Most organizations do not have a data problem. They have a data confidence problem.
In this webinar, the Apex42 team explores why workplace data, dashboards, and reports often fail to support confident decision-making across corporate real estate and facilities teams. The discussion breaks down what creates the “confidence gap” between having data and trusting it enough to act, including inconsistent processes, manual validation, unclear ownership, disconnected systems, and misaligned reporting.
You’ll learn how organizations can move from reactive data cleanups to decision-ready workplace data through stronger data foundations, defined ownership, automation, and reliable metrics. The session also includes a realworld example from a global financial firm managing a corporate real estate portfolio, showing how standardized lease, space, operational, and financial data helped reduce manual reporting time and create a single source of truth for leadership decisions.
The webinar also introduces Apex42’s Data Health Score, a new way to measure and monitor data confidence over time by evaluating data quality, data consistency, and system usage. Designed for organizations using space management software and corporate real estate portfolio solutions, this approach helps teams understand where data confidence breaks down and how to improve it with measurable, actionable steps.
Topics covered include:
- Workplace data confidence and decision readiness
- Space management software and reporting foundations
- Corporate real estate portfolio data and lease visibility
- Data quality, consistency, ownership, and governance
- Automation, integrations, and single-source-of-truth reporting
- Utilization, vacancy, cost, occupancy, and workplace performance metrics
- How to move from dashboards to trusted, strategic decision-making
For organizations managing complex workplace, space, occupancy, lease, or utilization data, this session offers a practical framework for turning data into confident decisions.
Full Transcript:
Welcome and Introductions
Amy Jefferson:
All right, welcome everyone, and thanks for joining us. We’re excited to be here and dive into today’s topic: the confidence gap.
The reality is most organizations don’t actually have a data problem. They have a data confidence problem.
Today is all about helping you understand where you are and what it takes to become truly decision-ready.
Before we jump in, let’s start with a quick introduction to your presenters.
Starting with me, I’m Amy Jefferson, and I’ll be your moderator today. I’ve been with the Apex42 team for 10 years, starting as a CSM before wearing a few other hats and then moving into my current role as Director of R&D.
Next, we have Ashley Betthauser, our Director of Sales, who has been with the team for 15 years. She also started as a CSM.
Continuing the theme, we have Chad Rohland, one of our amazing current CSMs, who has been with the team for five years.
Finally, we have Cory Siegrist, our Data Quality Manager, who has been with the team for 10 years. As you may have guessed, he also started as a CSM.
As you can probably tell, client experience is a common thread across our team. It shapes how we approach our work and drives our focus on helping clients make confident, data-driven decisions.
Based on the pre-webinar survey, many of you shared that you are somewhat confident in your data. Today is about helping you move from somewhat confident to truly confident.
Here is a quick look at our agenda for today.
Ashley will kick us off by talking about the reality of workplace data, understanding the confidence gap, and what it takes to close that gap.
From there, Chad will share a real-world example. Then Cory will introduce something new we’re especially excited about: a new way to measure and better understand your data health.
We’ll save time at the end for questions and discussion. We’ll also have a few polls throughout that you can answer directly in Teams.
With that, I’m going to hand it over to Ashley to kick us off.
The Reality of Workplace Data
Ashley Betthauser:
All right. Thanks, Amy.
So, what is the reality today?
You have the data. You have dashboards built. Reports exist. But when it comes time to make a decision, you’re not confident in it.
At first, it may not be obvious. On the surface, everything looks fine.
So what is actually going wrong?
First, there is trust. Can you rely on the data without second-guessing it?
Second is speed. Can you actually act on the data in the moment without going through validation hoops?
Third is alignment. Do stakeholders look at the same numbers and come to the same conclusion?
Even if one of these breaks, the whole system starts to slow down.
This is what it looks like in real life.
Someone comes to you and asks a question. What do you do first?
You pause to understand what data is really needed to answer the question. Then you validate it to ensure the data you have is up to date and accurate. Then you double-check it just to make sure everything is right.
Ultimately, you delay the answer. In that moment, confidence disappears. Not just your confidence, but leadership’s confidence in the data as well.
Naturally, most teams think this must be a data problem. But it’s not. It’s how the data is being used.
You have reactive cleanups going on. Things get fixed only after a question is asked. You are manually validating. Every decision requires double-checking.
