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Seamlessly Integrating Data into Everyday Construction Workflows

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Description

In construction, project success depends on identifying trends and finding ways to capitalize on those trends to improve workflows. Thanks to the Autodesk Construction Cloud platform's robust Data Connector, this data does now exist—and if it's managed well, it can boost safety, quality, and productivity on current and future projects. Issue arises in identifying these trends and analyzing them in an effective manner. Barriers to entry involve not knowing how to access the data, lack of understanding on how to analyze data if it is available, not continuously following up on the data to understand when changes need to be made, and not creating useful visuals to explain data to internal and external partners. This industry talk will specifically focus on how Autodesk Construction Cloud data can be maximized effectively both on a project level and an executive level. We'll dive into what data and trends typically will show indicators of pending procurement issues, safety issues, quality issues, and design issues, and then we'll explore how to effectively share this info.

Key Learnings

  • Discover best practices for making data part of everyday workflows.
  • Learn about maximizing data from an executive or admin level.
  • Discover important data trends for project teams to track.
  • Learn about empowering teams to analyze data and understand trends.

Speakers

  • Kyle Knauer
    I have been in the industry since 2015 starting as a laborer for a Concrete Company. After graduating college in 2017 I started working for DPR Construction as a Project Engineer. As a Project Engineer I was fascinated with the inner working of how we utilize our tools and technologies to improve productivity, safety and reliability throughout the industry. Working on projects that range from $150,000 to $75M I was able to experience a large scope of different variables in the industry. Once I became a PM, it was apparent that I had a passion for tools technology and improving workflows, so I transitioned into the Integration Manager Role for our SW Region. In this role I connect tools, people and technology together through training, process mapping and collaboration to better our front lines. I have been in this role for about 1.5 years and have grown to see both on a project level and on a national level how our teams operate.
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      Transcript

      KYLE KNAUER: So today I'm talking about seamlessly integrating data into everyday construction workflows. So I'm Kyle Knauer. I'm an integration manager with DPR Construction.

      And just as we start to get into this, I want to give a little bit of my perspective before we get really into the data. So my roles throughout have been-- I started off in project management about seven years ago with DPR Construction.

      I started off with doing apartments. I've done office buildings. I've done some lab space. I've done health care TI imaging and OR spaces, so everything from residential to class A office buildings, ranging anywhere between $120 million and $150,000. So really coming in with the perspective of different levels of difficulty of different projects. Also, coming in with really large projects to really small, technical projects.

      So that was on the project management side. My current role is actually the integration manager with DPR Construction. And what do I do in this role?

      So mostly I start off with projects doing project kickoffs. So these project kickoffs oftentimes are going through all of the workflows, technology, really anything so that that team can be successful as they go through the lifecycle of their specific project. So I'm doing this with all the projects in the Southwest.

      I do all the training, so after a kickoff to really identify, hey, what training does this team need. And specifically a lot of times, it's not necessarily data focused. It's not necessarily even a specific workflow.

      Sometimes, it's just understanding, hey, how does this construction technology work. Hey, is there anything I can do to maximize the way that I go about doing x, y, and z?

      Then I do ongoing touchpoints with the project. So I'm really getting in and understanding where project teams are struggling, where they're doing well. And not only are these touchpoints something that I'm continuously doing with every project of every size, but it gives me a foundation of understanding which projects are doing well, which projects aren't doing well, and starting to identify those trends.

      And the last thing that I really spend a lot of time on is a corporate feedback loop. So after I've done these ongoing touchpoints with projects, I'm then getting with our Construction Technology Team and really making sure that they understand where our front lines are doing really well, where we're struggling, potentially things that our front lines found out and are using that maybe we can bring on a more global scale, or maybe issues and lessons learned that we can bring back on a more global scale and make sure that we don't necessarily do it the same way.

      So a little bit of DPR statistics, so DPR overall, we're a $9 billion company. Currently, we have 8,400-plus employees.

      And I thought this was a fun little fact. But since our inception in the '90s, we've had 16,000-plus projects. So just think about that for a second. We've had 16,000 projects where we have specific data that we could have extracted. We have specific workflows that we could have tested. And really, this is a large pool of different places from beginning to end to test things.

      And in the Southwest specifically where I am-- San Diego, SoCal, and Arizona-- we have 2,000-plus of those 8,400 employees. And just between 2022 and 2023, at any point we had 254 active projects, so a lot of projects going on in these regions.

