Power BI, Will It Really Give Me Data Viz Superpowers?

May 15, 2025 by Jonah Hall

Why use a data viz software? 

Before we jump into the software, you may be thinking, “why go through all the trouble of learning another software just to create visualizations? Aren’t my [insert your software of choice here] visualizations good enough?” 

Even when you get comfortable and quick in [your software of choice], at the end of the day, these programs’ primary functions are typically to store, present, or analyze your data, not bringing in data with the purpose of creating visualizations. 

The advantage of learning data visualization software like Power BI is that it is designed with visualization as its primary purpose. If you have learned or even mastered creating visuals in another software, you can 100% learn and master visualization software like Power BI. 

What can Power BI do? 

First, Power BI is excellent at bringing in data. You can connect multiple large and different types of data sources to Power BI, transform them, and perform calculations as necessary to prepare visuals. 

For data sources, if you can access the data, Power BI can connect to or import it. Power BI can take flat files (ex. Excel, PDF, or CSV), pull direct (snapshot or live) from a database (ex. MySQL, Oracle, SQL Server), import from a website, R script, Python script, and so many more! Even if you have multiple data sources, you can load as many as you need in and create relationships between your data sources.  

Creating relationships serves as the backbone of your data model if you have multiple data sources. For example, say you have a data source with student demographic data and another with student course information. If both contain a unique identifier, such as their student ID, you can create a relationship between the data sources based on that student ID and Power BI will know which course information connects with which student in your demographic data.  

Most of the mistakes within building a model occur at this step, and it is important to understand how and why you are building your model in a certain way or else you could sluggish, incorrect, or confusing output. I suggest reading Microsoft’s overview of relationships and then later this two-part blog post on Power BI data modeling best practices (part 1, part 2). Warning! This blog post is overly detailed for beginners, but extremely important information to avoid common Power BI pitfalls with relationships. I have had to deal with, and overcome, issues related to cardinality, filtering, and schema structure that are discussed in the blog.  

An overview of Power BI’s capabilities: bringing in multiple sources of data, cleaning data, creating relationships between data sources, and using the data to generate a visual report. 

Once you have identified your dataset, Power BI has abilities to transform your data into clean, workable, data within their Power Query editor. This editor has functionalities like Excel such as updating data types, replacing values, creating new columns, and pivoting data. This is done using the Power Query GUI or its script language, M. These transformation steps can be “saved” to your data source and performed on your data each time Power BI connects to or updates that data source. So, once you have cleaned up your data once, it is done automatically using the steps you already created! 

Power BI can then do complex calculations on your dataset once you’ve loaded it in. It uses a function and reference library called Data Analysis Expressions (DAX, for short) that is like expressions used in Excel. Check out Microsoft’s overview of how DAX can be used within Power BI and the library of DAX functions. In my use within Power BI, I mainly use calculated columns and measures.  

For example, let’s say I have a column in my data set that shows the date a form was submitted in this format: mm/dd/yyyy hr:min:sec. If I want to count the number of forms submitted in the calendar year 2025 and display that value on my report, I can create a measure using the DAX functions. It would look something like this: 

Finally, Power BI’s main function is to create engaging visuals and reports to infer information from your data. Power BI has a workspace that allows you to easily select visuals, drag fields from your data into the visuals, and then edit or customize your visuals. The software is pre-loaded with many useful visuals, but you can search and download additional, user-created, visuals as well. Check out the image below showcasing Power BI’s workspace. 

image from Microsoft (source) 

Visuals can be used together (like in the image) to create a report. These reports can be published in a shareable environment through the Power BI Service so others can view the report. This is how companies create and distribute data reports! 

One exciting feature of Power BI is the ability to use and interact with Microsoft’s AI, Copilot. Copilot is quite intelligent when it comes to understanding and using data and can even help build visuals and whole reports. Check out this three minute demo on Copilot within Power BI to get a sense of its capabilities. 

I want to try! 

If you are interested in poking around Power BI to see if it could be useful for you, you can download the desktop version for free here. I will note that even if you are working on personal projects and have data you want to create visuals from, it may be worth it to try Power BI! 

Microsoft has training, videos, sample data you can play with once you open the program, and a community forum to help with any questions you may have.  

Curious what Power BI can do? Check out some of the submissions from this year’s Microsoft’s Power BI Visualization World Championships! 