Your process starts to drift because there is no consistent ownership or way of managing the data. Then leadership pressure builds. They have one number, you have another number, and you continue to provide numbers even when you are not fully confident in them.
Over time, this becomes the norm.
So what happens next?
Decisions start to slow down. Trust begins to erode. Teams shift into reactive mode. And the gap between having the data and actually using it keeps getting wider and wider.
Before we go any further, let’s take a quick pulse. What we’re talking about next requires us to reflect on where we actually stand today.
I’m going to launch a poll for everyone. It should come up on your screen or in the chat.
The question is: Which best describes your current data environment?
Are you reactive and doing manual cleanups? Do you have some automation? Or are you fully automated and decision-ready?
We’ll give everyone a few moments to answer.
As responses come in, you should be able to see them in the chat as well.
We have about 55% saying they are reactive, 50% saying they have some automation, and no one saying they are fully automated and decision-ready. We’ll keep that poll open so people can continue to respond.
What Is the Confidence Gap?
Ashley Betthauser:
Let’s dive into what the confidence gap really is.
A lot of teams assume, as I said before, that this must be a data problem. But it’s not.
The problem isn’t that you don’t have data. It’s that your data isn’t decision-ready. That is a very different problem to solve.
This is what we call the confidence gap.
On one side, the data exists. You have reports. You have dashboards. You have access to the information.
But on the other side, data is trusted for decisions. You can answer questions immediately. You don’t need to validate. Everyone is aligned on the same numbers.
That gap between those two points is where most organizations are currently operating.
And this is where things start to get expensive very quickly.
Decisions get delayed because no one wants to act on uncertain data. Opportunities are missed because insights come too late. Credibility starts to slip because leadership is questioning the numbers.
Ultimately, organizations end up overspending. They overspend on space, on moves, on build-outs, because decisions are not grounded in trusted data.
This is the hidden cost of “good enough.”
So the natural question becomes: What does great really look like?
This isn’t about perfection. It’s about building the right foundation so everything above it actually works.
This is the model we use to think about it. I’m going to walk you from the bottom up.
At the very base is your foundational data: your space, your people, and your workplace data. It needs to be centralized, complete, and current. This is table stakes. Without this, nothing else works.
Next is data integrity and ownership. You have clear ownership, defined processes, and accountability. This is where you actually start to create trust.
Above that is automation and intelligence. This reduces manual effort, minimizes errors, and frees up your team’s time.
At the very top is where everyone wants to get: metrics, optimization, and strategic decisions, where data doesn’t just inform action, it drives action.
The mistake most teams make is trying to jump straight to the top, where the metrics and optimization are, without building what supports it underneath.
When this foundation is in place, everything changes.
You can answer questions in the moment with confidence, not hesitation. Decisions shift from reactive to proactive. You have clear, trusted metrics around utilization, vacancy, and cost per seat. Teams start to align because everyone is working from the same trusted source of truth.
Most importantly, you are positioned to support what’s next, whether that’s growth, consolidation, or a broader long-term strategy.
This is the shift from simply having data to actually using it.
With that comes something powerful: metric-driven storytelling. It gives you the ability to bridge perception versus reality, which is often the biggest hurdle in space decisions.
Now that we’ve talked through the gap and what great looks like, let’s pause again to take another quick pulse.
I’m going to launch another poll.
The question is: Do you track any metrics against targets today?
Do you consistently track them? Do you sometimes track against some targets? Do you not track against any? Or are you unsure if there are targets you should be tracking against?
Go ahead and submit your response.
It looks like a few people are saying yes, consistently. The majority are saying somewhat. A few of you are also unsure or don’t track against them.
We’ll keep that poll open so people can continue to respond.
How to Close the Confidence Gap
Ashley Betthauser:
Now that we’ve talked about what the confidence gap is, how do we actually close it?
This is a big one because what we typically see is a lot of teams are tracking data, but far fewer have clear, consistent targets tied to that data.
Without targets, it becomes incredibly difficult to turn data into decisions because there is nothing you are measuring it against.
So the question becomes: How do you move from where you are today to being decision-ready?
More importantly, how do you do that without overhauling everything all at once?
The good news is you don’t have to boil the ocean. This isn’t about fixing everything overnight. It’s about taking the right next step and building from there.
When teams approach it this way, it becomes much more manageable.
We think about this in three simple steps.
