Contractors and developers in Stockton used to submit permits and wait, with little visibility into why timelines stretched or where projects stalled. That experience has changed.
By tapping into workflow data already inside Accela and building a custom KPI and dashboard infrastructure, the city’s Community Development team has gone from meeting commercial review targets 17% of the time to over 85%, and from 3% to 100% on-time for multifamily residential projects. What drove that shift is a model other cities can follow.
In the most recent episode of Civic Innovators, hosts Noam Reininger and Joe Morris talk with Rob Liddicoat, Deputy Director of Community Development for the City of Stockton, California, about Stockton’s journey from anecdotal performance explanations to data-driven KPIs and real-time dashboards.
Rob walks through how Stockton’s team built custom calculations from Accela’s enhanced reporting database, developed workload monitoring dashboards that gave every department visibility into “who has the ball,” and used that clarity to shift from reactive firefighting to proactive operational management.
The results speak for themselves: initial review targets for new commercial construction projects went from being met just 17% of the time to over 85%, and multifamily residential projects went from 3% to 100% in the most recent six-month period.
In this episode, you’ll hear:
- How to build trustworthy KPIs from existing workflow history data
- Why standardizing workflows makes performance metrics easier to calculate
- How shared visibility changes accountability across departments
- The policy change Stockton made after tracking review cycle counts
- Practical advice for agencies just starting their own data journey
Let’s dive in.
The problem with anecdotal data
Before the shift, Stockton’s permitting insights came largely from assumptions and individual incident reviews. If a contractor complained about a specific permit, someone could investigate that case. But no one could say, with confidence, how the department was performing overall.
“I asked a very straightforward question: how long does it take to issue a permit?” Liddicoat recalled. “And we couldn’t provide a straight answer. It just wasn’t acceptable anymore.”
When asked something as simple as that, there was no clean response. The data existed inside the system; nothing had been built to surface it.
That changed in late 2020, when Stockton began digging into its Accela enhanced reporting database with a specific goal: build the operational visibility that anecdotal management couldn’t provide.
Building KPIs from the ground up
Turning workflow history data into meaningful KPIs wasn’t a plug-and-play process. Liddicoat and his team spent significant time developing SQL-based scripting that could measure complete review cycles, including individual reviewer durations, and determine whether the city met its published targets.
The nuances added up quickly. Performance needed to be measured in both calendar days and working days, which meant accounting for weekends, city holidays, and Stockton’s 9/80 schedule with alternating Fridays off. Permit types and review cycle counts required different target timeframes. And workflow variations such as revisions, reissuances, and extended resubmittal loops each had the potential to throw off calculations if not properly handled.
“We had many iterations and a lot of diligence to make sure that the data being produced was reliable,” Liddicoat said.
The result is a fully automated data pipeline that pulls information from the Accela enhanced reporting database approximately every 30 minutes, processes it through a SQL environment, and pushes it into Azure storage. From there, Power BI dashboards published to Stockton’s internal SharePoint sites pull the updated data on the same cadence, showing both historical performance KPIs and live workload monitoring: who holds an active review task, how far along it is, and whether it’s at risk of going overdue.
From siloed reviewers to shared accountability
One of the most significant shifts was cultural, and it ran deeper than any process change.
Before the dashboards, individual reviewers and divisions could only see their own piece of the workload. A reviewer meeting their internal deadline had no visibility into whether a project was stalling in another department. The customer, meanwhile, experienced the city as a single entity, and that entity’s performance was defined by its slowest link.
“If there’s six reviewers on any given project and five of those reviews are done in a week, but that last reviewer took a month, well, the project itself took a month,” Liddicoat noted. “Our customers don’t experience departmental metrics.”
The workload monitoring dashboards changed that dynamic. Everyone across the organization now sees the same operational picture: which projects are active, where each one stands across review groups, and which are approaching or past their targets. Supervisors can now coordinate with other teams earlier and redistribute assignments before a missed deadline compounds into a bigger problem.
