Episode 65: How Vertex has Built a World Class People Analytics Function (Interview with Jimmy Zhang)
I am often asked if People Analytics is the preserve of larger companies and I always say no, every company has business questions that can be answered with People Analytics. That said there are different challenges in setting up a People Analytics Team in an organisation with, say less than 5,000 Employees.
For one, you don't have the luxury of building a large team, this means you need to take a different approach, one of partnership and leveraging skills from across the organisation. This is exactly what my guest on this week's episode has done and in doing so has built one of the most agile, most innovative and most successful People Analytics Teams I have come across.
Jimmy Zhang is the Head of People Strategy and Analytics at Vertex Pharmaceuticals. He joined the company, which has just over 3000 Employees, in 2018 and has built the People Analytics team from the ground up.
You can listen to this week’s episode below, or by using your podcast app of choice, just click the corresponding image to get access via the podcast website here.
In our conversation, Jimmy and I discuss:
How Vertex leverages a partnership model, a strong foundation in governance and ethics, to advance People Analytics in the company
The tripartite relationship between the People Analytics team, HR Business Partners and the Business and how this helps prioritise and elevate People Analytics work
Work that Jimmy is leading around skills to help drive career growth, development and mobility at Vertex
How Vertex is building a continuous employee listening program, which couples active and passive data sources
How Vertex tackles the build versus buy conundrum when it comes to HR and People Analytics technology
This episode is a must listen for anyone tasked with building or transforming People Analytics or who is involved in building or buying HR technology. So that is Business Leaders, CHROs and anyone in a HR Leadership, People Analytics, Workforce Planning or HR Business Partner role.
Support for this podcast is brought to you by charthop. To learn more, visit https://www.charthop.com/digitalhr.
Interview Transcript
David Green: Today I am delighted to welcome Jimmy Zhang, Head of People Strategy and Analytics at Vertex Pharmaceuticals, to The Digital HR Leaders Podcast. Jimmy, it is fantastic to have you on the show. Can you provide listeners with a brief introduction to your background and your role at Vertex?
Jimmy Zhang: Thank you for having me, I am excited to be here. So a brief background on myself. I started with Vertex back in 2018 and I head up People Strategy and Analytics. I usually joke with people that I grew up in HR, because if I go back in time, I actually started my career at Liberty Mutual as part of the Human Resource Development Program and then I move over to Thermo Fisher Scientific, leading some of the HR transformation and technology work there. And from there I moved over to Biogen, taking on various roles in People Operations, Learning Services and People Analytics.
David Green: Well, you have been doing some great work at Vertex since you joined and we are particularly excited to explore as it is a question I get a lot, is People Analytics just for really big organisations that have tens of thousands of people? Well you have proved that it is not, because Vertex is around three and a half thousand employees, I think. It would be great to explore how you have set up People Analytics at Vertex and talk a bit about how you have set the team up, as well as some of the challenges that you faced?
Jimmy Zhang: Oh yeah, definitely. So, I joined Vertex to create the People Analytics Function, there was really not much in place, so I had actually the opportunity to build the strategy and roadmap from the ground up. The first thing that we needed to do is determine the type of People Analytics function we wanted to build out. So after a lot of the internal discussions, we determined that we actually want to build out a world-class People Analytics function, within the Biotech Industry as an aspiration. A major challenge to achieving that, obviously like you mentioned, it is really the size of the organisation. We don't have the luxury of building out a big team, especially as we have a combined workforce of just over 3000 people, so we had to be a little bit more creative in terms of what work needs to reside within HR. Where does it make sense to actually ponder across other functions? And how do we actually more creatively leverage external vendors? To overcome this challenge, we decided on what we consider as the Partnership and Collaboration Model, where we kept some of the sensitive capabilities like Employee Listening, ONA, Workforce Planning, on the People Analytics team while engaging with our Internal Data Science and IT Teams on capabilities like Machine Learning Models, Self-service Analytics, Data Management, to round out the core capabilities that we need to build out.
So essentially we crowdsourced resources and capabilities across the organisation to quickly advance over the past three years. I have to say that I have been really lucky to be able to work with some of the best talent in the Analytics space through this cross-functional partnership model.
David Green: And I guess leveraging some of that expertise from elsewhere in the organisation helps, when it comes to addressing big challenges that the organisation is facing, because access to skills, access to business data as well and bringing that together with the people data.