      So why present on data? So really I do have a passion for making people's lives easier. Really want to make sure that when people go home they can feel a little bit more relaxed. They can feel a little bit more comfortable what they're doing. And data is a great spot where not a lot of people spend enough time. And I believe that through understanding and being able to leverage data, it'll just make people's lives just a little bit easier.

      I also have a want to make it go from a BHAG to manageable. So what is a BHAG? This is a term we like to use at DPR that stands for Big, Hairy, Audacious Goal.

      So really, we want to take something that just is like, oh my gosh, how could I even start to wrap my head around. How are we going to make data easy for my company? But something that's manageable to where you say, OK, well, if I start to break this down, if I start to look at the way that this data really ties together, now we're starting to come to a way where we can make this BHAG into something that can actually be done on your project.

      Repeatable industry. So I mentioned earlier, we have 16,000-plus projects. So what better way to be able to say, oh, man, did the data work on this project. Is there a way that we can streamline it on the next one? Then having this constant stream of different projects to be able to test and make sure that we can start to get better and better with every single project that we do.

      And now I'm going to go a little bit into really how to streamline that journey. So I don't want every single person to go through necessarily the same roller coaster on data that I did. This isn't necessarily representative of everybody at DPR, but this is my journey that I've seen and why data is so important to me.

      So a little journey through my data experience. So I'm going to do a little past, present, the near future, and then looking out a little bit more where we're seeing the industry start to go.

      So in the past, I like the word "disconnected." It's not that we didn't have the data. It's not that we weren't using the data, but everything just felt like it wasn't put together right, so different tools across every project.

      So we are not only saying, hey, project 1 might be using Bluebeam Studio, project 2 might be using BIM 360 Field, project 3 might be using Procore, and so on and so forth. So every single project, maybe because a specific PM came onto that project and had a different way to go about doing it, maybe every single PE had a little bit of a different flavor of how they did it, maybe it's an owner requirement, but we really felt like we had some disconnected tools. So we weren't getting the same kind of data every single time.

      And different tools in the same project. You might be on the same project and a superintendent might be saying, hey, I really like to see my punch lists done like this. And a PE might be going, that's great, but I really like to see them done like this. And now we have that disconnected workflow, even within the same project.

      And even when we are using the same tools for the same things-- let's say, for example, we have photos, and the photos are really living inside of something like ACC, but then they're also done, again, in StructionSite. And it's how do we bridge the gap and make sure that we can get away to have all these different tools, even in the same project, a little bit more consistent?

      And that also brings consistency to our metadata, the way that we're actually putting that data into the project. Oftentimes through dropdowns or through just standard practice, procedures, naming conventions, we can start to get more consistent metadata. And in the past, we were happy to just get anything. So now we're starting to get more closer to what we would like there.

      Present. So I would say in the present we're actually pretty connected. We're starting to be more selective with our tools. DPR, In particular, has actually adopted ACC for all of our new projects. And we're getting more and more ACC adoption as time has gone on.

      This process has started in July of 2022. And since July, we've had over 600 projects within DPR starting ACC. That is a lot of consistency that we did not have before.

      And now we can start to see that data relationship forming across projects, 600 projects. Even though not that many projects have gone through a full life cycle in the last year, we're starting to see hey, this project did it like this. And even though it's not done, we haven't had a full lessons learned, we can start to understand how that data is starting to relate to other projects and starting to see some of these trends on a more global scale.

      We're also just seeing more interest in data. With the rise of AI and the rise of data being on the forefront of everybody's mind, now we're starting to see teams actually asking for, hey, my owner is asking for this very specific timeline of when we've submitted things. Or, hey, my architect really wants to make sure that they're staying on top of all of their reports. And this interest in data is really driving a lot of allowing us to streamline it and allowing us to provide better information to projects.

      In the near future, specifically DPR is now trying to streamline that connected data. So we have greater access to this data for all stakeholders. Specifically in ACC through either the data connector or just data extracts, teams can now more than ever have access to that actual data, either for their own projects or across multiple projects.

      And they can start to see how this data is actually forming and looking through ACC Insights to really understand, hey, here are some of the AI things that it's starting to notice. Or, hey, I didn't really think about this, but it's analyzing what the greatest risk for certain issues are. And so this is becoming more and more accessible to just your everyday stakeholders.

      We're also talking about now having consistent visuals and reporting metrics. So through ACC Insights now, you can actually go in and you create, hey, I want to see an Issues dashboard. And it's going to be that same Issues Dashboard every single time.

