRAP-UP

---

# What We've Covered
--

+ Elements of a data-driven culture
--

+ Types of analysis
--

+ The analytics process
--

+ What else?

---

class:center,middle
# What might you do different with this information when you go back to your offices?

???
+ Facilitators encourage participants to think about how they can integrate the information into their work
---

name:final
# Final Thoughts
--

+ Data can tell a story, but doesn't speak for itself
--

+ Analysis is the search for understanding and where we learn to tell that story
--

+ Be good to your data and it will be good to you

---

# Technical Support
--

+ [Microsoft Office Support](http://office.microsoft.com/en-us/support/) - Documentation on various MS Office products
--

+ [Data Science Cheatsheet](https://github.com/govex/Data-Science) - Includes various terms and concepts related to data science
--

+ [Open Data Handbook](http://opendatahandbook.org/) - Guides, case studies and resources for government & civil society on the "what, why & how" of open data
--

+ [Copy of today's handout](workbook.pdf)

---

exclude:true
# Resources
+ [Data Driven New Orleans - Nolalytics](https://datadriven.nola.gov/home/)
---

exclude:true
# And Here's the R Code for It
[![img-center-85](images/smoke_signals_github_outline.png)](https://github.com/enigma-io/smoke-signals-model)
[Click here for the code](https://github.com/enigma-io/smoke-signals-model)

---

# Resources
--

+ [Harvard Government Performance Lab](https://govlab.hks.harvard.edu/) [Results-Driven Contracting](https://govlab.hks.harvard.edu/results-driven-contracting)
--

+ Carl Anderson [_Creating a Data-Driven Organization_](http://shop.oreilly.com/product/0636920035848.do)
--

+ DJ Patil & Hilary Mason [_Data Driven: Creating a Data Culture_](https://www.oreilly.com/ideas/data-driven)
--

+ [IDEO Design Kit](http://www.designkit.org/) - Resource for design thinking techniques
--

+ [Trello](https://trello.com/) - Free project management tool
--

+ [Asana](https://asana.com/) - Free project management tool
--
      
+ [Datapolitan training classes](http://training.datapolitan.com/)

---
name:contact

# .center[Contact Information]

## Elana Shneyer
<ul>
    <li style="font-size:35px">
        Email: elana[at]datapolitan[dot]com
    </li>
    <li style="font-size:35px">
        Website: <a href="http://www.datapolitan.com">http://www.datapolitan.com</a>
    </li>
    <li style="font-size:35px">
        Twitter: <a href="https://twitter.com/datapolitan">@datapolitan</a>
    </li>
</ul>

### Elizabeth DiLuzio
<ul>
    <li style="font-size:35px">
        Email: elizabeth[at]datapolitan[dot]com
    </li>
    <li style="font-size:35px">
        Website: <a href="http://www.evallearn.com">http://www.evallearn.com</a>
    </li>
    <li style="font-size:35px">
        Twitter: <a href="https://twitter.com/lizdiluzio">@lizdiluzio</a>
    </li>
</ul>

<!--## Sarah Kontos
<ul>
    <li style="font-size:35px">
        Email: kontoss[at]gmail[dot]com
    </li>
</ul>

### Richard Dunks
<ul>
    <li style="font-size:35px">
        Email: richard[at]datapolitan[dot]com
    </li>
    <li style="font-size:35px">
        Website: <a href="http://www.datapolitan.com">http://www.datapolitan.com</a>
    </li>
    <li style="font-size:35px">
        Twitter: <a href="https://twitter.com/datapolitan">@datapolitan</a>
    </li>
</ul>
-->

---

class:center, middle
#THANK YOU!

