Civic Analytics Network RePost: An open letter to the open data community

Open data is one of the most important and core missions of Chief Data Officers. In the past 5 years, the number of cities with an open data portal has grown significantly. While cities already have released terabytes of open data, CAN aims to set higher goals for open data to make it more accessible and usable. Our cities’ open data portals must continue to evolve to meet the public’s growing and changing needs.

While specific open data portal features may vary from city to city, there are universal requirements that all local governments need for an effective open data program…

The Civic Analytics Network (CAN), a consortium of chief data officers supported by the Ash Center for Democratic Governance and Innovation at Harvard Kennedy School, shares a set of guidelines for how best to advance the use of transparent government information. Among these guidelines is improving the usability and management of collected data. A guideline that, if followed, would likely prompt a spontaneous standing ovation from our otherwise calm and unflappable government auditing community.

Read more here, and go ahead and check out the Ash Center’s Data Smart City Solutions page while you’re at it.

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Stephanie Evergreen ReBlog: Qualitative Data Visualization (Gauge Diagram)

When we debuted our Qualitative Chart Chooser last November, we promised to dive into detail on specific visualizations, so let’s kick it off by discussing how and when to use one of the most derided charts of all: the gauge diagram.

I don’t know about your office, but we at OAD have a word for qualitative data: squishy. It’s squishy, and it’s smooshy. It’s sometimes indirect, and can be a real challenge to measure. Nevertheless, some of the most vital and telling information and evidence you can get might be squishy, smooshy, indirect, qualitative data. Interviews and long-winded answers to open-ended survey questions can provide context you just can’t get from pulling numbers out of a database, and can fill in those gaps of understanding left after a document review.

But how do you capture that in easily digestible picture form?

Jennifer Lyons, in a follow up guest post for Evergreen Data, breaks down one way to present qualitative data. Read more here!

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Goodwill Community Foundation Repost: Why, and how, to make a pivot table in Excel

When you have a lot of data, it can sometimes be difficult to analyze all of the information in your worksheet. Pivot tables can help make your worksheets more manageable by summarizing data and allowing you to manipulate it in different ways.

Check out the two-part video series below on the many uses and strengths of Excel pivot tables, and geek out quietly to yourself over your newfound powers of data analysis. You know we are!

 

 

 

Check out more here.

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Stephanie Evergreen Reblog: How dataviz can unintentionally perpetuate inequality (3 part series)

Maps seem to be especially prone to misrepresenting people in disadvantaged situations, particularly as we get ever closer to being able to pinpoint individuals’ locations geographically. Vidhya shared with me a map of concentrated poverty in Minnesota (a factor extremely comingled with being a person of color), where individual participants were marked by red dots. When presented to the actual participants living in these areas, they were not stoked. Instead, they felt like they were perceived as a threat, and the little pixels made them look almost like an infestation on an otherwise subtly colored map.

In this Stephanie Evergreen series, she explores some of the missteps that researchers and presenters of data can make when attempting to quantify the human experience. Visual presentation influences how data is perceived and understood. It is very important that we as collectors and presenters of data consider how it may (or may not) come across, particularly to communities involved and represented in the research.

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An approximation of the offending map

 

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The same information, presented in a way that “aggregates the issue rather than identifying individuals”

Part 1

Part 2

Part 3

We as auditors and eager life-long learners sometimes tackle complex issues of social significance. The last thing we want is the wrong graphic or stock photo to undermine our findings or the legitimacy of our work. This means approaching the presentation of those findings (in the report or otherwise) with the same care and deliberation with which we approach other aspects of the audit process, and creating an end product that successfully communicates our intended message.

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Stephanie Evergreen Reblog: Two alternatives to using a second Y-axis

Almost as often as I see a pie chart with a hundred tiny slivers, I see line graphs using two y-axes. And it is just as bad.

Is a chart that looks like this:

secondaryaxisoriginal…really so bad?