Jimmy Zhang: Yeah, that is absolutely right. Essentially because we all look at data differently throughout the organisation, when you have this cross-functional model, then you can look at it, even from an HR standpoint, looking at this from a different angle where we can actually leverage different datasets to actually tell a more meaningful story.
David Green: We are going to look a bit more into the partnership model and explore some of the relationships that you have built with vendors as well. So let's start with the partnership model, have you faced any challenges around the partnership model? What are some of the things that you have learned over the three or so years?
Jimmy Zhang: I think with all models there are always challenges, so partnership model is no different. Since we have cross-functional resources working on People Data, we quickly realised that we had to do more to safeguard People Data and maintain that trust. So that is critical to us. We know at that point we need to actually create some standards around how we handle, distribute and analyse People Data between the teams, so one of the key early priorities was to actually put together an Ethics Charter with our guiding principles and at this point we actually transparently shared the principles with our Employees through our intranet site.
We also have to link a lot of our communications to the Ethics Charter, so people know exactly how we are actually handling, distributing, analysing that data. We also actually, at the same time, formed a Cross-Functional Governance Team, which include HR, Data Science, IT, Privacy, Legal, Communications, essentially all the representing functions, to review all the studies that we undertake and make sure that we do the right thing by looking at all the angles possible. One of our guiding principles is really to protect the security of individuals by masking and aggregating data as soon as practical. So in order to do that, we actually mask and de-identify that data before sharing with the Cross-Functional Partners, so that we continue to maintain that trust.
I am actually really grateful to have had Dirk Petersen, who helped us accelerate the Ethics Charter work when we started the People Analytics journey, that is actually definitely a critical part of our operating model now. Another challenge I will say is to make sure that we have alignment between our Partners on the key focus areas. This does require us to start the planning process early, but I think one of the key things out of this is this does provide us with the time and space to think strategically about the priorities we want to tackle as a team. So we have a five-year AI roadmap right now, that spans across all stages of the Employee life cycle.
David Green: I am often asked about the importance of having good governance around People Analytics and I say that it is really important and it is worth investing the time to do it because it sets yourself up for success. And certainly I think the example that you shared around the Ethics Charter is really important and I know, as you said, Dirk worked with you in the early stages of that. But you have got a particularly good relationship, good partnership, with your Privacy Team I don't know if there is two or three best practices that you could share with other listeners?
Jimmy Zhang: Definitely. I think the core to collaboration and partnership is really to bring people in early. A lot of times when you bring Privacy and Legal and even other teams in early, I think you create a sense of community where you actually try to co-create something. A lot of times when you bring people in a little bit later, then what they are trying to do is actually mitigate certain risks that is around the projects you want to do. So that is where I think it is important to bring in your partner early on, so that you can actually start that co-creating process. That is why we see that the Governance Team being so effective because we have cross-functional representation. So when we actually talk through a project, we potentially might be able to actually look at it from a different angle. And then once we actually have that, we can then drive forward with a project with full alignment from everyone, including Privacy and Employment.
David Green: Anyone listening to that who is looking at setting People Analytics up within your organisation, or maybe re-evaluating your approach to working with your Privacy Team, I think there are some good words from Jimmy there. I think bringing people in early is definitely something that, I think, is going to resonate in our conversation. You have got a great, really impressive, working relationship with the Business at Vertex, to understand the business strategy and prioritising projects, which I guess is so important in a smaller team as well. Can you tell us how you have achieved this? And again, what advice you would give to People Analytics Leaders who are looking to do something similar?
Jimmy Zhang: I think, different organisations have different challenges at different times. So as People Analytics Leaders, we really need to understand the business opportunities, priorities and challenges as this will give us the information necessary to help the organisation. I usually treat this as more of an example, a lot of high growth organisations might put a lot of effort in to talent acquisition while some mature organisations might focus on Employee retention and each organisation is a little bit different. But in order to understand the priorities, I think People Analytics Leaders need to actually collaborate more effectively with the Business and HR Business Partners, to identify key organisational opportunities. On the flip side, People Analytics Leaders need to help the Business and HR Business Partners understand the value that People Analytics can bring to a table, to help drive actionable outcomes. That is where we see some collaboration there, where we can actually help HRBPs drive actionable outcomes by letting them know exactly what we are working on as well.