First, understand your current state. You can’t fix what you can’t see. This is about identifying the gaps, looking at inconsistencies, and understanding where confidence is breaking down today. Clarity comes before improvement.
Second, establish a strong data foundation. This is where you define ownership, put standards in place, and create consistency in how data is managed. This is what actually creates trust in the data.
Third, build toward decision-ready metrics. This is where you define the metrics that matter, align on targets, and start building consistency into the reporting.
The key is that you don’t have to start with everything. You start small and scale over time.
Now we’re going to bring up our last poll. This one is open-ended.
The question is: What is one metric you wish you could confidently report on today?
Go ahead and type in your response.
That could be utilization, vacancy, cost per square foot, or any of the other metrics you are trying to get to.
These metrics are exactly where we see teams begin. Not everything all at once. One or two key metrics they need confidence in.
When you start to get that right, you can build momentum across everything else.
We’ll keep that poll open so everyone can see the different metrics people are hoping to gather.
Instead of just talking about this, we want to show you what this looks like in practice.
I’m going to hand it over to Chad, who works directly with our clients on these types of things every day, to walk you through a real-life example of how this came to life for one of our clients.
Client Example: From Manual Reporting to Trusted Data
Chad Rohland:
Thanks, Ashley.
Hi everyone. I’m Chad Rohland, a Client Success Manager here at Apex42. My role is working directly with our clients to make sure we are working together and actually driving value for their business.
Today, I want to share a story I think will hit close to home for a lot of you.
One thing I’ve seen consistently across clients is that organizations where leadership feels confident, where decisions get made quickly, and where teams are not buried in manual work every month, all share one thing:
Their data works for them.
It’s clean. It’s reliable. It’s repeatable. And it’s actually being used in the room where it matters.
Today, I want to walk you through what it looked like for one of our clients to get there and what changed when they did.
This client is a global financial firm managing a significant corporate real estate portfolio. They had leases and spaces spread across multiple countries, multiple currencies, and multiple team members entering data all over the world.
Every month, someone on their team spent 5 to 10 hours manually pulling spreadsheets together to prepare reports for the C-suite.
Data was being entered inconsistently. Every month, data was reported inconsistently. They were pulling from different sources, trying to reconcile inconsistencies, and attempting to make it tell one coherent story.
The hard part was, even after all that time and effort, confidence in the numbers and reports was low.
Data was being entered inconsistently across global administrators. Exchange rates, region-specific data points, and lease terms were not standardized. And it showed.
Leadership was walking into high-stakes conversations questioning the numbers instead of acting on them.
They weren’t lacking data. They were entering it manually and hoping it was all done correctly. They just didn’t have one reliable way to trust it without double-checking and putting in extra time before they could use it or move quickly with it.
So how did we help as partners?
We stepped in. The first thing we did was not build the dashboard. The first thing we did was fix the foundation.
We sat down with their team and worked through how their data was being entered, where the inconsistencies were coming from, and how to standardize the process going forward.
We identified the gaps and built structure around them because a polished report built on unreliable data is still unreliable. It’s just harder to catch.
Once that foundation was solid, we built an automated BI dashboard that pulled everything together.
Lease data. Custom data points. Data fed through their own APIs. All of it consolidated into one place and refreshed every night.
No more manual assembly. No more waiting until the end of the month to see where things stood. The data was current every morning without anyone having to touch it.
Here is where it gets tangible.
That 5 to 10 hours of manual reporting every month was gone. Their team got that time back to focus on work that actually moved things forward.
More importantly, leadership now had a single source of truth for their entire lease portfolio.
I don’t just mean a summary. I mean real, actionable metrics.
They had cumulative portfolio cost across every region, cost per employee by location, and real-time projections for what an early lease termination would cost before they ever walked into a negotiation.
Think about that last one.
Before, that calculation required someone to go dig, build a model, and come back days later with an answer. Now it was sitting right there in the dashboard, updated every night and ready for the moment the question was asked.
That is the kind of insight that changes a conversation, that changes a decision, that changes an outcome.
What made it possible was not just the technology. It was the process we built underneath it.
The structure. The standardization. The discipline of making data something that could actually be trusted before it was ever visualized.
The bigger point is this: the story started with lease data, but I want you to think bigger than that for a second.
The framework we applied here works with any data your organization is sitting on.