“Visibility tends to be a very effective motivator,” Liddicoat said. “This has really reinforced the point that we succeed or fail as a city team.”
Proactive management, policy change, and real results
With data in hand, Stockton’s leadership didn’t just monitor performance. They changed how the city operated. One early observation: projects entering their third review cycle had a tendency to spiral into fourth, fifth, even seventh cycles. In response, the team instituted a policy requiring a direct meeting with the applicant whenever a project reaches that threshold, to identify what’s driving repeated resubmittals and get the project back on track.
The ability to distinguish between city review time and applicant-driven delays has also reframed conversations with the development community. Rather than debating who’s responsible for a slow permit, both sides can look at the same data and figure out how to keep things moving together.
The numbers tell the story. When Stockton ran its original baseline analysis on projects from 2018 to 2020, the city was meeting its initial review targets for new commercial construction projects just 17% of the time. Over the most recent six-month period, that figure has risen to over 85%. For multifamily residential projects, the on-time rate went from 3% at baseline to 100% over the past six months.
What Stockton would tell other cities
For cities looking to walk the same path, Liddicoat offered several lessons from the process.
First, standardize workflows before building KPIs. The more consistent the workflows, the fewer variables the calculations need to account for, and the more reliable the metrics will be.
Second, start with operational questions. “Don’t build a dashboard just because you can,” he cautioned. “If it’s not answering an operational question, it’s not useful.” The right starting questions: Where are projects getting stuck? What is the customer experiencing? How are we performing as a city, across all departments?
Third, validate constantly. In Stockton’s experience, the first year especially requires ongoing attention to catch edge cases and workflow variations that can skew calculations. Building trust in the underlying data is as important as the dashboard itself.
And finally: don’t wait for perfection. “Jump on in,” Liddicoat said. “Iterate over time.”
Watch the full episode here.
Frequently asked questions
How did Stockton, California, build its permitting performance dashboards?
Stockton used Accela’s enhanced reporting database as its data foundation, then developed custom SQL-based scripting to calculate complete review cycle durations and determine whether targets were met. That data flows through an automated pipeline into Azure storage and surfaces in Power BI dashboards published to the city’s internal SharePoint sites, refreshing approximately every 30 minutes.
What KPIs does Stockton track for its permitting process?
Stockton tracks performance against published review timeframes in both calendar and working days, accounting for weekends, city holidays, and its 9/80 work schedule. In addition to historical KPI dashboards, the city runs live workload monitoring dashboards that show active reviews in progress, who holds each task, and whether any are at risk of going overdue.
What results has Stockton seen from its data-driven permitting approach?
The improvements have been substantial. For new commercial construction projects, Stockton’s on-time rate for initial reviews rose from 17% (2018–2020 baseline) to over 85% in the most recent six-month period. For multifamily residential projects, the on-time rate went from 3% at baseline to 100% over the past six months.
How has permitting data changed the way Stockton’s departments work together?
Before the dashboards, individual reviewers and divisions only had visibility into their own portion of the workload. Now, everyone across the organization sees the same operational picture. Supervisors can identify risks earlier, coordinate across teams, and rebalance workloads before delays compound. The city has also used data insights to implement policy changes, including direct applicant meetings for projects entering a third review cycle.
What advice does Stockton have for other cities looking to build permitting KPIs?
Rob Liddicoat recommends three things: standardize workflows before building metrics (consistency reduces calculation variables); start with operational questions; and validate constantly rather than building and walking away. He also encourages agencies not to wait for perfect conditions, as the process benefits from iteration over time.
How does Accela’s platform support performance management for permitting agencies?
Accela’s enhanced reporting database gives agencies access to detailed workflow history data that can be used to build custom KPI calculations, workload monitoring tools, and performance dashboards. Its open architecture supports connections to external tools like SQL environments, Azure storage, and Power BI, enabling near real-time operational visibility across an entire permitting organization.
