Tallahassee utilities improve services, quality, and productivity

Florida capital city, Tallahassee, provides services to more than 180,000 citizens. In the following videos, Business Systems Analyst David Carnes and Business Process Solution Manager Brian Scott discuss how the city is using Tableau to:

  • Speed insight into wastewater treatment plant data for better quality monitoring
  • Enable 30% improvement in field teams’ workload efficiency
  • Play a part in reducing sewer overflow problems

The more than 180,000 citizens of Tallahassee, Florida rely on their local government for many important services. In the following videos, Business Systems Analyst David Carnes and Business Process Solution Manager Brian Scott talk about how the city is using Tableau.

In video 1 David explains how they are delighting managers at wastewater treatment plants with fast, easy insight into water quality data.

In video 2 Brian talks about how his Tableau visualizations helped utility managers better understand sewer clearing teams’ productivity—and make decisions that improved productivity by up to 30%.

Tableau: How do you work with Tableau and data?
David Carnes, Business Systems Analyst: The people I interact with the most are the managers and the field personnel. They're the ones that really want the data, and I try to give to them in a timely manner. Tableau has enabled me to build a really good relationship with the wastewater treatment managers.

Tableau: What did you first use Tableau for?
David: The very first visualization I made for them was for total nitrogen. The old way they was to look at four different charts to get daily, weekly, monthly and yearly. I put it all in one, and I also gave them trend lines and some things like that, too. And when I saw the senior manager there one day in the parking lot, he came to me, he was like, oh, this is great, I can see all of my data on the same page! So, they're really excited about what they're doing, and what we're doing there. So it's really opened up a nice connection to those folks.

Tableau: What are some other projects you’ve done with Tableau?
David: I'm very proud of many of the things I've done in Tableau. One of the most recent examples would be the dashboard I created for the phosphorous effluent that comes out of our wastewater treatment plant. We're regulated on phosphorous and total nitrogen, because those are fertilizers that can affect our aquifer and have a dramatic impact upon the Pristine Spring down in the neighboring county. So we really want to track that and keep it low.

There's a need that came up and I got the request from the wastewater treatment plant manager in the afternoon, and by midmorning the next day I had a complete dashboard for him, and I had been able to explore the data and I understood what he wanted to see.

I did version one and then they also mentioned they also were tracking weekly, monthly and yearly averages, and their limits are all lower than the daily (load ?). So I quickly hit a version two where I put sparklines across the bottom and just show here's where you are, so they could look at the daily and go, oh, we're in compliance and they could quickly see, oh wait, we're heading for noncompliance. So it was just a nice little quick turnaround.

Prior to using Tableau, the phosphorous data was show in an older tool, and they had to click through a bunch of things to see it didn't show much. It was hard to really notice the change over time. So they were quite happy when I was able to show a lot of data all in one screen of all the different time intervals.

Tableau: How has this change benefited you?
David: One of the benefits of moving this into Tableau is the fact I can automate the updates that it tracks. Before, somebody would actually get a file that was spit out automatically by a proprietary system. They would open up Excel, munge the data, save it, import it into this other tool that would create the graphs for them, and then physically move the files up to a server so we could show it on our portal. Now it's just there. They just go do it.

The managers meet on a regular basis, and they do talk amongst themselves, and it's really nice when my boss comes back to us and says, hey, Joe was talking about you guys and really getting some feedback that they're talking to him saying, hey, you know, you guys did something nice. He's doing it in front of all the managers, so they all know, and so they're all starting to go, hmm, what else can we do? So it's been very nice getting that recognition.

We use Tableau to measure workload efficiency at the time of sewer main cleanings… there has been a drastic reduction in sewer overflows in the city since we started tracking this data better.

Improving workload efficiencies, fewer sewer overflows

Tableau: What’s the focus of what you do?
Brian Scott, Business Process Solution Manager: Our focus is for delivering excellent customer service and highest quality product to the customer.

Tableau: How does data analysis help?
Brian: People see value in looking at their data visually and being able to understand complicated relationships very quickly, and repeatedly come back to me for requests for more visualizations. So things that I never would have thought to try to look at for relationships I'm being asked from several different business units to generate more visualizations to speak to that.

Tableau: What are some specific projects you’ve used Tableau for?
Brian: Speaking directly towards some utility cases, we use Tableau to measure workload efficiency at the time of sewer main cleanings. And in that situation the metric isn't how many work orders you completed but how many linear feet were actually cleaned. So we built a visualization that would allow the dispatchers to understand in real time how many linear feet their crews had cleaned on an individual basis over the past few days or even that day. And there was a nearly 30 percent increase in productivity amongst some groups when that functionality was exposed.

It was a very iterative process, because I exposed a build of the visualization to them and they said, hey, you know, some of our guys were looking at this and they said, well, I only cleaned 2,000 linear feet yet, but I was working on Monroe Street that had a lot of traffic and one of my guys was flagging traffic all day.

So what we wound up doing was going back into the application, giving people the ability to track whether or not they were working in heavy traffic condition or easement condition, and then mapping that back onto the visualization. So it was a very iterative process between myself, the dispatchers and the field workers to try to make sure that we're adequately capturing the work that was being done.

Tableau: Who was involved in this project?
Brian: There were about five or six crews that were involved in the sewer main cleaning process, and as a result of that I think we saw the total linear feet cleaned a year go up by about 50,000 feet.

Tableau: And that is roughly a 30% productivity increase—amazing. What was the result?
Brian: So now, there were other things ongoing concurrently but one of the upshots is there have been a drastic reduction in sewer overflows in the city since we started tracking this data better and being more proactive at a preventative maintenance level. A lot of the times some of your best results come when you just are able to freeform play with things, and that was something that Tableau really made very easy to do.

Tableau: What data sources are you using?
David: My main data store tends to be Oracle databases. We've got some enterprise-wide systems for HR, customer information services and systems, and also our PeopleSoft financials data. I went and talked to them. I didn't say a thing about what I was trying to do. I just wanted to see what they wanted to get, and the priority in their data. And then I tried to understand their need more than just what they want.

Tableau: And are you using live connections or extracts? And how do you decide which one to use?
Brian: Our data source utilization is really dependent on the use case. So there are some situations where I have data that is updated maybe on a minutely basis, and I really need to use a live connection for that. If there are other instances where responsiveness of the visualization is more critical, I will run every two hours or every hour updates on data extracts.

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