Ryan Craaybeek:

Today, we’re on the phone with a number of Project Control professionals, cost engineers, schedulers, etc. You are the experts within the industry; you understand the data, the output, and the insights that you desire, and maybe even how to get there with the data you have. But in some cases, having that industry expertise just isn’t enough. Because with an analytics solution, there’s also technical expertise that needs to be involved, as it relates to implementing an analytic solution, such as setting up a common data environment, a data warehouse design, etc. 

There’s a lot that goes into an analytic solution from a technical perspective. So, in many cases, maybe there’s a lot of industry experience. But on the technical side, sure, you have your technical team that keeps your core systems operating, up to date, and performing well. But maybe there’s not that big data analytics expertise in-house. So, again, absence of subject matter expertise, and both are needed.

Karl Vantine:

And Ryan, before we move off that one, I know you’re going to talk about solutions, and ways to overcome these challenges, but in my experience with analytics, dashboards, and integration of data between systems, one of the great challenges, as Ryan pointed out, is not just the absence of SMEs. Maybe you have both of those SMEs, the industry and the technical, and they don’t communicate with each other, they don’t show up in the same meeting, they’re not teamed together correctly so that they can leverage each other’s expertise. And we see it everywhere we go: the technical person pushing one agenda, the functional thinking something different; they’ve got to be together.

Ryan Craaybeek:

Yeah, and I appreciate that, Karl, because you actually stole my thunder from the next slide, which is totally fine. So what you just mentioned is exactly the point of another common challenge, which is really a disconnect between the different teams or lines of business. That’s a big one, as well as maybe taking on an initiative without executive support or executive sponsorship. But also, to your point, Karl, it can’t be a singular focus, where one team has their idea and desires but goes on trying to do it without connecting with the other needed areas of the business. So again, it’s a big challenge.

So, we’ve gone over a number of different challenges as it relates to data governance, data quality, absence of subject matter expertise, as well as disconnect, maybe through different teams of business. But what are some of the things that organizations can do to overcome those challenges and increase their success probability in implementing and getting the business outcome they desire? Through a capital project portfolio analytics solution? Let’s talk about that.

So, one, have a plan. It’s a project. Manage it like a project. One of the things that comes to mind is executive sponsorship. I mentioned earlier that I’ve been in the E and C space for over 16 years, and I can’t think of one engagement, or client engagement, where they did not have executive sponsorship. The successful engagements always included executive sponsorship. Now, did I work with some clients who came in and they had a plan that didn’t have executive sponsorship? Yes. Did they succeed? Not all of them. There are a number of reasons behind that. But, again, having that plan, approaching it like a project, including having a core team, having a core team that takes on the responsibility of driving this project, and making sure that they’re including the other areas of the business because again, an analytics practice spans more than just a single business unit or line of business.

Also, think about the end goal, what a meaningful analytic solution looks like for the organization. So having that vision, whether it’s mocking up desired dashboards that can provide perspective and value to the different areas of the business such as executive dashboards versus maybe cost engineer, performance management dashboards. So, having that end state in mind, but also relating it to the business itself, what is the desired business outcome? What do we want to get out of this? So, tying those two together, and making that part of the plan. 

And I put the current state assessment. So what we mean by that is understanding where you sit today. We talked about having tons of data. We talked about governance in the form of processes and data sources and silos. Well, what does your landscape look like today? How many data sources do you have? How are you capturing that data? How are you organizing? How are you formatting, etc? So, having a candid, honest look at where you sit today is going to give you a ton of benefits as you continue to plan and design out and take on this project.

Karl Vantine:

So we did a poll. And we asked folks what the reasons for failure were in their experience. We listed a few options. But one of the options that folks added, and it got quite a lot of votes, was organizational change management, and people resisting the new solution. So since this is a controls community, I know that when we say we have a plan, you often think of the schedule, and you think of the cost plan, and you think of the scope of work, make sure that that plan includes the organizational change management plan as well. 

