Season 4, Episode 1
Reducing Injury & Increasing Retention of Industrial Athletes
Globally, 38,000 people get hurt on the job every hour. In the United States alone, over $250 billion dollars is spent on workplace injury annually. Sean Petterson, founder and CEO of StrongArm Tech, discusses the role of wearable devices to reduce workplace injury and increase retention of industrial athletes.
Sean Petterson is the founder and CEO of StrongArm Tech, a safety culture company that deploys IoT wearables and data insights to create better lives and better futures for thousands of Industrial Athletes at some of the world’s most iconic companies.
Welcome to Data Brew by Databricks with Denny and Brooke. This series allows us to explore various topics in the data and AI community. Whether we’re talking about data engineering or data science, we’re going to interview subject matter experts to dive deeper into these topics. For this season, we’re going to focus on connected health and how data and AI augment and improve our daily health. And while we’re at it, we’ll be enjoying our morning brew. My name is Denny Lee. I’m a developer advocate here at Databricks and one half of Data Brew.
And hello, everyone. My name is Brooke Wenig, machine learning practice lead to Databricks and the other half of Data Brew. Today, I am thrilled to introduce Sean Petterson, CEO of StrongArm Technologies. Welcome, Sean.
Thank you. Thanks for having me. Great to be here.
So to kick off our session today, I know StrongArm focuses on reducing workplace injury and workplace injury prevention. Can you talk a little bit more about the mission behind StrongArm and how it’s able to accomplish this?
Absolutely. So, our mission is simple, it’s to create a better future for the blue collar workers, we prefer to call industrial athletes. These are the folks that are building the roads we drive on, delivering the packages, delivering food to our door, frontline workers through COVID and the people that are really building the world around us. As that world around us accelerates, everything is connected. As you particularly look at the warehouse, at the manufacturing plant, everything in there is connected in terms of the machinery, but not the most intelligent, integral and dynamic piece of the equation, the human being. So, that’s what we do.
We develop sensors for these individuals as they’re performing these tasks, and those sensors are looking out for any danger that they may see, any chance of getting an injury. We provide them haptic feedback to eliminate the injury, but then we give them a voice. We give environmental health and safety managers the ability to take risk into consideration when designing the operational principles of an organization. And what we’ve done is effectively given safety a seat at the decision-making table when it comes to how you run business. Now, some of the most progressive organizations in the world are using our data to actually inform better practices for valuing and monitoring manual labor for these industrial athletes.
Awesome. Before we get into the tech and talk a bit more about the sensors and how you’re collecting that data, I’m curious to learn a bit more about the cost associated with these workplace injuries. So, could you provide any numbers or quantify the cost to both the employer and to the employee with workplace injuries?
Yeah, absolutely. It’s a massive, massive problem. It’s a pandemic-size problem, if you will. Around the world, every year, 3.3 trillion is spent at workplace injuries. Over 250 billion is spent to the United States alone, and around the world, every hour 38,000 people are going to get hurt on the job. So, what we’re really up against here is not so much getting people to care. It’s really getting people to a place where we can filter through the noise, where we can get to clarity around how do you inform businesses that are so well tuned today with industry 4.0 to give them the insights, to plot a chart to help them navigate around risk.
This is super impressive. I guess diving right into this, what are some of those benefits those employers do see when they invest in training and safety as you’re describing here?
Yeah. The goal here is to find a way to provide these insights in a way that they’re actionable. As you balance across all the different silos of an organization, those metrics for improvement tend to vary enough that it’s hard to make decision. In the past, our market is environmental health and safety, where they would basically enter this operational discussion would be with almost anecdotal studies, studies that were highly observatory, studies that had a static principle in a dynamic environment with 30% observatory biases and an end result that is some risk score, right? So it’s effectively that whole push in terms of the EHS conversation was driven towards the end of the year, pulled out of actuarial budgets and was really difficult to bring that into the fold.
So, we said, if we’re going to make any change in this industry, it’s got to start with those people, the people who care the most about this problem and giving them the tools to communicate effectively across the business, because that’s how you get real change management, aligning incentives, and one thing everyone can align on is ROI. So, what we do is we provide an ROI that is a tool driven off of the data that we see based on the risk perspective that were given from the industrial athletes we monitor. So, essentially, giving that ROI figure enables EHS to make informed decisions, not just about what is the next role this person should play in the business, but what’s the equipment that we can add that will not only make it more ergonomic, but also more efficient? What are the potential incentivization structures that we have today? Are we misincentivizing industrial athletes to push themselves to a point where they’re not only getting hurt, but they’re getting hurt at such a rate that pushing them that hard actually makes our ROI backwards?
