Introduction
Today was the last day of our dashboard week. Today was little different compared to the other days of the dashboard week. Instead of the day starting with the presentations of the previous day dashboard challenge, instead we were given a new dataset in the morning and had to present both the dashboards in the evening. In this blog, I will be writing about the second dashboard I built (link to the blog for the first dashboard I presented on day-4 here).
The Dataset
Today, we were given a dataset on 311 non-emergency services of Boston. The dataset had all the records from 2011 until 4th January 2022. The dataset was quite granular and the granularity was details of each call per record.
Alteryx Workflow
There were multiple datasets, where each dataset had all the call records per year. All the files were consistent and had the same schema. So, I used wildcard format in the input tool to union all the dataset together. After bringing the filter into Alteryx, I outputted the files as a Tableau Hyper file. Below is the screenshot of my Alteryx workflow.
Tableau Dashboard
The data was quite granular. So, it was a very good data to analyze how each department were performing. There were more than a dozen departments for each district in Boston. I wanted to analyze which departments were most efficient and what was making them different compared to the other departments. In the dataset, there was the details if the cases were solved on time. So, the basis of my calculation was that out of all the cases, I calculated percent of cases that the department was solving on time. I then compared the top-5 most efficient departments with the next 5 efficient departments.
Let us look at the first part of my analysis. Find the screenshot attached below.
As you can see that top efficient departments were solving more cases (average per department) as compared to the next 5 most efficient departments. Also, the time they were taking much less time to solve each case as compared to the later departments. It was clear in the initial part of my analysis that these top-5 most efficient departments were really doing something good to perform so well.
I decided to dive deeper and understand why they were performing so good as compared to the other departments.
But when I looked at the type of issue the top-5 most efficient departments were handling, I found the these issues were less severe as compared to the other departments. For example, solving an issue of unsatisfactory living condition is more severe as compared to picking up a bulk item. Which answers why it took the top-5 most efficient departments less time to solve so many cases in such a sort time per case.
Please find the screenshot of my dashboard. You can also explore this dashboard that I have uploaded to Tableau Public here.
To Conclude
With this presentation, it was the end of our dashboard week. It was a very good experience overall and had learned a lot. To know more about my experience as for the dashboard week, read my article here.
