Tips for Charting in Veera

Being able to collect and identify valuable data is important for making sound decisions to meet long-term goals, but collection and identification are only part of the process. Just as important is the ability to provide visual representations to transform your raw data into actionable information. This guide will help to create basic graphs in Veera and to improve them by making them more clear and eye-catching.

First and foremost, before you get started, you’ll need to identify what data you’d like to represent in your chart. You may need to whittle down your data so that it isn’t overwhelming but still fairly represents your population. For this purpose, you might consider using a Filter node to filter down to just the entries you’re truly interested in, or using a Cleanse node to create an “other” category to concatenate some of the smaller categories.

Once you have the dataset you’d like to use and have opened the chart node, your first step will be to select the chart type (pie chart, bar chart, etc.). Once you’ve done that, you’ll want to fill in all necessary fields on the right side of the Chart Data window, such as deciding which variables should be your x and y axes. Once everything has been labeled, you can edit the look of your chart by clicking on the colored icon in the top right of the window. 

As you begin to navigate through the Chart Style Editor window, here are some things to keep in mind:

1. The chart type does not automatically identify the chart type that was selected in the previous window – you’ll want to update this and select the appropriate chart type.

2. The sample chart you see on the right will update to reflect any changes you make in the chart editor. Use it to evaluate your aesthetic decisions as you go.

3. Below the sample chart you can choose whether or not to include data labels and adjust the font, size, style, color, and chart background.

4. When charting binary or categorical variables, you may not see a need to include a legend. If you select the ‘Legend’ tab and uncheck the ‘Display Legend’ box, you can remove the legend. If you do remove the legend, be sure the chart is labeled accurately in the first window.

5. If you’d like to make any of your charts 3D, you can do so by going to the ‘3D’ tab and checking the ‘Display in 3D’ box. There you can also change the inclination, depth, rotation, etc.

6. Once you’ve edited the background color and text color of your chart in (below the sample chart), you can choose a color palette for the rest of the chart by going to the ‘Color Palette’ tab. There you can select a pre-programmed palette or create your own custom palette.

7. One of the more subtle functions in the chart node is the ability to shade ranges within a chart – found in the ‘Axis’ tab. Here you can choose to assign colors to ranges within a chart to better visualize certain areas of interest in your data. This is especially nice when looking at retention rates or variables represented as percentages because the ranges are easy to define & interpret.

8. Another use of the ‘Axis’ tab is to help label your data. Too many times I’ve gone to chart my data but found that half my data points are unlabeled in my graph. Here, choose the axis that should contain those missing labels and unselect the ‘Auto Label’ box. Now under the ‘Major Tick Marks’ heading, unselect the ‘Auto Interval’ box and set the interval to be 1 to insert all missing data point labels. It should look something like this (note that I’ve chosen the y-axis):

9. Now that you know how to edit each chart individually, you might find that you like a particular style and want to save it to use later. You can do this within the Chart Node by clicking on the ‘…’ button next to the ‘Chart Style’ drop-down. Here, click the green plus button to add a new chart style – give it a name and you’ll find yourself in the ‘Chart Style Editor’ window. Once you’ve finished editing, you’ll be able to access your saved chart style within the Chart Node from the Chart Style drop-down menu.

10. Once you’ve created a chart type that you love, you can also export it to the Collaborative Cloud so that others can use it – if you have a minute, check out the cloud and search for chart styles for the opportunity to download styles that other users have created.

Do you have any tips for charting, or questions about how to use the charting node? Leave them in the comments below :)

Happy charting!

-by Jon MacMillan, Data Analyst at Rapid Insight

Set it and Forget it: The Case for Automated Reporting

During my time at Rapid Insight, I’ve found that regardless of the business, school, or non-profit we’re working with, reporting is a necessity. Anyone who has built a report knows that pulling data from an original source, cleaning it up, and transforming it into actionable information can be a clunky and time-consuming process. One of the best ways we’ve come up with to lighten the reporting load is to automate reports using Veera. Here’s our take on automated reporting, by the numbers; click on the links for full case studies:

The number of days (including interruptions!) it took Gloria Stewart, Director of Institutional Research at Schreiner University, to build an automated report.  

The number of departments within Tulsa County Juvenile Bureau that depend on reports that Shonn Harrold, Assistant Director, has automated. These reports include intake reports, detention center reports, case assignments, and referral reports. 

The number of hours that Scott Alessandro, Assoc. Director of Educational Services at MIT Sloan School of Management, saves each week by editing semi-automated ad hoc reports rather than creating new reports. 

The percentage of Excel spreadsheets that have errors, according to a 2008 study. Creating automated reports is a great way to catch spreadsheet errors as you're building a report and avoid them in the future by reducing the likelihood of human error. 

The percent improvement that Dallas Baptist University saw in terms of time spent to prepare a report by using Veera: "It took over 3.5 hours to prepare a report using our old system. It took 30 minutes in Veera."

What could you do with an extra five hours per week?

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Thoughts from a Reporting Wiz

Scott Alessandro of the MIT Sloan School of Management is a lover of ad-hoc reporting and coffee ice cream.  In anticipation of his webinar on Friday, we asked him a few quick questions about his day-to-day analytic life. 

CG - What types of analytic requests do you handle?

SA - Some ad-hoc requests and some internal reports for my office, including degree requirement completion, grade distribution reports, enrollments by programs, GPA comparisons among courses or programs, impact of student population on enrollment/availability, etc. 

CG - What is your typical response time?