Badge data shows how your spaces are actually being used. Operational data from day-to-day systems, financial data, utilization data, workforce data. If you’re collecting it, the same principles apply.
Structure it. Trust it. Automate it.
Suddenly, it becomes something people can actually make decisions with.
What stood out most with this client was not the dashboard itself. It was the client getting that time back every month from not having to do that process manually. It was automating the work and taking the guesswork out.
Questions that used to take days got answered in the room.
That’s what clean, reliable, repeatable data unlocks. Not just better reports, but better outcomes.
At the end of the day, this client came to us thinking they had a reporting problem. What they really had was a foundation problem.
Once that foundation was right, everything built on top of it worked the way it was supposed to.
That is the piece I hope really sticks with everyone today.
Before the dashboards, before the visualizations, before any of that, it starts with getting the data right. It starts with making it something you can trust, something that updates itself, and something that is ready when you need it.
That’s where the real value lives.
I hope today’s story gives you something to think about as you consider what the foundation looks like for your own organization.
Thanks so much. I’m going to hand it back over to Amy.
Introducing the Data Health Score
Amy Jefferson:
All right. Thanks, Chad.
What we just saw is what it looks like when it’s working. But the real question is, how do you actually measure and maintain that over time?
That is why we’re really excited today to introduce something new: a new way to measure data confidence.
What we’re really talking about is turning something that often feels subjective, data confidence, into something you can actually measure.
Once you can measure it, you can manage it and improve it over time.
With that, I’m going to hand it over to Cory to walk us through our newly released Data Health Score and how it helps you better understand and improve the quality of your data over time.
What Is a Data Health Score?
Cory Siegrist:
Hey everybody. I’m Cory, the Data Quality Manager here at Apex42.
You’re probably asking yourself: what is a Data Health Score?
A Data Health Score is a simple way to measure and monitor your data confidence over time.
Instead of guessing whether the numbers are correct, you can have reliable data and see where you stand.
We’re excited to introduce this new way of measuring data confidence in a more consistent and structured way.
The goal of the Data Health Score is to provide an at-a-glance view of the overall quality of the data that powers your reporting and operations.
Think of it as a wellness check. It is not meant to be perfect on day one, but it becomes more precise and valuable as we refine it together.
We start with a three-point approach to building your Data Health Score.
The first point is data quality.
This refers to how accurate, complete, and timely your data is. In other words, is your data current and usable?
A lot of people start a new project and say, “We’re going to keep this data up to date.” But over time, maybe the person keeping the data updated is no longer with the company. Maybe they switch positions. Maybe other priorities come into play.
That is where data starts to lag. Then your data confidence and data quality go down.
The second part is data consistency.
This measures how uniform the data is and whether it is aligned across systems over time.
This goes back to what Chad was talking about with having a single source of truth for your data.
Is the data there? Is it consistent? Are policies defined around how and when specific data gets updated? Does everyone have access to update the data, or is only an administrator able to update it?
The third point is system usage.
How high is your system adoption?
If people see it as just another system, something that takes time out of their day, or something less important than other priorities, then your data is not going to be accurate and it is not going to stay up to date.
Having processes around system usage is very important as well.
We see these three points as the foundation of a Data Health Score.
Poor user adoption can lead to poor data quality, such as inaccurate data. Poor data quality can then cause poor data consistency. Your source of truth may become unreliable for metrics. Then you have less confidence in your decision-making.
This is a sample of what a Data Health Score might look like.
It shows an overall score. In this case, it is 70%. Then it shows a breakdown of the individual scores.
For this specific example, data quality is 42%, data accuracy is 100%, and system usage is down at 4%. Together, that gives an overall score of 70%.
Right now, this is still in the early phases, and we are not evaluating every dimension of the data yet.
What we really want to understand is whether this metric sounds useful to you. Does it help you long term? Do you see where improvements or changes could be made?
Your feedback will be critical.
We want this to feel meaningful and actionable, not like another metric you do not use.
We would like you to think about what would make this valuable to you.
With that, I’m going to hand it back to Ashley to tie everything together.
Tying It All Together
Ashley Betthauser:
All right. Thanks, Cory.
This is a big moment for us and for our clients.
What you just saw with the Data Health Score is a new level of visibility most teams have not had before.
It takes what is often a gut feeling of, “We are somewhat confident in our data,” which a lot of you said in the survey, and turns it into something measurable and actionable.
