![Julio Rivera, Carroll University faculty]()
Dr. Julio Rivera teaches graduate and undergraduate courses in applied business analytics and performs outreach through the Analytics and Business Intelligence Consortium (ABIC), which is spearheaded by Carroll’s School of Business. Dr. Rivera is an international leader in the undergraduate research movement and is emeritus president of the Council on Undergraduate Research (CUR), where he was president and served on its executive board.
It is time to tell more data stories
I think I have put this blog off more times that I can count. Each time I come close to publishing something, events get in my way or something becomes more important. I had planned to start this last fall, but a sudden shift in work responsibilities meant that I had to concentrate on less data related matters. Those duties ended in January and I was all set to start in March 2020 and then the world changed. To write about some of the data questions I was interested in starting with seemed trite and insensitive. I’m also well aware that the world does not need another blog.
Still I knew I had to start. The sheer amount of data coming in about the COVID-19 crisis and the levels of uncertainty in decision making that abound across organizations as they face their futures makes it necessary to begin this process. I also had things I wanted to write about.
So here we go...The goals of this blog are simple:
- I want to highlight and tell stories about how people are using data in decision making. Some of these stories will highlight successes and others will find fault. Regardless, the goal will be to build an array of examples of data use in practice.
- Tell stories with the data that I am working with or that the people around me are working with.
- I want to talk about some of my own ideas about the world through data projects of my own.
- I hope to crowdsource some problems (that would require a crowd) so I’m hoping to gain some readership.
- I hope to have some fun with this.
Those goals will probably evolve and develop over time, but it is a good place to start.
One last note: data and analysis always have an edge to it. There is a myth that data and analysis is neutral and objective. It is not. Different people have weighed in on this asserting that quantitative methods are reductive and “right-center”, while others assert that these methods have a “left-center” bias. These labels dismiss the power of data, analysis. How we interpret data can have biases, but the techniques are the same regardless of the analysts frame on the world. The assumptions we make, and our own biases lead us to build different analyses. I have a good and close colleague and we sit on opposite sides of the political spectrum. We work well together because we can agree on data and methods. Often, we share results and notes of our work. We often respond back with something like “I don’t really like this outcome, but you did the analysis correctly and your data is good.” It is how we have become effective work partners. It is also how we work to persuade each other. The power is that if the data analysis is well done, it can drive conversation and possibly change another’s thought.
I write about my colleague and our relationship because as I write these posts I am bound to please you; I am bound to make you angry. If I am good at my work I will do both.