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 Richard Dunks

### 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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# 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)

> If data is enabling important decisions, then the data is important too

## - [Richard Dunks](https://twitter.com/Datapolitan/status/921435585386033153)

???
+ 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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# 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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# 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)]

---
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# It's Putting Them All Together

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

---
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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

---

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# 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 outcome measures
+ 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)]

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

---

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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)

---

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# 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

---

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# Gallery Walk
![img-center-45](images/gallery_walk.png)

---

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

---

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

---
# 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?**]

---

# 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

---

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 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]

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

---

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: Targets are difficult to identify or locate within a broader population
--

+ Opportunity: Data analysis and predictive modeling to identify targets 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: Services do not categorize high-priority cases early
--

+ Opportunity: Data analysis and predictive modeling to prioritize cases
---

# 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 overly focused on reactive services
--

+ 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: Repeated decisions are made without access to all relevant information
--

+ Opportunity: Developing 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: Assets are scheduled or deployed without input of latest service data
--

+ 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: Services have not been assessed for impact
--

+ 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/)

---

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)