Full episode transcript
Noam Reininger: Welcome to the Civic Innovators podcast. This is where we talk to government leaders that are transforming how services are delivered and improving the experiences for residents. I’m Noam Reininger, the CEO of Accela.
Joe Morris: And I’m Joe Morris, Chief Innovation Officer at eRepublic. Today we’re headed to Stockton, California, a city focused on strengthening service delivery, improving transparency, and using data to guide decision-making across all of its operations.
Noam Reininger: So the city of Stockton has a permitting process like a lot of other cities, a lot of handoffs, different departments, and historically, it’s been very hard to get an accurate understanding of why the lead times were happening, when things were stuck, and when leadership could intervene. So today we’re joined by Rob Liddicoat. He’s the Deputy Director of the City of Stockton Community Development.
Joe Morris: Rather than relying on anecdotal explanations, Stockton leaned into data by using Accela’s enhanced reporting database to analyze workflow history, build real-time dashboards, and create a clearer picture of how work actually moves through the system.
Noam Reininger: So today we’re joined by Rob Liddicoat. Rob is the Deputy Director of Community Development at the City of Stockton. And we’re going to jump right into the big question. So the big question today is how can a city leverage existing data that it already has to really understand processing times, understand where things are stuck, and understand where it can intervene, ultimately to get a better experience for constituents. Now, Rob, in terms of Stockton, maybe take us through what prompted the shift from using anecdotal data to true workflow KPIs. So how did you get there and what were you looking to accomplish?
[02:49]
Rob Liddicoat: Yeah, thank you for having me on. Well, historically, and I suspect like most agencies, we’ve been relying a lot on assumptions and subjective explanations about our performance. And if there was a complaint about a specific permit, it was easy enough to look into that one particular incident to figure out exactly what happened. But that’s really a far cry from telling you how you’re performing overall. And it really keeps an agency in sort of a reactive type of position. So a few years ago, when we added the enhanced reporting database, we were really excited to tap into our data to start seeing what we could do with it, because we knew that the raw information that we needed was in there. We just needed to get in there and figure out how to unleash it, so to speak. So we knew that we wanted clarity about our performance. But we also wanted better tools for our supervisors to be able to manage their teams and manage the projects that were in their queues. And that was a really important point for us because there’s usually several reviewers involved in a project and a single missed review will cause the entire project to miss its target.
So back in late 2020, we really started digging into our permit processes. We wanted clarity about where our sticking points were, where we needed to focus our improvement efforts and how we could verify that those improvements were actually having the intended effect. We knew we had a lot of raw information in Accela already, especially the workflow history data, but there wasn’t a readily available way to turn that into actionable operational visibility. And honestly, and this was super frustrating for me, I asked a very straightforward question like how long does it take to issue a permit? Very straightforward question on the surface, but we couldn’t provide a straight answer to that, and it just wasn’t acceptable anymore.
Now, of course, that type of a question, there’s a lot of nuances. What kind of a permit is it? How many review cycles did it go through? How long did it take the applicant to resubmit? Those are all very valid points. But the raw data to be able to answer those types of questions was in the system. We just needed a way to turn it into quantifiable information that we could really use.
Joe Morris: You hit on this multi-department, multi-review kind of reality, but you also hit the nail on the head in terms of one bottleneck stops the whole process. Before you had that visibility that you just spoke about, what was really difficult in understanding where that permit may have been getting stuck and how did that affect coordination across teams?
[05:21]
Rob Liddicoat: Yeah, prior to this development, we could really only rely on the perceptions and assumptions as to the cause of the delays in general. We just needed that broad picture. So we did an initial baseline performance analysis and what we found was really eye-opening and it really drove home the principle that we either succeed or fail as a team. So for example, if there’s six reviewers on any given project and five of those reviews are done in a week, but then that last reviewer took a month for whatever reason, well, then the project itself took a month because that’s what the customer experienced and that’s what drove that project’s overall timeline. You know, our customers don’t experience departmental metrics. They don’t care about departmental metrics. Their experience is with the city as one entity from the time they submit their plans until they either receive their approval or a complete set of plan review comments back. We also found that there was no single reviewer division that was the cause of the missed reviews. Everybody had misses here and there, but the thing is when you look at the performance holistically, if one division misses these two projects and then another division misses those two projects, well, overall that adds up to four missed projects for the city. So having visibility into the data really allowed us to identify those pinch points throughout the process and led to much more informed operational discussions about where improvements were needed and what solutions could actually address those issues.