That is the shift from reactive, where you are constantly validating and fixing, to proactive, where your data is monitored, maintained, and ready when you need it.
That is how you close the confidence gap.
When you have that clarity, everything we’ve talked about today becomes much easier to execute with confidence.
If there is one thing to take away from today beyond Chad’s story, it is this:
You likely don’t have a data problem. You have a confidence problem.
The data exists. The question is, do you trust it enough to act on it?
That is the gap.
How do you measure it? How do you monitor it? Ultimately, how do you close that gap so your data is not just there, but ready to support fast, strategic decisions?
The path to solving this does not require overhauling everything all at once.
It starts small.
Understand where you are today. Build a strong foundation. Move toward decision-ready data.
For most teams, that starts with one or two key metrics, like the ones you shared in the poll. The goal is to confidently answer those key metrics without hesitation.
Based on the polls, many of you said you are somewhat confident in your data. That is exactly where you need to start.
The next step is pretty simple: let’s establish your baseline.
If this resonated with you, connect with your CSM. We’ll walk through the Data Health Score together.
We’ll look at where you stand today, where you think you stand, which might be different from what your Data Health Score shows, and where your biggest opportunities are to improve confidence.
For those of you who shared specific metrics in the chat, that is exactly where we would want to begin.
This is how you move from uncertainty to clarity and to faster, more confident decisions.
With that, I’ll hand it back over to Amy for Q&A.
Q&A
Question 1: How are organizations using automation today?
Amy Jefferson:
Thanks, Ashley.
At this time, we’d love to open it up for questions, whether they are about the Data Health Score, how to get started, or any other problem your organization is struggling with.
You’ll see a Q&A section within Teams. You can use that and select to post anonymously if you’d like.
We’ll give it a minute for questions to come in.
All right. Let’s do this one. I think I’m going to hand this over to Cory.
The question is: On the pyramid you showed, automation stood out. What are some examples of how others are using automation today, and how flexible is it when it comes to incorporating data beyond Wisp?
Cory Siegrist:
That’s a great question.
We’ve worked with a number of clients when it comes to integrating systems with Wisp.
We receive data from several sources, from HR to badging data to leasing and operational data.
Generally, if you are receiving a spreadsheet at the end of the month, or you are exporting data in some form, we can basically integrate with it.
Some common integration points we use are APIs, data transfers where you send a flat file to us, or we pick data up from an SFTP site on your side.
Those are some of the more common ways of getting the data.
By combining the Wisp data with the data we receive from you, we can create a good foundation and a single source of truth for reporting and decision-making.
Question 2: What makes this approach different?
Amy Jefferson:
All right. Going to take this next one.
I really like this one. It is probably a struggle many of us have seen.
The question is: We’ve tried to maintain accurate data before, and it didn’t stick. What makes this approach different?
I’ll hand that one over to Ashley.
Ashley Betthauser:
That’s a good question.
It’s very common, and it really comes down to the process, not the intent.
Most teams rely on manual updates, disconnected systems, and unclear ownership. Even if things start clean and you think they are accurate, data can definitely drift over time.
What makes this approach different is the focus on how you maintain ownership and accountability.
If you think about the pyramid, you start with the foundational data. Then, as you move up, you build ownership, integrity, workflows, and automation.
That is where you need to go next.
It is not a one-and-done effort. You also have ongoing support from our team to help keep everything aligned.
Instead of doing a one-time cleanup effort every quarter or every year when a big question needs to be answered, data management becomes part of your day-to-day operations.
That is what makes it stick. That is what keeps the approach different: maintaining ownership, automation, and continuously keeping the data updated over time.
Question 3: What does a typical next step look like?
Amy Jefferson:
All right, I’m looking through the questions here.
Chad, I’m going to hand this one over to you.
The question is: Can you walk us through what a typical next step looks like for a team like ours?
Chad Rohland:
Yeah.
As a Client Success Manager, we are going to start by having a session.
You will call me, or whoever your CSM is, and we’ll see where you are today.
That is the biggest thing: figuring out where you are today. We’ll look at what data is good, what data may not be as strong, and take a step back to view it through a clean lens.
That helps us get to the Data Health Score.
By understanding your data health, we can identify where things are strong and where there may be gaps.
Sometimes we think we have great data or that it is being used well, but sometimes it is not. We need to find the opportunities where we can get that cleaned up. That helps improve confidence quickly.