      Whereas, historically, back when we were disconnected, it was, hey, I want to spin up a one-off metric or one-off graph that I want to show somebody. But now we can start across the multiple projects. And even multiple projects with the same owner, we can start to be consistent with what we're showing them.

      Specifically, again with DPR, we're starting to create what we're calling the International Dashboard or Enterprise Dashboard so every new project has a dashboard that's added to their Insights module through Power BI. And we're setting that up for every single project. So regardless of what project you go on, you can click into that dashboard and know exactly what information you're looking at.

      And also the reporting metrics. So now not only do we have consistent visuals, but now we're creating owner reports or we're trying to figure out what are some of the trends. These are starting to become more and more streamlined.

      And we can then take that information and establish a framework for consistent project execution. We've established specifically at DPR things like the VDC XP, so our VDC Execution Plan. We're starting to spell out, hey, this is some of the metadata that we want to see. Hey, this is some of the framework of how you should build a template. And maybe there's some inconsistencies there, but it's getting closer and closer so that we can build that foundation so that projects can execute consistently.

      And in the future, now we're trying to mesh all that information. So we went from disconnected to connected to streamlined, and now we're meshing. And just for some visuals, but deliver consistent results using data-driven decisions is really the end goal.

      So we're starting to see this trend across projects. We can catch it early. We can make quick decisions based on the data that we're seeing, especially because we know it's reliable. We know that our project teams are always putting in the same kind of data every single time.

      We know that this decision isn't made inside of a bubble and that there's a lot of factors and things playing into it. And then when you go to describe, yeah, this is why we made this decision as a company, it's much clearer to all the stakeholders on, oh, that makes sense, we started seeing this data trend. And the data doesn't lie, it makes it a lot easier to have those conversations.

      So what are the key questions we need to start asking? And sorry if I leave you with more questions than answers. But really, what is data? How are we looking at data? Is data a little bit bigger than maybe what you thought in the past?

      How do you set up a project with data in mind? So when you're setting up a project, how do you build that foundation and understand that you're getting consistent results all the time? What data is important? And who is the audience?

      So really looking into, hey, we have all this data, we have all these templates. But what do we really need to be focusing on? And really who needs to be focusing on it?

      And then, what actions do you take after identifying an important data trend? Are you just going, hey, we need to fix this right now? Or are you going, let's take a step back? Let's go through some analysis. Let's figure out really is this the true data trend, and really trying to play that data versus our emotions.

      Often times in construction, we go quick to emotions. There's often times there's finger pointing, really just trying to leverage data as a way to potentially avoid that in the future and make sure that we really are streamlining and being consistent with how we approach different things as it relates to our data.

      So what is data? So this is the dictionary definition. And I'm going to break it apart a little bit. So the definition is, "facts and statistics collected together for reference or analysis."

      So let's start with facts. A lot of times when people say, yeah, this is how I feel, typically that's not a great way to start a conversation, especially if you're having a difficult conversation or if you're trying to understand why something is failing.

      So let's start with the facts in the data. How are we deriving this? Is it actually from something that is measurable? Is it something that we're consistently getting in a streamlined and consistent manner?

      Collected together. So you might have data like, for example, for us, we have it in some different programs right now. We're mainly trying to get a lot of our data into ACC, but we still do our financials in CMiC. We still do have a lot of our safety and DHS data over in HammerTech. We keep a lot of our photos in StructionSite.

      So it feels like it's disconnected, but how do you get that data to be collected together? How do you make the ties between that data to make sure that it's actually interactive and that they can start to be formed together?

      And now we're looking at reference or analysis. So let's think through this-- reference. So oftentimes, data is collected and it doesn't necessarily need to live on a trend line. It doesn't necessarily need to live in a spot where it's, like, hey, we're really trying to figure out on a global scale where we're going as a company.

      Sometimes, you just want to be able to click on a model element and say, oh, this is door number A01. Easy. And that is part of data that is added to the model.

      Sometimes from data, we're like, hey, I just want to be able to go out into the field and find all of the submittals related to this specific asset. Boom. That is data that you're looking at. And it's just a matter of, hey, how do we connect these ties so that we can always have that kind of data clearly shown through.

      But then also you do have data for analysis. So maybe you do put all of the door assets or all the hardware assets inside of there. But then how do you start to analyze that and say, hey, we're actually ordering a lot of this same hardware set?

      Maybe this is something that we should look a little bit deeper into finding better suppliers. Maybe we need to look a little bit deeper into how we're putting together on the project and trying to prefab some of this. And maybe we need to look at the submittal and maybe there's a way to streamline the submittal process, because sometimes door hardware takes a really long time. Let's look at some historical door hardware and see if there's a way to make it easier for future projects.