What we do is we help these companies navigate and find a balance. The results for us when we deploy our systems is about a 45% reduction in injury out of the gate and a 65% reduction in the injury costs. So, if we’re talking about a $250 billion expense around the country, we can have a significant, significant impact into the output of what industry see. In addition to that, we’re providing a significant ROI, close to three, 400%, and now that is specifically in and around injury prevention. But as we look at our longer term studies, the clients that we’ve been with for three, four years, we’re really granular in terms of every bit of data that we can get. So, we’re measuring 12 and a half times a second. And in our more sophisticated organizations, we’re tying that into the operational workflows.
What we’re finding is that by managing the workforce with a more altruistic methodology, by using their safety score, using their physiology and their personal capacity versus whatever the heck the spreadsheet says they have to do, we’re actually seeing a significant impact in the overall throughput of the organization, so the point of one to 2%. So, we’re reducing injuries by 45%, reducing injury cost by 65%, increasing productivity by one to 2%. Now, what we’ve found most compelling in the last year and a half as we’ve been getting really granular through COVID is we actually increase retention by about 15%. So less people getting hurt, more people seeing that the companies are also investing and engaging in them as employees. I think that win-win mix has really painted a significant set of metrics that have been helping us catalyze this mission to find a better way to assess and value manual labor.
Wow. These numbers are completely mind-blowing, which is pretty cool. So then I’m going to dive right into the thing that I always will want to talk about. Then what data are you recording in order to be able to facilitate this type of change, where you’re seeing the such great ROI and great productivity? Are these wearable devices? What data are you recording? Is it a lot? Is it a little bit? Just want to understand a little bit better about the data involved in all this now.
Yeah. It’s one, the data we record, and then two it’s what’s the data that we can interact with, right? So we have a large array of things that fall into the bucket of injury and risk, but where you have to start is real-time impact, right? The first thing you got to do is eliminate the injury and eliminate it before it happens. So being predictive means you have to measure a lot of data. So, we have two wearable sensors that are prescribed based on the industry. If you are in automotive manufacturing, you’re probably more focused on your ergonomic and your proximity-based type of challenges. If you’re in a foundry, if you’re in remote work, you probably want to more environmental information. If you’re in confined spaces, you probably want to get even more granular air quality information, et cetera, right?
So, where we start, which is pretty common across every single one of our client bases is the biggest problem, which is the ergonomics. So, we’re measuring how your body moves through space and we’re measuring that at a rate of, again, 12 and a half times a second. All those calculations are assessing your velocity and your chance of really hurting yourself based on your movements, and that is the basis of our safety score. So, as we see any one metric, whether it be your [inaudible 00:08:56] twisting velocity or your lifting rate go past the threshold, we’ll provide you haptic feedback, so that as the prompt to correct the behavior and eliminate the injury before it happens. So, that’s how our ergonomic portion of it works, understanding your truncal movement, how your body moves through space and helping you eliminate any extreme motions that lead to an injury.
Then, we’re going after the other kind of tertiary factors that happen as you’re moving through a dangerous environment, and the most common thing in a warehouse is fork truck collisions. So, not only a fork truck driver coming around a corner and hitting an individual, but that fork truck also entering dangerous areas. So, we have proximity-based solutions, both on the fork truck and the driver themselves that provide the alerts to avoid the collision, but also record that as a near miss to inform operations later that, “Hey, maybe we should redirect traffic flow in this area because of the high number of near misses that we’ve been given indication for over the last month.” So, we have transformed in that way. As we move up the ladder, again, depending on how risky the systems get, the sensors themselves collect air quality information, key information, PPM to recommend the right type of PPE. We collect decibel to recommend the right type of hearing protection. We have light in case you’re near welding and lighting exposures.