SA - Much faster now [with Veera] than beforehand. In the past, it would take me at least a couple of hours of unbroken time to create a report - which means a while. With Veera, unbroken time doesn't make a bit of difference. 

CG - Have you seen your decision-making become more data-driven with Veera?

SA- Most definitely. I hoped that it was always data-driven, but now because I have such easy access to data, it allows me to answer more questions, or anticipate more questions. 

CG - What do you hope attendees will take away from your webinar?

SA - That we have a lot of data and the problem was not having the time to use it or go through it. That's what Veera allows us to do. Since it's a visual tool, it becomes that much more accessible for people who are not as data-inclined. 

CG - Anything else you'd like to add?

SA - When I have Veera on at home, even my kids are impressed. It looks really neat. There's something artistic about it and that's why I like it. 

*

We are pleased to present Scott's webinar, From Data to Decisions: Ad Hoc Analytics and Reporting with Rapid Insight Veera, on how he is utilizing Veera efficiently to respond to the wide range of analytic demands confronting him daily. The webinar will take place on Friday, December 14th from 11am - 12pm EST. 

For more information about Scott's webinar, or to register, click here. 

For more information about Scott, read on:

Scott Alessandro is the Associate Director of Educational Services at MIT Sloan School of Management. His main responsibilities entail overseeing the Registration Team, managing MIT Sloan’s course bidding system, and reacting to various data requests. In previous lives, he has worked at Boston University (running summer pre-college programs), Temple University (in the Honors Program and the Undergraduate Admissions Office), and the College Board (coordinating AP workshops and data reporting). All of his jobs have combined numbers and people, which has made Scott quantifiably and qualitatively happy. Outside of work, Scott satisfies his curiosity and finds entertainment in hiking, woodworking, playing sports, watching sports (though has not found as much happiness lately rooting for the Chicago Bears and New York Mets), and trying to stay one step ahead of his two young children. 

Customer Tips From... Brian Johnson (DonorBureau)

Today's installment in our customer tips series comes from Brian Johnson, the VP of Product and Operations at DonorBureau. Here are his tips: 

1. You can have multiple select statements in the Query node as only the last statement does not have a temp table statement. This has allowed me to automate any complicated SQL queries I have for reports to run every week. 

2. If you need to create a new version of a scheduled report, use the old version and it will inherit the schedule of the original report in terms of run times. 

3. You can save output to Dropbox or Google drive to distribute data to team members without having to fill up their inbox. 

Customer Tips From... Dan Wilson (Muskingum University)

Our next set of customer tips comes from Dan Wilson, Registrar at Muskingum University. Dan typically uses Veera for repetitive and/or complex reports, including multi-year enrollment history by date, historical majors and minors (by year and department), and IPEDS reporting. Here are his tips:

1. It is important to remember the merge characteristics (all from a, all from b, all from both, only matching, etc.) so the last thing I do in developing any report is to verify each of these. 

2. While Veera's CrossTab feature is quite useful, I find it easier and more familiar to output my results to a target excel file, and then have another excel spreadsheet with my pivot table that has all of the formatting and other features set up. That way I can update the data file without overwriting my formatted "results" file. The same can be done with separate sheets in a file, but some of my reporting files pull data from different queries and Veera reports. For those reports I can run data from several sources, then open up my main file and hit "refresh". 

3. For those instances when a transform looks like a computer program, I'll break it into smaller bits and spread it out over several nodes. This allows me to test smaller chunks of the function at a time and locate any errant code prior to needing valium. (Editor's note: using the de-bugger in the Transform node can also help to find errors quickly!)

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

The Forgotten Tabs: Frequency Analysis

During this year’s User Conference, I gave a presentation called “Analytics: The Forgotten Tabs”, which I’ve decided to expand into a blog series. The purpose of this series will be to explain how and why to use four of the lesser-known tabs in Analytics – Frequency Analysis, Means Analysis, Correlation Analysis, and Profiling Analysis. Each entry will focus on one of these tabs and we’ll start with the Frequency Analysis tab.

The Frequency Analysis tab’s output is actually fairly simple; it gives you the frequency of occurrence for any binary or categorical variable. For a single variable, it will output counts and percentages for each value of that variable. It is also capable of creating two-way cross frequencies, which output raw numbers, as well as row, column, and total percentages. 

While Frequency Analysis isn’t actually performing any statistical test – its functions are simple summing and percentage operations – it is providing valuable information about the number and percentage of observations that fall into each sub-category of a binary or categorical variable. Using this tab gives you a quick by-the-numbers glance at variables like “Ethnicity” or “Department”, which allows you to instantaneously compare subcategories without doing any manual addition or division. This is particularly useful when you’re working with a variable such as “Department” that may have a lot of sub-categories.

One other little-known fact about the output from Frequency Analysis (and other tabs) is that you can save it to the Report Bar the same way you would a graph or chart. To do so, click on the ‘Reports’ section of the taskbar and select ‘Launch Report Bar’. 

The report bar will float over your analysis; you can save things to it by dragging the outputs you wish to save into the bar itself. Saving things to the report bar allows you to export them from Analytics in a few different ways. If you select the ‘PPoint’ option before clicking ‘Export’, Analytics will create a PowerPoint such that each of the graphs our outputs you saved will become their own slide in the presentation. The other option you have is to save the information you’re interested in to the Reports tab in Analytics (by selecting the ‘Report’ option on the Report Bar), which allows you to create custom reports within the program and export these reports as Word Documents to be used later on. In any case, there are a number of ways to take the information that you’re getting from Analytics and use it in a presentation or report down the line.  

-Caitlin Garrett, Statistical Analyst at Rapid Insight