      So it's really taking those reference data and making it into something you can analyze a little bit better. And some data does just live as reference and some data just does live more on the analysis side. But it is important to sometimes figure out, when do I need one, when do I need the other.

      So what counts as data in construction? I'm going to give a couple of examples here. They're not necessarily all applicable to every single project.

      And they're not necessarily applicable to a single project or globally. And there's a lot more than this. This is just to get your mind going about what could be out there. What are the things I need to worry about?

      So just ACC data-- anything that pulls from that data extractor, think of that as good construction data that is something that you want to be able to use in the future, something that you want to be able to reference and really understand.

      Financials. No matter where your financials come from or what your milestones are that you're trying to meet, financials is great data that you really want to track.

      Model metadata-- really metadata from anywhere, but I want to hone in on the model because it is something that is becoming more and more prevalent in today's infrastructure and how we're doing projects.

      And employee satisfaction. So I'm going to pause here for a second. When you're thinking construction, you're not immediately thinking, I need to make sure to get all my data on employee satisfaction. Typically, this is seen from the PX side. And typically when we're trying to do team balancing and understanding who should go where, there is some level of this.

      But really think, how do we gain data on? Maybe these teams are more cohesive than these other teams. Hey, we tend to see better data coming out of having this PM match with this PE, maybe, as opposed to a different PM matched with a PE. And then you can understand and put those items together and start to build those connections.

      We are sometimes on different project teams every few years. Are people spiking in their employee satisfaction surveys some years versus others? What are causing those spikes? Is it general market trends? Is it specifically a project they're on, so on and so forth?

      Then the red, yellow, green. So this is something that we specifically do here at DPR on our do work side. And we evaluate a project's riskiness, and it's almost a gut feel. And then we evaluate this gut feel versus the actual statistics that we're seeing.

      So red for us are the most risky projects. These are the projects we need to spend the most time talking about. These are the projects that we need to spend the most time visiting. These are the projects that we have the most of our overhead resources dedicated to to make sure that they're successful.

      Maybe we add a really strong superintendent from our project that's a green to a red project just to help it flow and make sure that it is successful. And so again, doesn't mean a project is failing, doesn't mean a project is not doing well. It's just, hey, how inherently risky is this. And how can we make sure that this project seeps itself into the yellow?

      A yellow project-- this is where we spend a medium amount of time. We probably have some resources checking up on it on a weekly basis. This is a project that the team is put together well. We have a good contract. We have a good owner relationship.

      But we want to keep our eye on it. Maybe there's some potential safety risks that we're not always thinking about that we want to make sure that we stay keen in on, and really just trying to make sure that it's something that we continuously come to.

      A green project is not, hey, we're done with it, don't worry about it. But a green project is actually where statistics and data comes in the most, believe it or not. Oftentimes, we look at these projects and go, man, that project's doing great, don't even worry about that, team.

      But what we actually do is we have this complex scoring system where we go into the financials, we go into the schedule, we go into different things that are going on in the project. And we'll do this for our gut-feel green projects.

      And sometimes we see things that we're like, oh, man, we thought this project was doing really well. But they have a really high score in terms of some of the issues that we're seeing on multiple projects. Let's go ahead and escalate this to a yellow to keep a little bit of a better eye on it until it becomes more of a green. So this is just a fun way to say, this is data, this is something. And data actually then ties back into it.

      So to end with what is data, I'm going to end with a little statistic. Did you know that 90% of spoken statistics are fabricated, according to this made-up statistic?

      So think about that a little bit. We get all the time people saying, hey, did you know this. Hey, did you know one 1/3 of projects go through this issue? OK, not always be the case. It's important to really make sure you understand that you're getting reliable data, that you're looking up things that you hear, and really just make sure to dive into whatever kind of issue, whatever kind of thing that comes up and come up into it with an open mind and not just believe whatever data that you hear.

      So how do you start out getting reliable data that you can then trust? Well, a lot of that is through consistent data collection. So really building a foundation for data collection starts with building out templates.

      And I'm going to speak a little bit more ACC related here. I think everybody that is on this call is probably a little bit more familiar with ACC, but this can be extrapolated to whatever other programs that you're working on or just in general when you're building out a foundation framework for a project.

      So starting off with the concept now of a freedom within the framework. So this is something we say a lot at DPR. So we want our employees to have freedom we want them to be able to be ever forward. That's one of our core beliefs and core values.