Empathy in Evaluation: A Meta-Analysis Comparing Evaluation Models in Refugee and Displaced Settings

March 15, 2025 by Jonah Hall

By Dr. Fatima T. Zahra

Hello, my name is Fatima T. Zahra. I am an Assistant Professor of Evaluation, Statistics, and Methodology at the University of Tennessee, Knoxville. My research examines the intersection of human development, AI, and evaluation in diverse and displaced populations. Over the past decade, I have worked on projects that explore the role of evaluation in shaping educational and labor market outcomes in refugee and crisis-affected settings. This post departs from a purely technical discussion to reflect on the role of empathy in evaluation practices—a quality that is often overlooked but profoundly consequential. For more information about the work that I do check out my website. 

Evaluation is typically regarded as an instrument for assessing program effectiveness. However, in marginalized and forcibly displaced populations, conventional evaluation models often fall short. Traditional frameworks prioritize objectivity, standardized indicators, and externally driven methodologies, yet they frequently fail to capture the complexity of lived experiences. This gap has spurred the adoption of empathy in evaluation, particularly participatory and culturally responsive frameworks that prioritize community voices, local knowledge, and equitable power-sharing in the evaluation process. The work in this area is substantially underdeveloped. 

Why Does This Matter?

My recent meta-analysis of 40 studies comparing participatory, culturally responsive, and traditional evaluation models in refugee and displaced settings underscores the importance of empathy-driven approaches. Key findings include: 

• Participatory evaluations demonstrated high levels of community engagement, with attendance and participation rates ranging from 71% to 78%. Evaluations that positioned community members as co-researchers led to greater program sustainability. 
• Culturally responsive evaluations yielded statistically significant improvements in mental health outcomes and knowledge acquisition, particularly when interventions incorporated linguistic and cultural adaptations tailored to participants’ lived experiences. 
• Traditional evaluations exhibited mixed results, proving effective in measuring clinical outcomes but demonstrating lower engagement (54% average participation rate), particularly in cases where community voices were not integrated into the evaluation design. 

The sustainability of programs was not dictated by evaluation models alone but was strongly influenced by community ownership, capacity building, and system integration. Evaluations that actively engaged community members in decision-making processes were more likely to foster lasting impact. 

Lessons from the Field

In our research on early childhood development among Rohingya refugees in Bangladesh, initial evaluations of play-based learning programs suggested minimal paternal engagement. However, when we restructured our approach to include fathers in defining meaningful participation—through focus groups and storytelling sessions—engagement increased dramatically. This shift underscored a critical lesson: evaluation frameworks that do not reflect the lived realities of marginalized communities risk missing key drivers of success. 

Similarly, in a study examining the impact of employment programs in refugee camps, traditional evaluations focused primarily on income and productivity, overlooking the psychological and social effects of work. By incorporating mental well-being as a key evaluation metric—through self-reported dignity, purpose, and social belonging—we found that employment offered far more than economic stability. These findings reinforce an essential principle: sustainable impact is most likely when evaluation is conducted with communities rather than on them, recognizing the full spectrum of human needs beyond economic indicators. 

Rethinking Evaluation: A Call for Change

To advance the field of evaluation, particularly in marginalized and displaced settings, we must adopt new approaches: 

1. Power-sharing as a foundational principle. Evaluation must shift from an extractive process to a collaborative one. This means prioritizing genuine co-creation, where communities influence decisions from research design to data interpretation. 

2. Cultural responsiveness as a necessity, not an afterthought. Effective evaluation requires deep listening, linguistic adaptation, and recognition of cultural epistemologies. Without this, findings may be incomplete or misinterpreted. 

3. Expanding our definition of rigor. Methodological validity should not come at the expense of community relevance. The most robust evaluations integrate standardized measures with locally grounded insights. 
4. Moving beyond extractive evaluation models. The purpose of evaluation should extend beyond measuring impact to strengthening local capacity for continued assessment and programmatic refinement. 

Looking Ahead

The field of evaluation stands at a pivotal juncture. Traditional approaches, which often prioritize external expertise over local knowledge, are proving inadequate in addressing the complexity of crisis-affected populations. Empathy in evaluation (EIE) methodologies—those that emphasize cultural adaptation, power-sharing, and stakeholder engagement—offer a path toward more just, effective, and sustainable evaluation practice. 

For scholars, this shift necessitates expanding research on context-sensitive methodologies. For practitioners, it demands a reimagining of evaluation as a process that centers mutual learning rather than imposing external standards. For policymakers and funders, it calls for investment in evaluation models that are adaptive, participatory, and aligned with the needs of affected populations. 