There’s a lot of things that give us a good baseline or an indicator that there’s a problem in here, right? Our sensors basically create a heat map. We’re able to then go into an area and get highly granular on what we can fix with that insight, right? So, that’s the stuff that we’re delivering in real time, right? That’s the most important thing, and that’s very reliant upon the wearable itself. But now, as we talk about the ecosystem, we’re able to get a lot more out of the context, which gives us more ability to inform environmental health and safety groups and operators with a plan of best practices. We provide those for our clients on a quarterly and monthly basis, where we say, “This is what we’ve seen out in the wild, and we’ve used this data to come up with a plan for you to get yourself on a roadmap to zero injuries in the next five years. This is what that roadmap looks like, and this is what it includes.”
When we’re in there, we’re able to help them optimize based on staying true to their ROI, because if it were all about safety, we would get the people who want to do the right thing, but there are some people who just simply don’t have that in their purview. It’s not like anyone wants to do the wrong thing or anyone doesn’t care. It’s just if you break down the organizational silos, my mandate is to get X packages out the door by Y. That’s it. That’s all I care about. So, if we could fold into them a more broad narrative around how treating these employees better, it actually does yield to that. If you give it the right time and the right attention and the right deployability and the right specificity with the data deliverable, you will all get a win-win outcome.
So with that, we tie in things like productivity data. So we can get ring scan information from a package handler to understand not only the weight of the package, but also how often they’re lifting, what types of loads that they’re lifting. We can collect information that’s pertinent around the person’s tenure and understanding how well they’ve been trained, where they may need new training. We’ve also collect information just generally around the roster, the turnover, and where we found a great deal of success is in the early days of hiring. Now, 3PL logistics businesses very commonly hire about up to 150% of their staff during peak season. So from September to January to get through the holidays, they’re just on a hiring spree. This year has been probably the most difficult year in history to do so.
What happens with those rapid hiring is that they’re hiring anybody that would walk the door and that is a challenge because they don’t know that person’s background. The job on the surface level may seem simple, right? You’re loading packages on a conveyor belt, but the truth is there’s a lot of biomechanics and training and repetition that need to go into that before you’re ready to do that as a career. So, what happened is we found in our data that 60% of the injuries that happen for these new hires that get hired in this temp way, 60% of those hires of those terms or someone leaving on an injury or just walking out the door happened in the first 90 days. So, what the employers typically thought were, “This is like a gamble. We rather hire fast.” The result is people just aren’t cut for the job.
What we said is there is absolutely no way that anyone lackadaisically signed up for this job. In many cases, you’re busing people in for an hour and a half to come and move boxes for 10 hours a day, and they’re probably also working another job. So, that person isn’t lazy, that person isn’t not cut out for this job, that person just needs help because this person definitely needs that work. So, what could we do to help figure that one out? We were able to dissect the data. When we found out that all these terms are happening so rapidly in that first 90-day window, we were able to go and talk to these temp workers. What we were able to see is what are the reasons that you’re leaving. Is it just because you’re hurt? Is it just because you don’t understand the work? What really happened was that they didn’t know the cadence.
So, we had these workers who came in right off of the street, haven’t lifted before, their bodies aren’t trained, trying to keep up with the tenured workers on the site. So their lift rates were actually three times that of the tenured workers. So they were coming in, essentially trying to keep the job and earn the job and burning themselves out to the point where they’re either hurt themselves or were afraid of hurting themselves and didn’t want to get fired. So they would just walk off the job or they thought they were just not capable of it. So this is a really simple data exercise, where we were able to step in and say, “Hey, if you’re a new worker, we’re going to put you on a slightly slower conveyor belt in this section of the warehouse and the tenured workers are going to be over here doing their thing at the regular cadence. Once your safety score hits a plateau, we will then increase the speed for you so you can be with those tenured workers. And if that works out, then you have a full-time job here.”
So being able to do that, we not only were able to retain that employee by a rate of about 45% at this particular warehouse in this zone, but we’re also able to give someone a new chance at a different career, right? We’re able to show them a way that this is a marathon, it’s not a sprint. All we needed to do is have the data to show that to the team, to show that to the operators, get their agreement on how this workspace design works. Now, that hiring crunch is just that much less severe for them. So that’s how we use this data and break it out across the different silos to create a win-win for everybody inside of the workforce.
That is incredible. You have fully convinced me that all industrial athletes should buy StrongArm Tech. And so, as a follow-up to that is what are the biggest challenges you see with either employers or employees adopting StrongArm?