      But how do you keep consistent data if they're always changing, if they're always doing the next thing, if they're never doing things the exact same? Well, that's sometimes providing guardrails, but those guardrails are really just to provide a framework. And then they can have freedom between those guardrails to do whatever makes that project most successful.

      So really think about that when you're building out a data infrastructure and building out a project template, whether that's on a global level or specifically for a project, just to allow those guardrails to exist so you can get consistent data. But also, don't try to pigeonhole a project to make it harder on the team.

      So we're going to do a little bit of how do you create that freedom within the framework. And issues is a great example. Sometimes with categories, we're like, hey, we want commissioning to be one. We want quality to be one.

      Let's make a new category for every single issue type. Let's have every single trade partner have their own issue type so that they can track it and do it however they want.

      But sometimes some of these things can be accomplished with just custom fields. So we can stay consistent across projects or even within the same project, always making sure you have the same stuff.

      Great example here is, hey, I want a dropdown for my millwork trade partner. I want a dropdown for my other trade partner. And then that way, when you go to search, you're searching that custom dropdown and maybe not that full category. And then that way, all of the questions are consistent. And then you can start to see trends across all of your different trades.

      And another great one is custom field dropdowns versus text fields. So let's say you're like, hey, I want the superintendent to be able to write in which trade they think is involved and should be able to see this. But do you make it a multi-select dropdown? Maybe you have 40 trades on a project. Do you really want your superintendent scrolling through 40 different trades to find that one so that they can select it scrolling, find that second one so they can select it? Might take way too much time, and it could lead to some fatigue that stops them from filling it out at all.

      The other approach is maybe you just do a text field. Maybe it's not as consistent, but at least you're getting that information in there. So it's that balance between, do you make it harder on people and get great data, or do you make it easier on people and get maybe a little bit less data.

      So you have to be really mindful of what are we really trying to track. What statistics are really important to us? And inversely, it also is a thing. Maybe you have three trades on a job and you don't want to have the superintendent writing in a trade partner's name different every single time. And you're like, just use a custom dropdown so that we can quickly choose and see which ones are related to certain trades.

      Talking through forms, you have that 95% versus 25% approach. So does this form template work for almost every single project, the 95% of projects, or does it work for just 25%. And there's a time and place for both.

      Let's say, for example, for receiving. This form might be, hey, I want them to just have a receiving inspection form. They can ask the same questions every single time.

      Is the trade aware that it's coming? Has it been signed off on, so on and so forth? It can have a signature line. That's something that would work great for the 95%.

      But let's say we have a lockout tagout for a project that's very specific to that project. Maybe you just want it to be on all data centers. And so maybe you develop it to where just it works for that 25%, and then using stuff like the library to actually push it to those 25% of projects.

      So there is this balance between, hey, do you put it to where it works for everybody, or just to it where it works for the various specific projects where it is going to be helpful, or very specific people where it's going to be helpful and you just have to make that balance.

      Assets is another great one. Assets are something that we're getting more and more into at DPR. Recently, we talked about, how do you name assets to make it consistent in your collection.

      We have projects sometimes that have three or four error handling units on a project. Do we want them just calling one asset air handling unit so that we can track them all together? Or do we want them to split it up between all the air handling units? And maybe it makes it to where, from project to project, we can't track it as well. But on that specific project, it helps them way easier to keep track of all those other air handling units.

      So that just recently came up on a project when we're going through that. And I'm like, should we just make it generic. And it's like no, let's make this what works best for your project because it's not that much of a tweak, but it's going to help you out so much.

      And then it's consistent statuses from project to project. When you're going through the air handling unit, maybe more consistent statuses that are always going to be seen by a project manager or a superintendent every single time. Makes a little bit more sense.

      And it's like, OK, I know what it means to change the status from this field to this field, because I understand that this is going to be exactly what it is. And you can have that description and it's not a surprise and they don't have to relearn the status gate in every single project.

      So we set up our template. We figured out where that line is between how detailed or how not detailed to go. So now what do we do? We have that data.

      Well, let's be honest. There is a mountain of data out there. There are so many things. If you ever run an ACC data extract, you will quickly be overloaded by all these different data points. And you're like, I need to go call somebody to help me through this, right. So it's not always super cut and dry for what data is actually applicable to you at that time.

      So I'm going to give a little bit of a way to start to break down that. And we're going to start with a simple question or a simple statement that I actually recently got from a project. They said, hey, Kyle, all of my submittals are late. It always feels like they're coming late. What gives? How can I approach this with the architect and with the owner so that I can start to get more consistent submittal review times?