David Green: It is that two way thing, isn't it? Because, the day job of a HR Business Partner is typically very challenging. You are getting lots of requests from lots of different people, so by helping them understand what analytics can do, you are actually helping them make some of those tasks a little bit easier and they can provide more impact and value in those conversations with the Business. And again, with the Business, what it doesn't know about people, it doesn’t know. So there is that need to communicate what you can do, but there is also a need for us in People Analytics, to understand the challenges of the Business and the challenges of the HR Business Partners as well. What are some of the things that you have done to help your HR Business Partners, for example, be more comfortable around using analytics in their conversations?
Jimmy Zhang: I think there is a couple of things, initially when we started the function, we wanted to have that push for self-service analytics, but we quickly realised that that is not the support that they need. I think from a self-service analytics standpoint, I think a lot of the tools right now is making everything easier and easier.
So what we would need to help the Business and HR Business Partner understand is we need the insights out of that. Based on the insights, we would need to actually collaborate with them to be able to understand the data, understand the insight and then they can take that to the Business, to drive actionable actions on.
I think that is actually critical, to really sit down with them to understand essentially the insights and then drive actions afterwards.
David Green: As you say, it is a team effort, isn't it. People Analytics, HR Business Partners and the Business, coming together to work together for the benefit of the Business and the Workforce.
It is not People Analytics versus HR Business Partners, which is what I hear sometimes.
So Jimmy, we have shared a number of conversations over the years and I am always really impressed with the initiatives and the work that you are doing at Vertex. So we thought we'd spend some of the next section of our conversation talking about those. Let's start with the focus that you have gotten on Skills Data, tell us a little bit about some of the work that you are doing there?
Jimmy Zhang: I think I have to actually go back to 2019, at that time we actually found signals from our data, that career growth and development will become a priority area for the organisation. We see signals from our survey and email from our data to say, we have to actually do more around the space. We were able to get ahead of it by creating a business case around internal mobility, which then actually started our exploration around the skills data. So going back to that Partnership Model, we then quickly engaged our Partners, including Data Science and IT Teams to understand the quality of skills data that we already have in place. Also because of the smallish organisation that we operate in, we need to figure out ways to actually enrich that data by looking at both absolute skills that we have, but we also have to actually engineer a little bit to look at infer skills. So based on the collaboration, we were able to create a really high quality custom skills model to match employees to open jobs. We did a comparison against some of the external products and found our model to be more superior and we are now in the process of deploying our custom job matching algorithm, as a first people analytics product, as a way to help people navigate their career at Vertex. So as the next evolution of the skills model, we are expanding the use case to match people to career goals that generates career paths, to help them see where they can go from a career destination standpoint. This work is really exciting and because we actually created this as a custom model, we have full control over the roadmap and then we can deploy it, based on the use cases that we have instead of actually buying a product from a vendor.
David Green: Again, a great example of best practice. Develop something internally, then compare before you then invest further in it, actually compare it to what else is out there externally on the market. Sometimes the tools outside will be better, but in this case, it showed that your internal model was actually going to provide more value for the Business moving forward, so you go down the build route rather than the buy route.
Jimmy Zhang: That is absolutely right. There has to be a balance between buy versus build. You can't do everything in house and I think at that moment, you need to actually think about what is the best strategy based on the company needs.
David Green: What sort of feedback have you had from users around the product at the moment?
I know you are in quite an early stage, but I would be interested in some of the feedback you have had?
Jimmy Zhang: From a feedback standpoint, we actually released it to a small pilot group first and get their feedback to make sure that the quality of matches what they are expecting. We actually were getting a lot of great feedback on the accuracy of the model suggestions. Another thing that we will be testing in the future is really the outcome, because now we have this tool out there, we want to make sure that people actually apply to those open jobs, so we are actively testing that as well but early feedback is very positive right now.
I think just that transparency is critical because allowing other people to be able to see the possibilities across functions, I think in itself is a really positive thing.
David Green: The next area we are going to talk about is productisation anyway, but if we are developing a product from a consumer basis, we want to understand what the consumers, who we want to use the product, feel about it. So it can give really important feedback to support the next stage of the development cycle and iterate the product, I guess it is working in an agile way. I know from previous conversations that links quite nicely to this, you are looking within the People Analytics Team to shift from a project mindset to a product mindset and actually using Data Science for productisation.