So you’re describing the value not just to the business but also to the individual players and what folks are going to get out of it at their various levels. So if you’re implementing a solution, a software solution, an analytics solution, everyone needs to understand how it helps the business and where it creates value, as well as how it helps your individual roles and where it makes you more efficient, then people will buy in. And so that’s a big part of having a plan.

Ryan Craaybeek:

Excellent, thanks for that, Karl. So, next we’ll discuss data management. We’ve talked about governance and data quality, so establishing what we refer to as a common data environment is crucial. I’m sure many have heard that term. Having a common data environment acts as a single repository to aggregate all the data, then organized based on a data warehouse design. That’s all about the format and organizing of the data, ensuring there’s consistency within it. So, it is ready to be leveraged and capitalized on. This all comes down to data governance. So having standards around how it’s being captured, or standards that drive consistency in how it’s being captured and formatted, then it can feed into the common data environment, as well as fit within the data warehouse design that’s been set up.

This provides the foundation to bring in data from other data sources. Having a common data environment doesn’t mean that all data goes there and nowhere else. Data can originate in another system or data repository, but we want to get that data out and into the common data environment. This happens through mapping the data, as well as integration or data exchange, also known as ETL—Exchange, Transfer, Load—which is more lingo in the analytics world. Again, data management is key. Having a plan around that and understanding the components behind the data management process, common data environment, mapping, etc., is essential.

Then there are the dashboards and the analysis. When we all think about an analytic solution, the first thing that comes to mind is the pretty pictures and the ability to drill down. Well, there’s a lot of work that goes into the design of that. What is the desired output? Going back to having the plan and thinking about what each line of business cares about. What would they want to see in a dashboard? What about this particular role, whether it’s an executive, or a project manager, or a cost engineer, etc.? Thinking through that, designing it.

I’m working with a client today, and on our first call, we discussed how they have mocked up dashboard designs, using them as a means to reverse engineer how to get there. Do they have the right data? Where’s that data going to come from? Design is key to establishing those dashboards from an analysis perspective. The analysis is the user experience. Being able to drill down, filter, and slice and dice, engage, and interact with the dashboards to go through a number of different experiences. 

A lot of folks like to do root cause analysis—they see an indicator that causes some concern, but they’re able to drill down and slice and dice using the dashboards or the analysis capabilities within the dashboard to get to the root cause analysis or answer to their question.

And then, you have to build the dashboards. This goes back to subject matter expertise. Training, as Karl mentioned, helps leverage and connect all lines of business. This will drive the change management practice within the organization, including the training, go-live, and role of the organization. We have a number of different recommended best practices, ones that we’ve seen that are part of a successful rollout. And, of course, there are probably several others, but these are the ones we emphasized today.

So where we’d like to close out our presentation is talking about the different options. There are various options for organizations looking to establish and implement a capital project portfolio analytics tool, and we’re going to emphasize two today.

So, one option is the famous DIY—everyone loves DIY these days. Sure, you can take on the project yourself as an organization and build out an analytic solution. But I’d like to use the analogy of the iceberg. What we see, maybe it’s not the best iceberg, but it’s the tip of the iceberg above the water. It’s what everyone sees when they’re in their boat. However, when we relate it to analytics, as I mentioned earlier, we think about the dashboards. It’s the tip of the iceberg. What a lot of people don’t think about is what’s underneath. How do we get to those dashboards? A lot of what we talked about today—the common data environment—are all pieces of the puzzle to establish an analytic solution within your organization.

This starts with the data warehouse, also known as the common data environment. What about the data warehouse design, setting up the different tables, subject areas, measures, and facts, all of which that design enables the analysis, behavior, and user experience behind the dashboards? Being able to put that design together, what about data mapping and integration between different data sources and your common data environment? 