      And I said, OK, well, let's start through and let's build this out. So is the data correct? First thing I ask a team is check up on the data.

      Is it up to date? Are we using the correct parameters? We might have up-to-date data, but maybe there's sub-jobs that are being pulled in? Maybe it's not actually showing the full scope of all the submittals and you're just looking at crucial ones. Or maybe you're not looking at the ones that are mission critical?

      Check to see if it's pulling from the correct source. Maybe there's a broken link somewhere. Maybe your data isn't necessarily going to the data pool correctly. So just really start to think, is that data correct.

      The next thing you want to do is start to gather the story. So when you're gathering this story, it's a little bit more than just, hey, the data is showing me this. It's, hey, let's go talk to the architect. What are they seeing that's struggling?

      And sometimes we make up stories in our head of why they can't do it. I actually had this with a team last week. I was like, hey, why do you think the architect's not responding.

      I don't know. I keep sending them emails. And they seem to just not care about why their submission. So let's say, OK, let's give them a call. We gave them a call and, sure enough, they were like, yeah, we just aren't sure which ones to focus on. So we've just been doing the ones that come across our plate first.

      And so we were able to start to, hey, they're bombarded. Let's see if we can figure out what the best ones are for them. And so starting to gather that whole story, understanding the full picture is really important before you just throw data in someone's face.

      And then let's take appropriate action. So we use the data, we gathered the story, we painted a picture. But now let's align all of the parties involved and start to develop an action plan. So this action plan could be as simple as let's sit in a room and let's talk through it.

      What are the five whys? Why are we seeing these issues? And then start to develop a matrix of, OK, we're seeing these trends. Let's see maybe if we change this it will help us in the future.

      And once we start to establish what that plan and what we can do in the future is, then it starts to build into lessons learned. So maybe this is applicable to your exact same job potentially in the future. Maybe you go through and the submittals are late again.

      And it's like, OK, well, we already went through this. Let's make sure that we use our lessons learned here so we don't restart all the way from the beginning.

      And does this scenario hold true for other projects? We have so many projects always going on. Why not leverage this to where we can start to make it streamlined across different projects and start to tell people, hey, this is what we're seeing? How do you make sure that you're doing it within these confines?

      Or, hey, we were actually doing this really well. You can share good and bad lessons learned. We had really, really consistent submittal numbers. And maybe this project team that doesn't have consistent submittal numbers is going in and talking to a team that does have them, and they're sharing those lessons learned on what they did right.

      So let's go back to the can't get submittals reviewed on time. And let's break down what we just learned and talk through each of these steps. So the first one when you look at this is, is the data correct. So we're going to look at this.

      The last update was 9/1/2023. Date today is 9/8/2023. So it's close, but you know what, maybe in the last week the architect has stepped it up. Maybe they've done a little bit better. So date is close, but not fully up to date.

      We're also looking at the actual timeline of what we're pulling. I know for a fact this project started back in 2022. So maybe the team's not giving me the full picture. Maybe the architect was crushing it at the beginning, but they just put that slider to this point where they're saying, hey, Kyle, I'm having a lot of issues with my architect. Look at all these numbers.

      But realistically, if we move this back, maybe the architect did a great job at the very, very beginning and then got an influx of submittals later on that they weren't expecting and that didn't have the staff for. So looking at these little things and trying to understand that story and ask the questions so you can then go back and develop that next action plan.

      Now we're going to look at this. We're starting to see a little bit of a discrepancy here between how many submittals are open versus how many are closed. So we see that we're spiking in how many are open. And we're spiking down in how many are closed.

      So maybe we're bombarding the design team way too quickly with submittals without giving them the chance to even review them. We could have sent all of these in July. And then they might be a little overrun and not have a chance to respond to them quick enough for how quickly we're sending.

      So we've understood, this is where our data is showing. But is there any other indicators in here? Well, I put over here, we have 103 submittals that are showing us shop drawings.

      I don't know about you, but usually the majority of our interior construction, which is what this project is, aren't all shop drawings. So I talked to the team and I said, hey, I'm seeing you guys have a lot of shop drawings out there. Why do we have so many shop drawings for this project?

      And they came back and they said, well, for our steel fab, we're breaking it out by each individual unit. And it's like, OK, well, if we're giving a different shop drawing for every individual unit, the design team probably wants to review all of these holistically and at the same time. So maybe we're bombarding them and we need to do a better job of packaging them together so that we can get responses a lot quicker.