As evaluators, we hold a critical responsibility. We can either reinforce existing power imbalances or work to build evaluation frameworks that respect and reflect the realities of the communities we serve. If we aspire to generate meaningful knowledge and drive lasting change, we must practice empathy, cultural responsiveness, and community engagement at the core of our methodologies. 

Additional Resources

For those interested in deepening their understanding of these concepts, I highly recommend the following works: 

• Evaluation in Humanitarian Contexts:  

• Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press. 

• Culturally Responsive Evaluation:  

• Hood, S., Hopson, R. K., & Kirkhart, K. E. (2015). Culturally Responsive Evaluation. In K. E. Newcomer, H. P. Hatry, & J. S. Wholey (Eds.), Handbook of Practical Program Evaluation (4 ed., pp. 281-317). Jossey-Bass. https://doi.org/10.1002/9781119171386.ch12 

• Participatory Research in Development Settings:  

• Chouinard, J.A., Cousins, J.B. The journey from rhetoric to reality: participatory evaluation in a development context. Educ Asse Eval Acc 27, 5–39 (2015). https://doi.org/10.1007/s11092-013-9184-8 

• Empathy in Evaluation:  

• Zahra, F. T. (n.d.). Empathy in Evaluation. https://www.fatimazahra.org/blog-posts/Blog%20Post%20Title%20One-gygte 

• Empathy and Sensitivity to Injustice:  

• Decety, J., & Cowell, J. M. (2014). Empathy and motivation for justice: Cognitive empathy and concern, but not emotional empathy, predict sensitivity to injustice for others (SPI White Paper No. 135). Social and Political Intelligence Research Hub. https://web.archive.org/web/20221023104046/https://spihub.org/site/resource_files/publications/spi_wp_135_decety.pdf 

Final Thought: Evaluation is a mechanism for empowerment and is more than just an assessment tool. Evaluators have the capacity to amplify community voices, shape equitable policies, and drive sustainable change. The question is not whether we can integrate empathy into our methodologies, but whether we choose to do so.  

Giving Yourself Room to Grow is Critical to Long-Term Wellbeing, and In Turn, Success

February 15, 2025 by Jonah Hall

By M. Andrew Young

I’ve noticed that in higher education, particularly in graduate and professional programs, there are a lot of highly conscientious people. I am one of them. This anecdotal observation or generalization extends to faculty, staff, and students alike. A year ago, I was doing some research on the changing landscape of evaluation and assessment career skills, and when I looked at how much the landscape has changed post-pandemic, I was astounded how rapidly the culture, values, and demands in the workplace had shifted (see this resource included in my reference section for more info, even though it is even becoming outdated: Essential Post-Pandemic Skills | ACCA Global, 2021).  

The laws of physics demand that for every action there is an equal and opposite reaction, and I have noticed that oftentimes, being conscientious, which is a good thing, is counterbalanced by its less-useful companion: high levels of self-imposed demands for excellence or even perfection. In 2021, Forbes magazine released an article called “Why Failure is Essential to Success” (Arruda, 2021). It is a really good read, and their interview with Dr. Sam Collins was eye-opening. The basic premise is that our culture celebrates and glorifies success; we even idolize overcoming adversity success stories, but we rarely see the numerous and deep failures those success stories encountered along their road to success. We love victory, but do not fully feel the depths of the pain, depression even, or discouragement they waded through along the journey.  

People like me are often so concerned with getting it right the first time and setting a personal standard so high that when we can’t attain it, we immediately sink into an unproductive self-deprecating, self-condemnatory internal dialogue. Doubts gnaw at our own self-concept of our worth and capabilities to succeed, and there is an insidious voice telling us to give up, that we aren’t capable of succeeding, that we are alone or unique in our struggles, and that the effort we put into it won’t result in anything other than wasting our time we could be using by just being satisfied with our current status-quo. 

It is incredible how we can grow without even noticing it in the moment. Let me tell you about Andrew 10 years ago. Andrew worked for a web design and marketing consulting company. The hours were long, the pay was abhorrently low for the job title I had, and I was unhappy and out of my element. The original job I was hired to do was create data visualizations for marketing surveys. It morphed into learning survey instrument development, data cleaning, statistical analysis, search engine marketing, search engine optimization, and website quality assurance. I was not ready for the work because I was not properly trained nor supported by professional development for what I would encounter. I made a LOT of mistakes, and I was unhappy. I recall a conversation with my then supervisor. It was one of those uncomfortable conversations where my work quality didn’t measure up to the demands of the job or their expectations. We were speaking about data visualization, and they gave me a scenario of a creative way to visualize geographical map information. Something was said along the lines of, “This is the type of stuff we are looking for”, and my response was, “I don’t know that I am capable of thinking up those things on my own”.  