The biggest challenges with employers is really just educating in the right way, making sure that we are using the data for the right reasons and we’re translating that. Our biggest fear is that our industrial athletes get wins that this isn’t a tool specifically built for them and it very much is. But if you have a C-suite manager or a regional manager come down into a warehouse and they’re been before and tell people to wear things, they’re on edge, right? Our biggest challenge there is always just providing the tools for those managers, making sure that they know how to educate the workforce, making sure they know how to leverage the data in the right ways across their workforce, and work very closely with our data science teams to come up with those prescriptive roadmaps and adhere to them.
We also have our client sign what we call safety pledge, where if we find out that the data collected on a platform is being used for punitive action, we will actually redact that platform. We will anonymize the individual and roll it up to the job type, and that’s written right in contracts. We have posters on our website. Again, it’s about security and transparency. Even though wearables are fairly ubiquitous across the world, putting them in the workplace can still be slightly sensitive. It is not nearly as challenging of a cultural hurdle as it was five, six years ago, but it’s still something we want to be highly aware of, right? From the industrial athlete perspective, the biggest challenges are simply finding a way to get more of their feedback and understanding how we can get more of their data, more of their input into our data in the system, and that we’re measuring for the right things, right?
And that’s it, and just that they’re adhering to it and we’re providing the right training, the right follow up behind that and just simply making the jump that there is a huge, huge, huge downside to not protecting yourself. My father passed away on the job and I clearly understand most pains very articulately, but most folks are just in there to put bread on the table and put their bodies in a line. But the truth is just educating them that if you just take an extra second to take a pivot step, that means you can probably eliminate the risk of you not seeing your son play basketball this weekend or not going to hang out with family because you’re icing your back, right? And it’s these kinds of things that we just… It’s all about education and it’s all about consistency of the messaging, because beyond that, the system works and it just takes a safety culture and a cultural change in an organization to adapt this new methodology and it’s a win-win
I really like that key theme of education. Also, sorry to hear about your dad. I like that theme a lot about education because it’s exactly to your point earlier, nobody is maliciously trying to injure their employees. It’s not in the best interest of the employer or the employee, but it’s really about educating them as to how they can better protect them. Because prior to the video that Databricks had hosted on StrongArm, I hadn’t heard of StrongArm, but as soon as I saw it, something clicked of, “Oh my goodness, I can’t believe this isn’t more common in the workplace.”
Yeah. Thanks. That’s our change, right? And I think for the first number of years, it was about how do we prove this science? How do we get people to buy into this thesis? Now, that we’ve done that at scale around the globe, it’s how do we humanize it? How do we get people to understand that this is a vehicle to foster proper communication and the reason we have the data points is to initiate proper coaching? We want to make sure that you don’t have to hide around the corner when your manager comes, because he’s not here to reprimand you anymore.
He’s actually here to say, “Hey, you’re doing a great job, but if you do this, you can increase your safety score. And if we have this interesting moment, there’s a chance that we’re going to eliminate that pain you have in your shoulder or that tweak you have in your back. We’re just here to get rid of these things and I’m here to just make you better.” That’s how management should be, but management in a vacuum is just a cause for problems. I think that there’s a big cultural shift that we’re starting to change by providing the fodder for those actual helpful discussions versus the hit you in the back of the hand with the ruler type discussions that were common in the past.
Mm-hmm (affirmative). And who do you see deriving more benefit from StrongArm? Do you see it more being the employees that are new to the field or more tenured employees? Because I know you talked a lot about reach or training people that are new to lifting and getting them educated on how to properly work their bodies in a warehouse. But then for the more tenured employees, do they still regularly receive feedback in ways that they can improve, et cetera?
So from a training standpoint and an onboarding standpoint, you’re going to get the most out of the platform if you’re a new employee, right? You’re wide-eyed. You’re engaged. You’re learning a new thing. We’re going to help you ramp up that learning curve faster. When you’re a tenured employee however, there are a lot of things we sometimes have to untrain, so I think that becomes challenging. We’ll have folks who have been on the job for 10 years saying, “I started wearing this wearable and now my legs are sore.” It’s not like the wearable is heavy. It weighs an ounce. So, that’s not what did it. It’s the fact that you’re now lifting with your legs and you’re not lifting with your back.
We’re actually changing your physiology, to see people squat versus bend over with their back and hurt themselves. So, seeing that is a… I think the challenge there becomes, again, education. You’re doing the right thing, which is why your legs hurt today. But the way the tenured folks outside of that behavioral change, because many of the tenured folks lift perfectly… But the challenge is when you get hurt the first time, you are 10 times more likely to get hurt the second time and it’s 10 times more expensive, and by expensive, I mean 10 times more severe and requires a lot more work behind it to fix yourself, because you’re likely going into surgery.