Noam Reininger: Wow, so Rob, from what I understand, you got pretty hands on with the data and you built custom calculations. This was not just import and move. What was that process of really understanding the workflows and then transforming the data to actionable insights?
[07:11]
Rob Liddicoat: Sure, as we started to develop our KPIs, we realized very quickly that the raw workflow history data doesn’t itself tell the whole story. It can tell you how long individual reviewers or divisions are taking, but what we wanted to measure was that full review cycle because ultimately that’s what drives that customer timeline. So we started by building our calculations based on workflow history data pulled out of the ERD. And through some trial and error, we ended up using SQL-based scripting to measure those complete review cycles and to tell us whether or not we met our target or not.
And like most agencies, our timeframes vary based on what type of project it is and what review cycle it’s on. So getting our durations there was relatively straightforward in terms of calendar days, but we also wanted to understand our performance against the city’s working days. And so through that trial and error, we were able to get the scripting to account for weekends, the city holidays, and then even our city’s 9/80 work schedule with alternating Fridays off. So we were able to bake all that into the scripting. So we have really two sets of durations that we can look at.
Now the one thing that we realized was that fortunately we had already spent a few years standardizing our workflows and that actually became very important to this effort. The more consistent your workflows are, the easier it is to build reliable performance metrics because it reduces the amount of variations that the calculations need to account for. We also spent a lot of time validating that the KPI calculations were producing accurate durations in both those calendar and the working days and confirming if the target was met. Many iterations and a lot of diligence to make sure that the data being produced was reliable. But as a result of all that work, today we have a data pipeline that actually works really well. It starts by automatically pulling the data out of the ERD about every 30 minutes, we have it on schedule, and then it processes it through our SQL environment, and then it pushes both the raw data and the new KPI data out into our Azure storage, and then our Power BI-based dashboards pull that updated information out of Azure about every 30 minutes, and then those dashboards are published out through our internal SharePoint sites.
Then in addition to the performance dashboards, we also have live workload monitoring dashboards that show current reviews in progress, sort of who’s got the ball on any given project at any given time, are they overdue, things like that. So we’ve really been able to make the shift from relying on the manual ad hoc reporting to near real-time operational visibility across our organization.
Joe Morris: It’s like you have a treasure trove of visibility and insights that are powering this. You mentioned the concept of who has the ball. How has that changed how your departments are working together and maybe holding each other accountable?
[10:09]
Rob Liddicoat: Yeah, that’s really been probably the biggest operational change, having those workload monitoring dashboards. So those are based on the active workflow tasks in Accela. Those dashboards provide everybody across the organization with visibility into that same workload picture. And that includes all the pertinent details that the team and the supervisors need. So before this level of visibility, individual reviewers and divisions, they really only saw their own portion of their workload. And now everybody can see how their piece of the review fits into that broader process and it’s fostered a much stronger sense of ownership and collaboration in between the divisions and the teams. And then let’s just say in terms of performance, visibility tends to be a very effective motivator. So this has really reinforced the point that we succeed or fail as a city team and not as individual contributors.
It’s also improved coordination because supervisors can now see where a project stands across other review groups and that gives them the ability to coordinate with those teams earlier. So those dashboards have really created a shared operational awareness that just didn’t exist before.
Noam Reininger: If we explore that further, so you’re getting insights into potential delays a lot earlier. And you mentioned that now different departments can see where something may be stuck across the entire life cycle of the permit. How has the operation itself changed? Are there more meetings where you’re looking at things holistically with the department heads? What’s the operational cadence been like so that you can swarm, get ahead of things, and then also potentially reallocate resources to make sure that you hit deadlines?