      And this resonated with the team. And they're like, oh, yeah, I guess if we did package them together then they don't have to wait for the other ones. And that's just increasing our overall review time.

      The next one that I pointed out to this team was, hey, you have this person that has 13 submittals just in his court. The rest of them have one to four? Why does this person have so many?

      Well, this is our structural engineer. And he's the one that's our biggest issue with not getting submittals back. It's like, OK, well, let's take a step back and let's understand what problems he's running into and what he's doing differently and see if we can maybe provide him some extra help.

      The last thing I looked at with this team is, hey, the design team might be getting back to you a little bit slower. I see that you have 46. The majority of our submittals are make corrections noted. Are they going through and are they having to do a lot of the review process that we should already be vetting for us? Are we just trying to push submittals through? Maybe it's worth looking at a second point and understanding why it's taking them so long.

      And specifically, this team was like, yeah, well, we just had a lot of issues with getting trade partners. It's like, well, we're trying to make this a design team issue when, in reality, maybe we should look back at the trade partners and understand why aren't we getting quality submittals from them and try to better their process.

      So just really understanding, this is our big problem that we're seeing in the data, saying, hey, we have way too many open issues. And now we're starting to see some of the nuances. We're starting to see why we're having so many open submittals.

      Another example here. So I had a team coming to me saying, hey, I'm having some issues with issues. What can I do? So looking through this, I said, all right, well, it looks like from a severity standpoint you just have all of them on here. So you're saying you're having a lot of open issues, but it looks like you're actually taking a photo of every single paint scuff and you're adding it as an issue.

      Yeah, it's going to show that you have a lot of open issues because it's just going to take time to close all of them. So maybe if we move this severity to critical or important and take out all of the non-important issues, maybe this data looks a lot cleaner when you start to build the picture of what do we really need to do next. What do we need to focus on?

      The next thing I looked at with this team is I said, hey, the main people that are actually creating issues on here is the DPR Admin Team and the DPR Project Team. I don't see any trade partners creating issues. Maybe if we empowered our trade partners to do rolling completion lists and being able to track their issues beforehand, then we won't run into this same issue of them being like, oh, DPR just assigned me all of this stuff and I just have to go through my list. But they'll own it a little bit more.

      So we talked to the team about empowering them to download the app to their phone and not just be using spreadsheets or using the PDF printouts to do it. And so now our actual trade partners are getting more and more involved.

      The last one I looked at here is, hey, I would love to understand a little bit better about why we are having all these issues. But you're keeping our root cause on here as blank. So I really can't get a good sense of what's causing all these issues.

      So maybe we need to go back and gathering the story, why are we leaving this blank. Well, it's too much effort to fill out the root cause every time. OK, well, this is something that could really help us. And maybe we identify this as one of the issues.

      So what are the actions? One of the things we talked about with this team is making that a required field. So when DPR is creating it, we make sure to put what the root cause is, and then we can dive a little bit deeper.

      And the lesson learned that we brought back to the business unit was, hey, for new projects we should start to put root cause as one of our lessons learned, or we should start to put root cause as a required field. And then that way we can gather this data across all of our projects.

      So want to look at a big picture one as well. So far, we've talked a little bit more project to project and looking at it and talking about it. So we want to look at some overall trends.

      So a couple of the trends I want to talk here, first off, is, hey, this is what stands out the most to me. We have an average days of close of 58. Realistically, we should be hovering around 10 and 30 should really be our maximum. What is going on here?

      So we start to analyze the data a little bit. First one that we do is we say, all right, our last update was 8/28. It's a little bit old data, but this is still looking at overall trends. So it's not that far off.

      Next thing we want to look at is our scroll wheel for what dates we're looking at. Yeah, our range looks good. But you know what, we're seeing 3/14 is when you're looking at this data tell. So actually, this isn't showing newer data at all. We need to make sure to update this.

      Next thing we want to look at is, all right, so it looks like we're having a huge spike down in the amount of issues closed this month. This could speak for a couple of different things. Maybe we identified a large average trend and we're really just trying to mask close those issues that may have just been outstanding on projects. Maybe our projects aren't doing as good of a job closing as they go and they're just trying to do mask jumps. Maybe our projects have just stopped creating as many issues, which is causing us to close them a little bit, and that can relate to it.

      So there's so many stories you can start to tell yourselves and so many connections. But until you start to dive into it, you really have to understand that.

      Next thing that I show teams is, hey, we have a test job. We have two test jobs in here that are our third and fourth source of data. Man, maybe we really should make sure to exclude our test jobs from this data source.