So, it’s basically for the tenured workers, where they benefit is after your first strike, that’s where StrongArm helps you. We just keep you on the rails because now you’re 10 times more likely that that small tweak with your foot at the end of the slippery stair is guaranteed to hurt you and cause a much more severe injury that can’t just be solved at the weekend of relaxing is the other area of benefit that we see. So I think the early employees will get the benefit of the education and the tenured employees will get the benefit of avoiding a severely debilitating injury because they’re already at such higher risk.
Well, this is pretty amazing. So then I have to naturally ask, what’s the pricing model for this? There’s a lot of data. There’s a lot of processing. It seems like it’s super helpful to the employees, to the employers. Yeah. How much does something like this cost them?
Yeah, it’s a lot of data. It includes an articulate hardware strategy. We deploy Smart Docks. We deploy different ways to wear. We deploy collateral and training. The truth is we need this to scale fast, so we make it all a really simple SaaS fee. The average is basically about a dollar a day per user on the platform.
Wow. Okay. Well, that’s pretty cool. Okay. Well, then, I want to switch gears a little bit because we’ve talked a little about the price. We’ve talked about how expensive this is for the industry as a whole to people as a whole, but you had called it out a little bit, about humanizing this a little bit. So, can you talk maybe a little bit about your favorite, without revealing any privacy issues, obviously, favorite stories about the employee or the employer, how using this tech has really helped the industrial athlete?
Yeah, I would love to. It’s my favorite part of the job. We get letters all the time that are as simple as “Before we started using the StrongArm risk management program, we were taking six painkillers every day after work. Now, I’m taking none. I have a better relationship with my wife. I am actually playing football with my kids on the weekend instead of icing my back on the couch. I have more energy, I have better sleep.” It’s all these things that I think you wouldn’t normally expect. And then there’s the fun stuff, which gets me more excited, which is, “We’ve started a competition here and the folks with the best safety score get a prize.”
And then to see that culturally change inside of a whole organization is really cool, and to see warehouses against warehouses competing with each other on safety score and building something that is exciting in what can be a very mundane job, lifting packages over and over again for 10 hours a day for 10, 20 years, to bring even more excitement to that and camaraderie around that is, I think, super fulfilling that we’re taking it above and beyond just a widget that helps people. We’re really turning it into a cultural change tool. Now, the next step is just getting the operators to have the same level of the excitement because they go the extra inch to incorporate this new data and that they should see the value of all the people that they’re making their lives better while also hitting their numbers.
It’s actually a little bit mind-boggling what you just said about this. I’m going to go back to what you just mentioned about the fact that you had this one employee talk about the fact that they used to take six pills and it actually now take none, right? There seems to be a lot of implications about this network effect of pain management, the idea that actually now people actually by using a system like this, by using data for connected health, they’re actually able to now, as opposed to basically taking pills all the time, they’re actually able to do pain management in a much more natural way, so their bodies are actually healthier, right? I’m just wondering if you’ve seen any correlations or interesting piece of information related to that.
Oh, absolutely. We see it in the claims data basically, what’s being issued for the individual. We’re partnered with the five largest work [inaudible 00:26:48] in the world and getting granular on the hundreds of millions of historical claims cases. You understand patterns and how things are solved. Not to get too ahead of ourselves at this scale, but there’s a massive impact to the opioid crisis because of simple things like this, right? This a simple equation of vitamins versus painkillers, right? What we’re able to do is offer the prevention, get ahead of the chance, get ahead of the problem, get predictive, help the person solve this with holistic training, with stretching, with PT, with easy things out of the gate, just by giving the early indicators of risk and risk change versus waiting for something to pop and then putting a bandaid on it with a painkiller and then sending the person back into the workforce, right? Because that is a loss for everybody.
It’s terrible for communities, but it’s also terrible really just for the individual who is 10 times more likely to get hurt now that they have that injury today. When they get hurt, they’re not going to be on comp forever, right? They there’s a chance where that injury means that they can’t work again. I can’t tell you how many warehouses or distribution centers we’ve been to, where people had fused discs and bulging discs and just were in constant pain and constantly wearing a back brace, which is ultimately terrible for you. It only exacerbates the problem. It’s just a lack of awareness and education that stems from the lack of data infiltrating the industry into the level that we’re bringing to the table that lead to these spin-outs, but I think this is a problem that can greatly be corrected as long as we’re fast about deploying these solutions and integrating them in the right way with the right organizations.