Can you tell us a little bit more about your focus here and what are some of the key challenges? I guess it might come back a little bit to the skills.
Jimmy Zhang: Oh, it does. One of the key learnings, for me at least, throughout this project is there is a big difference between Data Science for research projects versus Data Science in creating products.
So a model that you create for research purposes might not actually scale to become a product. You actually need to have a different infrastructure to be able to scale the model to support certain things like error handling, model re-training, model feedback. David you touched upon this already, people’s likes and dislikes, how do we actually capture that and pull that back into the model. So this becomes more of a model operations problem, which is in my opinion, a lot bigger. So to overcome this challenge, we actually decided to bring in an external vendor that specialises in Model Operations Engineering to create the initial infrastructure for us.
We are going back to a buy versus build model because we don't necessarily have the internal skill-set, at this point, to do it so we actually bring in a vendor that specialises in this, to help us. But now our internal team is fully trained and they have the knowledge to be able to get on top of the infrastructure, so that was such a good upfront investment where now we have the internal skills to be able to actually move forward with productising this.
David Green: Can we maybe expand on the conversation around the build versus buy conundrum. What are some of the things that you consider when you are going down that road of building or buying technology?
Jimmy Zhang: When we talk about build versus buy, I think we always look at a couple of things. One of them is resources. Sometimes it is actually a lot easier to be able to buy certain things, for example, external data. So it might be actually a little bit easier to buy because you have to spend a lot of resources internally to be able to actually build out that capability, it might be even cheaper also to buy as well. So it is always looking at resources, cost and the quality. I think those are the three things that we look at when we evaluate, build versus buy. The thing behind that too, is where do we want to actually differentiate ourselves? So in that case, we think that we can build very accurate and strong models, so we want to keep certain capabilities in house. At any given time, when we make that trade off between build versus buy, we also want to keep, for example, the algorithm development in house versus outsourcing that.
David Green: A great example again, we don't need to talk about the particular vendors, but I know in the external skill side, when you were working out which partner to work with you piloted a couple of vendors. You then kind of understood the one, not necessarily were the best vendor, but they were the best vendor for Vertex and the problem you were trying to solve.
Jimmy Zhang: Yes absolutely. In the external sensing space, for example, there is a lot of quality vendors with certain vendors being more specialised in the Biotech Industry from a scraping standpoint.
So when we did a comparison against different vendors that is one thing that we did compare. A good way of comparing this for example, there are two different types of external data. One of them is for example, job postings in the People Analytics space. Another one might be our social profile. So if we look at job scraping, one of the things that we need to look at is the quality of the job scraping. So we do compare it by actually giving them the data set to actually scrape, let's say, the past three years of Vertex job postings and because we have internal data to be able to compare the quality of it and we can do a comparison to see which vendor is better for us at that point. So I think before we engage with external vendors, we do need to actually have that step to be able to evaluate the solution against something that you already have, so that you can be comfortable with the solution that you buy.
David Green: I guess it is a similar approach, if you are going to build something internally, you pilot it. And it is the same if you bring in external technology, you want to pilot it and work with the vendor, potentially, on developing a solution for your organisation as well.
So let's move to the next area, which I know is another area that you are really focused on, around continuous listening. Again, I think you are building a solution in-house here for collecting passive and active data. I would love to hear more about that because I think it is a really hot topic.
Jimmy Zhang: Oh yeah. From my standpoint, we have been very proud of the work that we are doing around employee listening. When I started with Vertex we actually didn't have an internal engagement survey. So the company actually rely on external best place to work surveys, to gauge employee engagement. I knew right away, at that moment, that we had to focus on building out our Employee Listening Strategy as a key priority.
We actually introduced our first internal engagement survey back in 2019, along with other surveys like on-boarding and off-boarding, but then we quickly expanded our survey to become twice a year and then introduced the bi-monthly COVID post survey as well, to better support our Employees. We also provided the Managers and Leaders with real-time access to the data, so they can actually drive real time actions.
This year, like you mentioned, I think one of the things that we are doing is introducing new tools to collect both active and passive collaboration and network data. The ultimate goal is to see if there is a way to look at passive and active data sets together, to form what we consider, the continuous listening strategy.