      The next thing I was looking at with the PX was, look, we have 2.6 to a 1,000 issues that are quality type. This tells me we're really spending a lot of time doing rolling completion lists, doing punch lists, tracking quality on our projects. That's a great sight to see.

      The next one is design. This tells me that the second thing that we're really focusing on, issues-wise in ACC, is really trying to find design issues early and being able to either look at constructability reviews or trying to do what's in black here and focusing on coordination. So there's all sorts of stories you can start to dive out of here. And then you have to take these and look at it on project levels and see if it actually holds true. So these can be on a macro or a micro level for the lessons learned.

      So I want to end today with a couple of tips, tricks, and fun facts. So the first one is really just to empower teams to build their own dashboards. Now with ACC more than ever, teams can actually go in, build their own dashboards. They have the data extractor right at their fingertips through Insights.

      So it is easier than ever. So really encourage you to go out and try to learn how to build a dashboard from scratch. If you click around enough and if you find that data connector in Insights, it's actually really intuitive. So just try to build your own dashboards, try to mess around with the data, see how you can make connections, so on and so forth.

      The next one is utilizing ACC Assets to tie different data elements together. So really think about ACC Assets as a bucket, and you're adding issues to that bucket. You're adding RFIs to that bucket. You're adding every piece of information related to that asset to that bucket.

      And then your statusing so that you can understand how all those items inside of it relate to where that actual asset is in its life cycle. So by using that, it gives you a lot of really valuable data and how everything ties together.

      The next thing I encourage is apply the scientific method. I love to go sciencey. And if you apply the scientific method when you come up with an issue-- I made an observation, we're having a lot of open issues on this project and it's hard for us to close down our quality. OK, well, maybe we need to look at making it easier for people to download it to their phone. Maybe it's a training thing.

      And then start to dive into that and then see, OK, what is our data actually showing us. Are we actually having issues with this data? If we fix and give some more lessons learned and give the why to our team in the field, oh shoot, now they're actually starting to trend a little bit better in our data. So applying that scientific method and that ongoing loop to understand why you have a problem can really make it better on your teams so that they can start to identify those problems early and not just go, oh my gosh, we have a problem, it's a fire drill. And they can start to understand a little bit better why that might be a problem.

      The next thing is tying different data between categories. My favorite one is safety to schedule. Oftentimes, we look at the circular loop of, hey, our safety is affected by our schedule. Our schedule is affected by this, is affected by that. And you start to understand how everything's interconnected.

      So look at your projects. Hey, this schedule got a little bit tighter. Is there also safety trends and issues that we're seeing?

      Hey, this schedule has a significant amount of time for us to actually put tie-ins into our concrete columns. Wow, we saw an increase in safety when we took the extra little bit of time. Oh, this project used a robot surveyor to survey all of the flooring. We built that time into the schedule. Well, now we're not seeing any of our surveyors having safety incidents. So really trying to see which projects have these trends between different data sets and start to connect all of those together.

      A great one, too, I'm talking about Power BI here. I'm talking about ACC Data Connectors. I'm talking about a lot of things that maybe your average superintendent or your average PM isn't going to have the time or want to spend the effort on.

      Honestly, I want to encourage teams to just utilize Excel or Smartsheets for quick data analysis. So you could easily go into an Excel and put quick data points, run a graph off of it, run a couple of charts off of it. So they can just start to see trends themselves.

      It doesn't necessarily take a data analyst to understand trends. And really like to encourage teams to come in and really just try to build something out for themselves. And then maybe they take that and give it to a data analyst and they say, hey, this is the trend I'm starting to see. Can you look at this on a more global scale? Can you make sure that all my data I'm gathering is correct?

      And the last thing I'll leave you with is, did you know Australia is wider than the moon. So you probably didn't know that coming in. And if I didn't teach you anything today, at least you now know that Australia is wider than the moon. Maybe you'll look up at the moon and think about how you can have integrated data seamlessly throughout your construction technology.

      Thank you so much for your time.

      ______
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      We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
      AgrantSEM
      We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
      Bidtellect
      We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
      Bing
      We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
      G2Crowd
      We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
      NMPI Display
      We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
      VK
      We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
      Adobe Target
      We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
      Google Analytics (Advertising)
      We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
      Trendkite
      We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
      Hotjar
      We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
      6 Sense
      We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
      Terminus
      We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
      StackAdapt
      We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
      The Trade Desk
      We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
      RollWorks
      We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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      Your experience. Your choice.

      We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

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      Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.