That’s truly remarkable. I really like this whole conversation has just focused around the theme of education and the human and the individual. And so, I want to shift gears a little bit and just talk about the upcoming features and releases of StrongArm Tech. What are the most call request that you see from your customers or from employees that they would love to have StrongArm offer?
So, I think it’s two sides of the coin. So one is what’s next? How are we going to make the hardware strategy more articulate? How can you help us take in a bigger envelope of risk? And the other side is how do we engage more with the data? How do we drive better operational tool sets? How do we help insurance partners find better ways to assess the risk that they’re undertaking? How do we give risk engineering further tools? So, all those pieces are on the docket. I think at the end of the day, it comes into enablement and providing the tools for those to get granular with our data, marry data sets that they see that are out there. And that’s exciting for us because we’re trying to constantly make this conversation relevant in the context of everyone’s initiatives based on the silos that they sit in in the business. So the more tools we can give to more players in the value chain gives us a better perspective onto what we can impact.
So, where we’re starting now is really just giving the ability to match different data sets together to say, “Hey, I had never looked at circadian rhythm and how that may impact what we’re doing here today. So, where do we get that information from?” and really getting granular on the health and wellness of an individual outside of work, and also getting more information around the middle layer, right? So, what we found particularly through COVID is that environmental health and safety groups have almost, I wouldn’t say truncated, because the budgets are still there, but they’ve gotten lean, and that is really difficult, particularly during COVID because everyone’s remote. How can you be a safety coach? Well, the result is we’re pushing this responsibility in many ways onto managers.
Managers are in charge of enough, right? They have to keep the staff in. They have to make sure they’re doing the job, to make sure they’re coming in, make sure the next person is getting hired, right? So, they don’t necessarily always have EHS training. So, there’s a whole opportunity for us to solve that gap, because that is the way that education gets from what the EHS person mandates down to the industrial athlete, but what’s in it for the manager, right? So, how do we get them to, again, have their incentives and data sets aligned with our overall mission and what the impact is, and really just creating an alignment of incentives and unity inside of an organization? So, we’ll be pushing towards that for the next year or so, and that’s the plan.
Awesome. And do you see employees try to take these wearables home? I know you had mentioned circadian rhythms and trying to analyze additional metrics outside of the workplace, but do you ever see employees actually taking their wearable home with them to help provide feedback when they’re doing other tasks perhaps around the house?
Not so much, because it’s mostly because they forget it’s on, which I think is a testament to decent industrial design. They walk out the door with it. The data needs to be plugged into the… We pull it off the Smart Dock at the shift. We have a lot of people forgetting it’s on, which means it’s doing its job. It means the person is being trained and they’re not overburdened, right? That was always our concern is that we’re going to alert somebody too much, but I think we figured that out and maybe you have to actually increase it a little bit. But there is a world where that data of what happens at home becomes incredibly pertinent to what happens at work.
A great majority of the folks that we protect inside of warehousing and logistics and 3PL are working two jobs. So, it’s not just going home and going to the gym or doing your daily routine. It’s a lot of people will work from 4:00 AM to 1:00 PM and then go do landscaping until 8:00 PM. That is a very, very common thing. Understanding what that does to an individual’s physiology on a weekly and monthly basis is really interesting insights, not only just around what happens operationally to those individuals, but just demographically, what happens to the areas that these people operate in. Really learning more about that, reaching deeper into those areas is going to help us uncover, again, a better way to manage and value manual labor.
I guess if it’s only one ounce, it’s pretty easy to forget that you’re wearing an extra ounce. My daily lunch is going to weigh more than that.
Awesome. So, I’m going to go ahead and close out the session here. Sean, thank you so much for taking your time and talking to us about StrongArm, industrial athletes and how we can really help keep people safe. Just as a shout out, I forgot to mention this at the introduction, but StrongArm is a Databricks customer, and so they’re doing some of their data analysis on the Databricks platform. That’s really the sort of things that fulfills Denny and I and everybody at Databricks is just seeing the impact that our product can have on real people.
Absolutely. Thank you. Thank you all for the partnership and thanks for the time today. It was great speaking with you.