If we can actually find signals within the passive dataset on some of the outcomes we care about, we actually no longer need to waste six months to see the trend, it becomes more real time. But the early signal of the passive dataset is not necessarily matching up with some of the active datasets when it comes to some of the key outcomes we try to measure. So this requires some further exploration, but I am happy to dive in deeper once we actually learn more and then study it more.
David Green: Listening is always important, we should always be listening to what our employees, our workforce is thinking, particularly those that are close to customers as that can help get better customer outcomes as well. But I guess, in the last 12 to 14 months with the pandemic, it has been even more important. You talked a little bit about how you moved to bi-monthly pulses during the pandemic, if you don't want to get into the details that is absolutely fine, but what are the sorts of things that are surfacing up that is helping the organisation adjust during what has been quite challenging situation?
Jimmy Zhang: Yeah. A couple of key themes that we noticed is employees actually need better support in terms of work-life balance. Another one is, and by the way this is not just specific to Vertex, industry wide we are seeing this trend too, is really related to the pandemic it is also correlated to certain things like burnout. So, those are some of the key things that we measure. When I talked to other companies as well, they are seeing the same thing, so those are some of the things that we are looking into.
David Green: Talking to a lot of your peers they similarly stepped up the frequency of listening, making it actionable in real time so that the Managers and Leaders within the Business around communications and responding to it. But also looking at the whole thing around returning to the workplace, looking at the post pandemic workplace, whenever that actually happens. Is that some of the work that you are digging into as well?
Jimmy Zhang: We are definitely digging into that as well, I think we are lucky in the fact that we actually have a portion of Employees on site, especially the Laboratory employees. We actively measure both populations, so the people working remotely, which is giving us remote working insights, but we also actually have certain onsite employees that we can measure the sentiment there too. Essentially that is really going to help us, when we return to work, on some of the challenges we might actually face.
David Green: And there is so much complexity around this isn't there. It is not as simple as just looking at employee preferences, clearly that is important as that informs it but it is understanding the Business need, it is also understanding that different people in different groups, depending on their work and their home situations are going to have different preferences as well. So it is a lot to analyse from a People Analytics perspective.
Jimmy Zhang: That is right and also, I think the great news is we are being able to draw on some of the external research as well in this space, so I think if we marry up the internal research plus the external research, hopefully we can come up with a plan to bring people back.
David Green: That is an important learning point for People Analytics practitioners, there is a lot of external research out there and that can support a lot of the work that you are doing internally.
One more question around that. You talked about that real time access to enable Managers to take action on the data. That doesn't happen by magic, obviously there has to be some communication with Managers so that they can interpret what the data is telling them and the actions that they can take. What are some of the steps that you have taken around that?
Jimmy Zhang: Yes, I think the main thing is just to make the data easy to consume from a user experience standpoint. If we look at Managers, a lot of Managers do not necessarily have a big team, so they don't need advanced analytics on top of the survey data that we are providing them. I think by being able to offer them some of the numbers as comparison and for them to be able to dive into a little bit deeper in terms of comments, I think that naturally allows them to be curious and want to take action. I think the key thing is being able to train Managers to want to take action and knowing that based on their action it will improve the number. I think that is basically what we need to put in place.
David Green: Yeah and I guess that is the key thing, isn't it? If we take action, it is actually seeing what is the impact of taking that action on the outcomes that we are trying to affect.
Jimmy Zhang: Yes and that is why having multiple surveys a year is important because you can actually see those incremental changes over time. I think that is critical to the Managers when they take action.
David Green: It is a very different world to where we were, not that long ago, around the frequency of surveying and how we did it as well.
The final area that we really wanted to touch upon is the piece around external sensing, we talked about it a little bit there. So for those listening it is about using external data to bolster insights on the workforce and the work that is being done now. What are your key aims with the work you are doing in here and what actions are you taking to try and achieve them?
Jimmy Zhang: Yeah, so external sensing is another capability that we are really, really excited about because if we think about external sensing. I think if we do it right, we are actually moving towards what we consider as the big data space. We can use the data to actually understand a variety of different things. One of the things that is, in my opinion, critical is looking at the evolution of skills in the marketplace and then those skills data can be supplemented back to our Skills Model. So there is a lot of different benefits out of that. Also another key area that we are heavily leveraging external sensing now is around our Strategic Workforce Planning.