[12:02]
Rob Liddicoat: Yeah, that’s a really good question. So I think the biggest operational changes really come from those workload dashboards specifically. It really gives the supervisors and the teams one holistic snapshot, a very clean snapshot of everything that’s coming through the pipeline. And so what that does is it allows our supervisors and leadership to see those workloads and then intervene before delays compound and affect the customers. So we can now identify those bottlenecks before they become delayed reviews and also flag projects that might be at risk of getting stuck in those extended resubmittal loops. Prior to this, those issues were really discovered after deadlines had either already been met or a project was in its fourth, fifth review cycle or in some cases escalated up into the city manager’s or the mayor’s office. And at that point, you’re really just reacting instead of proactively managing the projects.
So one of the biggest things is that the supervisors can now look at workload balancing. So if a supervisor sees a large blocker review coming due at the same time for the same reviewer, or if somebody has upcoming leave scheduled, then some of those assignments can be reassigned to redistribute the workload across the team.
I think another thing is that we enacted a policy change based on observations working with the data early on. Looking at the review cycle counts, it became clear that if a project was going into its third cycle review, then chances are it needed a higher level of attention and support to avoid getting into that fourth, fifth, even sixth, sometimes seventh cycle review. So we actually put a policy in place where our team will set up a meeting with the applicant if something’s going into that third cycle review, to understand what’s driving the repeated resubmittals and hopefully get that project back on track.
Another important shift is that the conversations really are now based on measurable data instead of assumptions. So instead of things like, well, it feels like we’re understaffed or it seems like another division is causing us delays — we can just look at the review volume, the turnaround times, the backlog trends, and we can have much more informed data-backed operational discussions and decision making. So these tools have really provided us that clear information to shift away from the reactive explanations and move into that proactive operational management based on cold hard facts.
Joe Morris: I love the cold hard facts, but I imagine as you work towards getting those, there had to be some trial and error, a lot of learning. What did going through that process teach you about building those KPIs and the dashboards so that people not only use them, but trust them?
[15:02]
Rob Liddicoat: Yeah, definitely. A dashboard can look great, but it doesn’t matter at all if people don’t trust the numbers that are behind it. It’s not going to change operations. So through that process, it really taught us that repeated validation is critical when you’re developing those KPIs from the workflow history because there’s variations in the workflows and how they might be used. So we spent a lot of time testing the logic against real projects, making sure the numbers reflected what was actually happening on the projects in real life. And if something looked off, we were very deliberate about figuring out what the root cause was and making the adjustments we needed to account for it in that background scripting. So for example, we found a few scenarios — things like permit revisions and reissuances where some of those nuances in the workflow history were producing inaccurate duration calculations. So they were throwing our KPIs off. So we had to dig in, figure out what was going on, and then update the script to account for that properly.
Another big thing, and this was huge for some of our teams in particular: we ran the new dashboards, especially the workload monitoring dashboards, in parallel with existing Accela ad hoc reports for several months. And what that did is it allowed staff to compare the results side by side, especially as new projects came into the pipeline and existing projects evolved through the process. And that side by side comparison allowed the team to build confidence that the new data and the new dashboards were operating properly and that they could actually rely on it to manage their workload.
I think another important lesson is you don’t just build this and then walk away. Workflows evolve, and as those happen, the calculations will need to evolve with them. And you’ve got to continue paying attention to the data. And if you see something that looks a little off, you may need to dig in, understand what’s going on, and then fine tune it to account for some of those nuances over time. So ultimately, building trust in that underlying data was just as important as building the trust in the dashboards themselves.
Noam Reininger: So in terms of building trust, I’m assuming this all had great outcomes for your customers and that built a whole lot more trust in the department and the permitting process. So can you take us through what the outcomes were after you went through this work, you implemented the KPIs, you then had operational changes. What does it look like now for constituents and customers?