---

Based on the work of the [City of New Orleans, Office of Performance and Accountability](https://datadriven.nola.gov/home/)

???
+ Facilitator goes through the types of analysis with participants to introduce key ideas
+ Facilitator connects each type to either the process map they've created or to the their own work

---

class:center,middle
![img-center-90](images/nolytics_type_stacked.png "Copyright: Harvard University Ash Center https://ash.harvard.edu and used with permission")

---

# Finding a Needle in Haystack
![img-right-15](images/needle1_rev.png)
--

+ Challenge: Services target a small number of individuals (either those at high-risk, or most likely to be non-compliers) — and locating them is challenging. 
--

+ Opportunity: Predictive modeling helps to identify those who are likely to need services based on existing data

---

# New Orleans Distributes Smoke Alarms

![img-center-40](images/neworleansfire.jpeg)
.caption[Image Credit: Michael Barnett [CC BY-SA 2.5](http://creativecommons.org/licenses/by-sa/2.5), via Wikimedia Commons]

---

# Targeted Outreach Saves Lives

---

# Targeted Outreach Saves Lives

---

# Prioritizing Work for Impact
![img-right-15](images/prior1.png)
--

+ Opportunity: Data analyses that compare information about current cases to the information and severity of previous cases helps to instantly prioritize services.
---

# NYC Restaurant Inspectors Save Time

![img-center-65](images/greasetrap_csessums.jpg)

.caption[Image Credit: Grease Trap Cover Asphalt by [Christopher Sessums](https://www.flickr.com/photos/csessums/4740455231), [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/)]

.center[Source: http://www.nyc.gov/html/dep/html/press_releases/12-71pr.shtml#.W6v2nRNKhZo]

---

# Early Warning Tools
![img-right-15](images/early1.png)
--

+ Challenge: Resources are overly focused on reactive instead of preventive services; service providers are often surprised by spikes in need and have difficultly responding
--

+ Opportunity: Developing tools to predict need based on historic patterns

---

# Using Data to Fight Fires

---

# Better, Quicker Decisions
![img-right-15](images/decisions1.png)
--

+ Opportunity: Developing tools for collecting only relevant information and creating recommendation tools for operational decisions

---

# Cincinnati Targets Urban Blight

![img-center-40](images/cincinnatiurbanblight.jpg)
.caption[Image Credit: Wholtone, via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Abandoned-Over-the-Rhine-building.jpg)]

[Learn more](https://dssg.uchicago.edu/2015/08/20/blight-prevention-building-strong-and-healthy-neighborhoods-in-cincinnati/)

---

# Optimizing Resource Allocation
![img-right-15](images/optimize1.png)
--

+ Challenge: Department resources have been scheduled or deployed in the same way for a long time, despite changing patterns of need.
--

+ Opportunity: Use data to drive decisions on the deployment of resources

---

# Shortening Public School Bus Rides
![img-center-80](images/boston-public-bus.jpg)

Image Credit: [Patrick Hudepohl](https://www.flickr.com/photos/patrick-hudepohl/), used under [Creative Commons BY-NC-SA 2.0 license](https://creativecommons.org/licenses/by-nc-sa/2.0/).

[Source](http://www.datakind.org/blog/datakind-and-sas-to-help-boston-public-schools-shorten-school-bus-rides-lower-costs)
---

# Experimenting for What Works
![img-right-15](images/experiment1.png)
--

+ Challenge: Low-cost engagement tools such as letters, texts, and reminder calls are used but ineffective.
--

+ Opportunity: Experimental testing and improvement of service options

---

# Redesign of NYC Summonses
![img-center-65](images/summons.png)

---
---
# Outline the Process
--

+ What are the steps to creating our outputs?
--

+ What is the best order of steps?
--

+ How granular do we need to break this down for clarity?