After that, we have our common data environment set up. It’s organized within our data warehouse design. We’ve got all the data exchange methods to bring the data in. Okay, cool. Now, let’s go ahead and start building dashboards. What BI tool are we going to use? Power BI? Tableau? There are a number of different tools out there. After that, we have to go ahead and design and build the dashboards and then go out to train and roll out.

So, that’s the picture here. There’s a lot that goes into rolling out an analytic solution. Now, Karl and I have worked with organizations where they have used or have gone this route and have done it themselves, and they have been successful. It can be done; it has been done. And one point that we have recognized is that it’s a large-scale project. It requires a lot of resources, a lot of money, or budget, and it takes time. That is one of your options.

Now, the second option is an out-of-the-box analytics solution, and that’s what we offer here at Contruent. Contruent Enterprise has an analytics solution that is industry-specific. It’s pre-packaged, which means all of those pieces of the puzzle you saw earlier, well, we’ve already done the work. And that is going to help from a time-to-value perspective.

Let’s dig into pre-packaged and what that means more specifically. Contruent Enterprise is a common data environment built on a data warehouse that already has the data warehouse design built out. It’s specific to the industry. So when we think about subject areas, measures, and facts, it’s all in project controls and project management terminology. There are a number of advantages or benefits to that, especially when it relates to building out dashboards. Again, a prebuilt data warehouse design.

In addition to that, we have pre-mapped and integrated data into the common data environment. We support the data capture of a number of different solutions. The bottom line is that we bring that data in through pre-mapping and integration. So now that we have all of this data organized within a common data environment, we’ve also pre-built dashboards. We’ve done the design and the build. It’s specific to the project controls and project management world, not only from a project-specific perspective but a portfolio. So, being able to gain insights by leveraging the most recent data captured within the data, common data environment, and looking through things such as executive dashboards, enterprise dashboards, drilling into individual projects, or areas, etc. These pre-built dashboards offer that instant benefit and insights, as data starts coming into the common data environment.

In addition to having pre-built dashboards, we offer the opportunity for you to build your own dashboards. So, what does that mean? Well, let’s go back to subject matter expertise and the BI tool or the authoring tool we use in our analytic solution, which is Microsoft Power BI. We use this tool to build out these pre-built dashboards, but it also empowers an organization to say, “Hey, these dashboards are great, but there’s other data in there. And we want to use it this way. Let’s create our own dashboard.” So if you have folks who know how to use Power BI, understand the lingo, and remember the data warehouse design is all around project management and project controls specific terminology, you can be empowered to build your own dashboards and add to the solution.

And then lastly, when rolling out the solution and going live, we’ve got a team of industry experts that can help train, rollout, setup, and get you up and running in an expedited fashion to gain those insights.

Alright, before I close out, did you have any comments on the last couple of slides that I went through?

Karl Vantine:

Yeah, I want to throw something in. Actually, while you’ve been talking, there have been some questions on the side. And one of them, I think, relates nicely to what you just described about the out-of-the-box solution. Guillermo asked a question about how to get started and mentioned the level of digital maturity in his region—his company is writing from Peru—is lower. One of the great things about an out-of-the-box solution versus doing the custom build, or the “do it yourself,” as you phrased it, is that doing it yourself takes a lot of people getting together and achieving consensus on how you’re going to do it. It’s a lot of meetings, a lot of opinions, a lot of back and forth. And you often never get to an answer because you spend time in that creative process, and it just sometimes stalls these initiatives. 

Whereas with an out-of-the-box solution, you get everything in; you start to generate results. And I’m fond of saying that it’s easier to red pen and create. So, you get the initial results, you start producing output, and dashboards, and views. Then, everybody will have an opinion about what needs to be changed, what needs to be fixed or updated, or what colors to change. That’s easier to deal with and move forward more quickly and get to a goal line than when everyone’s still sitting around a table in a do-it-yourself approach and trying to design from scratch. So, I just liked that question, and it related very well to what you were talking about, Ryan. I wanted to call it out. And I know there are other questions we’ll get to in a minute.