[17:42]
Rob Liddicoat: Yeah. And just to be clear, it’s an ongoing evolving process. We kind of got over that big initial hump, but there’s definitely still a lot of work going on and planned for the future.
But really, that’s what it’s all about — creating a more predictable and reliable experience for our customers and also holding ourselves to a high standard of performance. So as you mentioned, we had implemented several process improvements over the last several years. It was kind of difficult to isolate the impact of any one of those changes. But what the KPI dashboards have allowed us to do is measure whether those collective improvements are improving performance in the right direction.
And it definitely has. The results have been pretty significant. So just a couple of examples. When we ran our original baseline performance analysis, we looked at projects between 2018 and 2020. And for new commercial construction projects, we were only meeting our initial review targets about 17% of the time. Whereas now, over the last six months, we’re either meeting or beating our published timeframes a little over 85% of the time. And then for multifamily residential projects, we were meeting targets only about 3% of the time to start. Whereas in the last six months, we’ve hit 100% for that period.
Another big one is that we can now distinguish between the city’s review time and the time that’s driven by customer resubmittals and responses. And so that helps us have a more useful and honest conversation with our customer focus groups about that full permitting timeline. What pieces do we control? What do the customers control? And then how can we work together to keep the project moving and get that permit issued as fast as possible? We know speed is important and that’s always our goal. But one thing that we really focus on and strive to do is give our customers consistency and reliability and meet their expectations. If we publish that a project should take 15 days to review and get back to them, then that is absolutely our intent to stick to that 15 days or less. So really, the better visibility and internal coordination has helped us align our actual performance with our published timelines, and that has definitely benefited our customers.
Joe Morris: Those are big wins that you just shared with us. For cities that maybe are in the same space that you were when you started and looking to walk the same journey — what’s the one piece of advice that you’d have?
[20:56]
Rob Liddicoat: Well, I would say take up some form of meditation, any form, that way you don’t end up throwing your computer out the window, especially when you’re working through your KPI development. No, but seriously, I would definitely suggest agencies tap into their data because for one, it can reveal a lot of those cold hard facts and it really helps you focus your process improvement efforts. And once you have access to that data, the information is what it is. And so those conversations about staffing levels, about performance, those become so much more productive when you have that level of clarity. And it also gives your supervisors the visibility they need to effectively manage their workloads before a delayed review becomes a city manager complaint.
So I would definitely encourage agencies to standardize their workflows as much as possible, especially around the review tasks — that will help them develop those KPI calculations a lot easier. There are fewer variables to have to account for. And another important lesson is don’t build a dashboard just because you can. Just because you have Power BI and you can put something together that looks really cool, if it’s not answering an operational question, then it’s not useful. So start with those operational questions first. Where are projects getting stuck? What is the customer experiencing? How are we performing as an agency, not just as individual departments? How are we meeting our goals? Where can supervisors and teams benefit from earlier and broader visibility? If you start with those types of questions, then the dashboards become really valuable because they’re solving real operational problems.
And lastly, just validate constantly. Don’t build it and walk away. You have to keep your eye on it, especially for the first year or so, because you’ll see little tweaks that may need to be made here and there. So just keep an eye on it and jump on in. Don’t wait for things to be perfect. Just iterate over time.
Noam Reininger: Rob, thank you so much for spending time with us today. I think your KPI data journey is extremely inspiring and insightful. And frankly, it shows that you don’t really need to wait to start. You need to get in the data and get your hands dirty. But ultimately, what you showed was that with the right level of insights, you were able to make pretty material operational improvements that resulted in better processing time, less errors, fewer deadlines missed for constituents and customers. So really, really insightful story. Thank you.
Joe Morris: And I think it also shows visibility can become a service in itself. When staff knows what’s happening, leadership can act earlier, and whether it’s the customers or the residents, they get clearer expectations and more predictable turnaround timelines.
Rob Liddicoat: Absolutely.
Noam Reininger: So until next time, keep asking the bold questions.
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