While “bad” is perhaps a strong word for using 2 Y-axes, Stephanie Evergreen shares some alternatives in this post that may do away with the resulting confusion. Follow the link, or check out some options below.

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secondaryaxisconnected1

 

 

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Methods (to our Madness): Leveraging Administrative Data to Understand a Management Issue

Periodically, we will highlight some of the methods used in a recently released audit. Every performance audit is unique and can require creative thinking and methodologies to answer our audit objective. Some of these methods could be replicated or present valuable lessons for future projects.

Anyone who pays attention to the news or lives near a fire prone area, knows that Oregon’s fire seasons have been extreme the past few years. But I sat down with Amelia Eveland, Senior Auditor, and Luis Sandoval, Staff Auditor, to learn about more than Oregon’s formidable wildfires: how the team used data to understand workforce issues at the Department of Forestry, as described in the recently released audit, Department of Forestry: Actions Needed to Address Strain on Workforce and Programs from Wildfires.

Department of Forestry staff had described fire seasons in terms of acres burned and other fire activity measures, but hadn’t put numbers to what they intuitively knew; those large and frequent fires were affecting all of their programs. The team was able to quantify some of the impact of fires on department programs and staff by analyzing the actual hours worked by employees.

Don’t Overlook Administrative Data Sources

One of the things that I found most interesting was their data source: payroll data. Payroll data is collected for administrative purposes. But administrative data should not be overlooked as a source of information for management analysis. Payroll data provided the information that the team needed and was possibly more accurate than other data sources, since accuracy is important when people are being paid.

Understand Your Data and Its Limitations

Using a data source that is collected for another purpose can have downsides though. The payroll data only went back 6 years and only showed hours billed, not worked. The hours worked by some staff who weren’t eligible for overtime weren’t captured.

The team also had to understand the data and parameters. To do this they worked with the department’s financial staff who were familiar with it. They asked the department staff to pull the data and to check the team’s methodology. In the course of this process, they eliminated pay codes that would double count hours. For example, if someone was working a night shift on a fire line, they could receive pay differential (a supplemental payment) on top of their regular salary. Pay differential hours were logged separately from the hours logged for regular pay, despite applying to the same shift. Initially the team had been counting these hours twice, but working closely with the agency helped them pinpoint and correct potential methodological errors.

Putting Numbers to the Impacts on Staff and Programs

The team overcame these minor obstacles to conduct some pretty interesting analyses. They found that the past three fire seasons had been particularly demanding in terms of staff time, mostly due to regular and overtime hours from permanent employees (as shown in the graph below). This suggests that these employees may be pulled from other activities, and may also feel overworked.

 

forestry-chart

Payroll Hours Billed to Fire Protection by All Oregon Dept. of Forestry Employees

 

The team was also able to get a more accurate picture of which programs were contributing to fighting fire through specialized incident management teams. Because many Forestry employees split their regular time between different programs (for example, someone may split their time 80/20 between Private Forests and Fire Protection), it can be hard to track which programs are being affected when that person goes out to fight a fire. The audit team totaled the regular hours billed to each program and used the proportion of this total to arrive at a proportion of contributing programs.

Get the Power Pivot Add-in (so cool)

I asked the team for advice on using payroll data. They suggested manipulating the data as much as possible in the data query tool before exporting the data for analysis. The team used excel for analysis but used the power pivot add-in to be able to summarize the large quantity of data.

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Stephanie Evergreen ReBlog: Timelines, 4 Ways

 

The least helpful timelines I’ve ever seen are these:

TimelineExamples3

where time is basically bulleted, as if each of these intervals is equidistant and as if a bunch of text is the best way to communicate something inherently not based in narrative. You are basically saying, this journey is going to be really confusing because I’m not willing to help you see what’s really going to happen when. Buckle up! And that’s probably not the vibe you’re trying to give off.

Read more about interesting alternatives to developing timelines (useful in reports! useful in project management! useful, all around!) over at Stephanie Evergreen’s humorous and enlightening blog.

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