When I reflect on that moment, I chuckle at how simplistic that data solution was within the context of my current knowledge. When I look at the types of data analyses I’m capable of and knowledge I possess now through the lens of what I was capable of only two years ago, I can see the growth. When I look at the quality of my work today compared to in the past, distant and recent, there is growth. As a parent of school-aged children now, I see the incredible pressures this culture levies on immediate success and high performance. My middle child, who is four years younger than her older sister, has unrealistic expectations of her own capabilities and limitations, and often finds herself at a comparative disadvantage to her sister. Both my school-aged children have been asked to perform tasks, to which they fail or don’t perform to their level of desire or expectations, and when asked to do it again they’ve huffed in frustration and despair, “I can’t do that, dad!”, to which I always reply, “No. You can’t yet. You CAN figure it out!” 

Oh, if I had learned that lesson earlier in my life. Sometimes we have families with impossible expectations for us. Sometimes we work for employers who want us to perform at a high level, never make mistakes, and are waiting with the hammer held twitchingly above our heads, ready for us to fail. Sometimes our educational system is designed to grind us through the mill at their speed when we really need to back up and master foundational things….the list goes on. 

Let me assure you of some things: you will disappoint those you love. You will make an embarrassing mistake at your job. You will misunderstand a school assignment and get a bad grade. You will send that email or chat message that you didn’t think through well enough. You will forget a deadline. You will get turned down for that promotion. You will receive rejection letters for almost all of those “dream jobs” with the nice salaries you’ve applied for.  

And that’s ok.

Embrace failure. It isn’t the end; it is an opportunity to learn and grow. 

Embrace chuckling at the simpleton’s drivel you produced “back when”; you were proud of it then because it was what you were capable of then.

Pursue growth, not perfection; every project and every challenge are opportunities to get better, so embrace where you’re at.

Finally, never get comfortable. Life is a journey, not a destination, and if we ever deceive ourselves into thinking that we can rest on our laurels, we stop growing. It takes an oak tree a hundred years to tower over its peers. Do you see it now? If we recognize that our journey is about growth, it is ok to be where we are and recognize that growth takes time and persistence.

Cool Extra Resources:

A UTK Class I HIGHLY recommend to study student success: ELPS 595: Student Success in Higher Education 

A book that was instrumental for me understanding wellbeing/belonging/success:  

Quaye, S. J., Harper, S. R., & Pendakur, S. L. (Eds.). (2020). Student engagement in higher education: Theoretical perspectives and practical approaches for diverse populations (Third edition). Routledge. 

Wellbeing/Strengths Assessments: 

Gallup Clifton Strengths: https://www.gallup.com/cliftonstrengthsforstudents/ 

EdResearch for Action: https://edresearchforaction.org/research-briefs/evidence-based-practices-for-assessing-students-social-and-emotional-well-being-2/  

 
Full Reference List: 

Arruda, W. (2021, December 10). Why Failure Is Essential To Success. https://www.forbes.com/sites/williamarruda/2015/05/14/why-failure-is-essential-to-success/ 

Essential post-pandemic skills | ACCA Global. (2021). https://www.accaglobal.com/lk/en/affiliates/advance-ezine/careers-advice/post-pandemic-skills.html 

Evidence-Based Practices For Assessing Students’ Social And Emotional Well-Being. (n.d.). EdResearch for Action. Retrieved January 5, 2025, from https://edresearchforaction.org/research-briefs/evidence-based-practices-for-assessing-students-social-and-emotional-well-being-2/ 

Quaye, S. J., Harper, S. R., & Pendakur, S. L. (Eds.). (2020). Student engagement in higher education: Theoretical perspectives and practical approaches for diverse populations (Third edition). Routledge. 

Singh, A. (2021, August 23). The top data science skills for the post-Covid world. https://www.globaltechcouncil.org/data-science/the-top-data-science-skills-for-the-post-covid-world/ 

Clean, Correlate, and Compare: The Importance of Having a Data Analysis Plan

February 7, 2025 by Jonah Hall

By Dr. Jennifer Ann Morrow

Why a Data Analysis Plan?