So by understanding the future of skills, then we can actually understand and help the Business to see around the corner. I think another use case that we are excited about is really around that competitive intelligence. So if we think about job posting data, it is actually a really good leading indicator to predict business objectives.
So actually as part of that we are collaborating with a University to create a sensing tool to help detect the changes in business patterns, based on job postings. So I can actually see a lot of benefits out of that, if we can actually make that project work.
David Green: Can we maybe talk a little bit more on the “seeing around the corner” piece, around the workforce planning, I think that is really interesting because I think that connects back really well to the skills work that you are doing to support mobility and development and then I guess the other piece of the jigsaw, that is the workforce planning piece.
Jimmy Zhang: Definitely. I will give an example of another industry. We kind of know that a lot of companies now move to a Cloud Strategy, but at one point what we know is that when some of the leading companies actually moved to a Cloud Strategy, you started to see a lot of Cloud Engineer type of postings coming up six to eight months beforehand. So if we were able to actually see around the corner to say, hey, these companies, our competitors, are hiring for Cloud Strategy then we know that potentially there might be a shift in business strategy. So you can actually plan against that.
Another one is, do you see a surge in postings for specific companies in a specific location? Vertex is located in Boston, from a talent pool standpoint are we seeing a surge in postings from, let's say, one of the companies nearby that will give us early signals on potential ways for us to protect the workforce from being poached.
David Green: It is that use of external sensing data is, I wouldn’t say new in People Analytics but it is definitely an area that is rapidly emerging. Before we get to the last question, I am going to add one more in because I love your view on some of the exciting things that are happening in the space.
So I'm going to ask you to take a broader view outside of Vertex, what most excites you about the People Analytics space? And then, what is your biggest concern, or concerns, about the space as we continue to grow rapidly and evolve?
Jimmy Zhang: What I am most excited about is being able to leverage some of the more advanced techniques in the analytics space to actually help solve people problems. So I mentioned something about external sensing because I think we are using more advanced techniques, compared to before. I think one thing that I am seeing a big change in right now is, we went from essentially basic reporting in the industry to now actually looking at some of the predictive models. But I think ultimately what excites me the most is actually taking it beyond that and basically leveraging more advanced models in terms of creating a prescriptive model. So I think that is the assignment in the industry and I can see that coming. A couple of years ago, if you asked me that, I would have said we we still have ways to go but now I feel like we are actually getting a lot closer.
And then what is the key challenge. I think People Analytics is always going to be in the trust business, so essentially safeguarding people data and work in the confines of the ethics principles, I think is going to be very important for the longevity of this industry. I think if we have even some bad news within the industry it is going to impact everyone, so collectively I think it is our job to be able to actually safe guard this.
David Green: Having that Ethics Charter in place, having a close partnership with your Privacy Team and being transparent with your employees on the data you are collecting and how it is being used is literally foundational elements of doing that and keeping that trust is so important.
So last question to finish and we certainly covered this so it might be more of a summary of where we have got to. We are asking all of our guests on this series, this particular question.
How does having the right people data in place support the fast paced environment that Vertex is operating in?
Jimmy Zhang: I think this is a great question. So reflecting back on our conversation, I think having the right people data is the foundation of everything that we do. So, figuring out problems we might want to tackle, creating models, measuring outcomes. But we don't always actually have perfect people data from the start, I don't think any of us do. I think it is critical to start small and then use the people data that we have in place to add Business value, especially in a fast paced environment, right away. Then once you do that I think it will buy you time, to put the roadmap together, to acquire additional people data that you need to do more advanced work. I think this applies essentially to a small company or applies to people that are trying to start out in this space.
David Green: I completely agree. It is interesting we had one of your peers on, Guru Sethupathy from Capital One, on a few weeks ago and he said that when you are starting out, just be useful. Just help the Business solve problems and you will get the momentum to grow, which is very similar to what you said. There is no surprise that you are both very successful within your organisations at helping to do that, so there is some great advice there for people to follow.
Jimmy, thanks so much for being a guest on The Digital HR Leaders Podcast. Can you let listeners know how they can stay in touch with you and follow you on social media?
Jimmy Zhang: I am on LinkedIn so people can feel free to reach out if they want to connect.
David Green: Jimmy, it has been an absolute pleasure, so much that we could dig into and have a much deeper conversation, I am sure we will revisit that some day. So thank you very much and enjoy the rest of your day.