Ryan Craaybeek:

Yeah, excellent. Well, the good news is we are just about a minute away from taking questions. So I’d just like to take a second to summarize and quickly go through what we covered today. The message is there’s a lot of data out there, and project-intensive organizations are trying, well, one, they see the value in the data, or they see the benefit in turning that data into meaningful information. 

It’s being realized that they need to take on these initiatives and try to capitalize on that data. And unfortunately, to this point, the success rate has been low. But there’s a lot of lessons learned there. And that’s where the best practices come from. So, not only around the best practices, but again, weighing those options, do it yourself, or looking at an industry-specific prepackaged out-of-the-box solution that provides a number of different benefits, and really around expedited time to value.

So, I want to thank everybody for joining, for listening, for entering questions in chat. Karl, if you have any closing words, or we can just get right into the Q&A and start addressing some of the questions in chat.

Karl Vantine:

No, yeah, other than just thank you for your attention so far. There are some questions floating around in the chat box now.

Ryan Craaybeek:

Alright. So yes, folks, if you haven’t asked a question but there’s something on your mind, please enter it into the chat box. Karl and I will go ahead and do our best to answer those questions.

Karl Vantine:

We have one from Jose Morrell around providing cost factors associated with onboarding the tool, training, and ongoing management. If I’m following the question correctly, I’m not sure if we want to get into specific costs, other than maybe just to mention effort. The effort associated with standing up a do-it-yourself environment for common data environments and for analytics can be pretty significant. It often involves the company. Some consultants are regularly engaged, and you may have quite a lot of teams involved in doing that design and starting to produce results.

It’s much less significant when you deal with an out-of-the-box system. Or a system like we’ve presented with Ryan’s slides, and you give us the raw data, we can get companies up and running often in four to six weeks. Even more complex mega programs we’ve been able to roll out in 10 to 12 weeks, and that’s a much more focused exercise and a more focused team. When you think back to the subject matter experts engaged, the IT groups, the functional expertise, and the SMEs that Ryan mentioned earlier, you can keep it much more streamlined and focused when you start with an out-of-the-box system and then make the tweaks as you go.

There’s a question, since I’m reading them as they come in, about how you approach the transition from manual processes, Excel spreadsheets, Word documents to digitization so the data is available. Can you scan documents? We can scan documents; we can import the information. Excel is a brilliant tool, but it’s a single-user tool, as most folks know. So we just suck it in through import engines and bring the data across that way and put it in a proper framework where then it’s open to multiple users to access. 

How can industry companies’ specific data analytics requirements be captured by system capability? Let’s see if I can understand that question. I don’t know. By the way, if we have to stay with the chat, or are we allowed to take folks off chat now?

Moderator:

Yeah, we have a few more questions coming in. We’ll wait for another two or three minutes.

Karl Vantine:

I’m not quite sure I understand the last question: How can company-specific data analytics requirements be captured by the system capability? If that means, can you take my comment about “easier to red pen and create” to mean you can take the out-of-the-box pre-built views that are generated by our system automatically and then begin to make them more specific to your industry or to your company? The short answer is yes, absolutely. The coding structures applicable to various industries are all configured according to your needs and driven by your requirements, things that are specific, you’ve had reports or analytics that are specific to your business and your way of approaching projects over time. All of that can be layered in; it’s quite quick with some of the tools we have around analytics and reporting so we can help you customize those things quite quickly. But none of it requires any software engagement or software development. It’s pure configuration. So it’s a very simple, quick process.

Yeah, there’s a question. One last question came in while you were doing the final closing. Thank you, but just to confirm, do we have another team that can assist with implementation and training worldwide? Absolutely. We do have a global team. We do support our customers 24/7. So in terms of contact information, it’s me and it’s Ryan, and we’ll put those out for you. You can come to our website, contruent.com. We’d be thrilled to show you more. This was a very fast hour, but we’d love to show you more about what our solution can do and how our team could support you. So thanks. Thanks so much for the opportunity.