From there, we outline some easy steps to help get things into a better place.
We are not trying to do everything all at once. It is little things here or there to build the foundation and focus on a few key items first.
Those start to add up and help us get to a place where we are automating and building a process.
We talked about getting the same data entry and repeatable data. It is really about focusing on the small steps that lead to bigger improvements.
Once you get that data repeatable and consistent, you can make some big improvements.
It all starts with contacting your CSM, having a conversation, being honest with each other, and figuring it out from there.
Question 4: What is the biggest mistake teams make when acting on data?
Amy Jefferson:
All right. Let’s go ahead and take this one.
Ashley, since you have 15 years of tenure here, what is the biggest mistake you see teams make when trying to act on their data?
Ashley Betthauser:
That is a good one.
The biggest mistake I think we see most often is people trying to jump right to insights and dashboards.
Dashboards have been a buzzword for a long time. Everyone says, “I want dashboards, I want dashboards.”
But dashboards are only as good as the data.
Before you can get to insights and dashboards, you need the foundation fully in place.
You might have the data. Like we talked about, there is so much data everywhere that we almost become numb to it. You can even have the reports built.
But if you have inconsistencies in definitions, gaps in standardization, gaps in accuracy, or too many manual touchpoints, that doesn’t help anything.
One big problem is people go right to dashboards and say, “I need these metrics,” but they don’t take a step back and ask, “Do we have the data in place to stay accurate and consistent on an ongoing basis?”
Again, it is not a one-and-done exercise. You should not have to constantly pause, validate, and double-check.
Instead, when you go to those reports, you should be confident in them every time.
That is where people see the most success. They take a step back, understand and align on the core data, standardize the information, build trust in a few key metrics, and then scale to more advanced reporting and analytics.
That is how you move from having data and having reports to actually making quick decisions with that data.
Question 5: How are clients measuring utilization and adoption?
Amy Jefferson:
I think we’ll do this one, and maybe this can be a combination of what we heard from the client side.
Cory and Ashley, feel free to chime in.
The question is: As we think about our Data Health Score, how are other clients measuring utilization and adoption? What strategies have you seen successfully drive adoption? Without utilization data, we are lacking the insights needed to make informed decisions.
Cory, maybe you can touch on the fact that utilization data is not the only thing we integrate with. You can also talk a little bit about how there is one thing to have ongoing utilization data, but if you don’t have that, you can start small and do a one-time project.
Cory Siegrist:
Sure.
We have done multiple projects with clients as a one-time dashboard or one-time utilization project.
Usually what has happened in the past is they give us the last six months of badging data. Or, if they have other sensor data or something similar, they can provide that.
We bring that data in and match it up with the Wisp data. Then we can create utilization views based on the number of people coming in on a daily or weekly basis, average utilization over that time period, or weekly or monthly views, depending on how they want to slice the data.
Once we get the data in, we can do a lot with it and provide insights into whatever the biggest pain point is.
We can work with you and get that taken care of.
Ashley Betthauser:
Something else that is really powerful to think about is looking beyond the badging data itself.
You may be able to get utilization data from your access control system, but coupling that with your space and occupancy data is what creates real value.
That is really what we strive to do.
You might look at utilization data and see you are at 80% utilization and think, “Wow, this is great. Everyone is coming into the office.”
But if you still have 40% vacancy in your space, your space is not optimized.
We want to look at it holistically across those different data sets to understand whether you are truly efficient in your space.
That means taking your Wisp data, your space and occupancy data, and coupling it with other data sets, whether that is leasing data, utilization data, or something else.
Then you can really look holistically at whether your real estate and workplace are optimized.
Leasing data is another great example.
If you are looking at utilization and you see that you have a lease coming up next year, and utilization is low while vacancy is high, that is a great point to ask: should we relocate to a smaller space? Should we validate what we really need?
By mixing all of these data sets together, you get a much clearer picture of what your real estate and workplace are today.
Closing Remarks
Amy Jefferson:
All right. It looks like the rest of the questions are a little more organization-specific, so we will have your CSM follow up if you submitted one of those.
Thank you for joining us today.
We truly appreciate your time and the perspectives you shared.
We are passionate about helping organizations rethink their data, and we are excited about what becomes possible when teams can fully trust their data and move forward with confidence.
Thank you again.