While it can be tempting to just dive right on in and conduct your proposed analyses (I mean who doesn’t just want to run a multiple regression right away?!?) it’s good practice to have a detailed plan for how you intend to clean your data and how you will address your evaluation/assessment questions. Creating a data analysis plan BEFORE you start working with your dataset helps you think through the data that you need to collect to address your questions, what specific pieces of the data that you will use to address your questions, how you will analyze the data that you collect, and what are the most appropriate ways to disseminate the data that you analyze. While creating a data analysis plan can be time consuming, it is an invaluable part of the data management and analysis process. Also, if you are working with a team (as many of us evaluator/assessment professional do!) it makes collaboration, replication, and report generation easier. Just like the data codebook, the data analysis plan is a living document that changes as you make decisions and modifications to your dataset and planned analyses.  

I share the data analysis plan with my clients throughout the life of the project so they are aware of the process but also so they can chime in if they have questions or requests for different ways to approach the analysis of their data. At the end of my time with the project I routinely share a copy of the data codebook, data analysis plan, and a cleaned/sanitized dataset for the client to continue to use to inform their program and organization. 

What is in a Data Analysis Plan?

Whether you create your data analysis plan in Excel, Word, or some other software platform (I tend to prefer Word) these are my suggestions for what you should include in a data analysis plan: 

• 1.) General Instructions to Data Analysts
• 2.) List of Datasets for the Project
• 3.) Who is Responsible for Each Section of the Analysis Plan
• 4.) Evaluation/Assessment Questions
• 5.) Variables that You Will Use in Your Analyses
• 6.) Step by Step Description of Your Data Cleaning Process
• 7.) Specific Analyses that You Will Use to Address Each Evaluation/Assessment Question
• 8.) Proposed Data Visualizations that You Will Use for Each Analysis
• 9.) Software Syntax/Code (e.g., SPSS, R) that You Will Use to Analyze Your Data

Since many times there are multiple people working with my datasets (Boy…did it take me a long time to get used to giving up control here!) including step by step instructions for how your data analysts should name, label, and save files is extremely important. Also providing guidance for how data analysts should document what they do (see project notebook in your data codebook!) and how they arrived at their decisions is invaluable for keeping the evaluation/assessment team aware of each step of the data analysis process. 

I typically organize my data analysis plan by first listing any data cleaning that needs to be completed followed by each of my evaluation/assessment questions. This way all of my analyses are organized by the questions that my client wants me to address…and this helps immensely when writing up my evaluation/assessment report for them.  

Including either the software syntax/code (if using something like SPSS or R) or the step-by-step approach to how you are using the software tool (if using something like Excel) to clean and analyze the data is so helpful to not only your team members but also your clients. It allows them to easily rerun analyses and critique the steps that you took to analyze the data. I also include in my syntax/code notes about my decision-making process so anyone can easily follow how and why I approached the analyses the way that I did. 

Additional Advice

While it is important to develop your data analysis plan early in your project always remember that it is a living document and it will definitely change as you are collecting data, meeting with your client to discuss the evaluation/assessment, and during the data cleaning process. Your “perfect” plan may not work once you have collected your data, so be flexible in your approach. Just remember to document any changes that you make to the plan and to your data in your project notebook! 

Resources

12 Steps of Data Cleaning Handout: https://www.dropbox.com/scl/fi/x2bf2t0q134p0cx4kvej0/TWELVE-STEPS-OF-DATA-CLEANING-BRIEF-HANDOUT-MORROW-2017.pdf?rlkey=lfrllz3zya83qzeny6ubwzvjj&dl=0 

http://fogartyfellows.org/wp-content/uploads/2015/09/SAP_workbook.pdf 

https://cghlewis.com/blog/project_beginning

https://learn.crenc.org/how-to-create-a-data-analysis-plan

https://pmc.ncbi.nlm.nih.gov/articles/PMC4552232/pdf/cjhp-68-311.pdf

https://the.datastory.guide/hc/en-us/articles/360003250516-Creating-Analysis-Plans-for-Surveys

https://www.slideshare.net/slideshow/brief-introduction-to-the-12-steps-of-evaluagio/26168236#1

https://www.surveymonkey.com/mp/developing-data-analysis-plan

https://youtu.be/105wwMySZYc?si=9SEqjP2HWB5k4MDn

https://youtu.be/djVHKjmImrw?si=BdfSxl6C4weZEOgD