## .center[.green[Collection<br>Verification<br>Description<br>Sensemaking<br>Communication/Visualization]]

---

class:center,middle
# 4 Concerns

---

# 1. Technical
--

+ Having the right tools
--

+ Having the people who can use them
--

+ Making everything work together
--

+ _Potential trap: having a solution in search of a problem_
---

# 2. Legal
--

+ Laws
--

+ Regulations
--

+ Practices
--

+ _Potential trap: not doing something because of mistaken assumptions_
---

# When HIPAA Gets In The Way of Health Care

![img-center-35](images/hipaa.jpeg)

.caption[Image Credit: Hipaa Nurse Shredding Papers, by [Atlantic Training](http://www.atlantictraining.com/blog/hipaa-compliance-paper-shredding-illustration2/), [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.en_US)]

[Learn more](http://www.nytimes.com/2015/07/21/health/hipaas-use-as-code-of-silence-often-misinterprets-the-law.html?_r=1)

---

# 3. Cultural
--

+ “We’ve always done it this way”
--

+ “I’m not sure I understand how this works”
--

+ _Potential trap: being afraid of rocking the boat_
---

# 4. Political
--

+ Inter-departmental
--

+ Intra-departmental
--

+ Public relations
--

+ _Potential trap: not putting the necessary effort into something that will pay dividends to your office and ultimately to the organization as a whole_
---

exclude:true
# Concerns
+ Are there any concerns with what we've mapped out?
+ Do you have an example of these concerns in your work?
???
+ Unnecessary after discussion above and takes up time

---

# Benefits
--

+ Time, money, lives saved
--

+ Better delivery of services to stakeholders
--

+ More transparency
--

+ More accountability
--

# .orange[**What could be some of the benefits we'd realize from what we've mapped?**]
---

exclude:true
# A Word on Performance Metrics
+ If it doesn't change your behavior, it's a bad metric

![img-center-40](images/badmetric.png)
.caption[Image Credit: Jurgen Appelo, [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/), via [Flickr](https://www.flickr.com/photos/jurgenappelo/8485492756)]

---

exclude:true
# Performance Metrics
+ What metrics do we have in our process map?
+ Will they influence what we do?
+ Do we need more/better ones?

---
exclude:true
# Goodhart's Law

>“When a measure becomes a target, it ceases to be a good measure.”

## - Charles Goodhart, Economist

# .orange[**How might this relate to our map?**]

---

name:open-data
# Definition of Open Data
--

> Open data is data that can be freely used, shared and built-on by anyone, anywhere, for any purpose

## - [Open Knowledge International](http://blog.okfn.org/2013/10/03/defining-open-data/)

---

# Key Features of Open Data
--

+ Availability and access
--

+ Reuse and redistribution
--

+ Universal participation
---

# Open Data Benefits
--

+ Transparency
--

+ Releasing social and commercial value
--

+ Participation and engagement

---

# Keeping NYC Accountable on Parking Tickets

![img-center-75](images/nyc_parking_ticket.jpg)
.caption[Image Credit: Parking Violator by [Atomische * Tom Giebel](https://www.flickr.com/photos/atomische/2299948817/), [CC BY-NC-ND 2.0 ](https://creativecommons.org/licenses/by-nc-nd/2.0/)]

Source: http://iquantny.tumblr.com/post/87573867759/success-how-nyc-open-data-and-reddit-saved-new

---

exclude: true
# New Yorkers Save Millions In Parking Tickets

![img-center-70](images/sidewalk-parking.jpg)
.caption[Image Credit: [Ken Lund](https://www.flickr.com/photos/kenlund/7236966946), [CC BY-SA 2.0](https://creativecommons.org/licenses/by-sa/2.0/)]

[Learn more](http://iquantny.tumblr.com/post/144197004989/the-nypd-was-systematically-ticketing-legally)

---

# Open Data Concerns
--

+ Privacy ([PII](https://en.wikipedia.org/wiki/Personally_identifiable_information) and [the Mosaic Effect](https://gcn.com/articles/2014/05/14/fose-mosaic-effect.aspx))
--

+ Accuracy
--

+ Security
---

# When Good Data Goes Bad

![img-center-70](images/nyctaxi.jpeg)
.caption[Image Credit: Kenny Louie, [CC BY 2.0](http://creativecommons.org/licenses/by/2.0), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File%3ANYC_TAXI_(7038011669).jpg)]

[Gawker matches Taxi and Limousine Data with Paparazzi Photos](http://gawker.com/the-public-nyc-taxicab-database-that-accidentally-track-1646724546)