Steve McMillan: Yes, thanks Howard for the question. So just in terms of migration of our — of the entire VantageCloud enterprise customer set to like we haven’t actually announced when we would migrate that customer population over. What we’re seeing is the adoption of VantageCloud like is really been driven by departmental and experimental use cases so utilizing the AI or ML models against the VantageCloud Enterprise data. And so it’s really a great expansion play for us. By the end of this year, we’re going to have some great opportunities to start essentially coalescing our VantageCloud enterprise technology with our VantageCloud lake technology. And we’re looking forward to being able to do that because as we do that, because of the very nature of the ability to expand VantageCloud Lake because of its nature from a self-service perspective, because it’s highly dynamic, because it’s a fully cloud native solution, and we’re pretty excited about how that’s going to drive expansion for us into 2024.
Operator: Thank you. The next question will be from the line of Erik Woodring with Morgan Stanley. Your line is now open.
Erik Woodring: Hey guys. Thank you for taking my questions. Congrats on the quarter. Steve, maybe if I just start with you, can you maybe just share some color on how your customer conversations trended over the last 90 days in terms of desire to spend on data and analytics needs versus concerns about the macro and really not the ability or the inability of customers to potentially open their budget, could that come more in 2024? Just maybe if you could just to post maybe the caution versus desire to spend on data and analytics from your customers that would be helpful. And then I have a follow-up. Thank you.
Steve McMillan: Yes. I think we’re seeing still a robust demand from a data and analytics perspective in the market. Certainly, it stays in the top three priorities for our customer spend. I think a lot of our customers are looking at and have already declared business value generation from data analytics, AI and gen AI activities. Lori Beer from JPMC recently went out in some public remarks and was talking about the over $1 billion in returns from AI projects. And I think that is really taking the interest of our customers and kind of putting off any macro risks that may be out there. The other thing I would add to that is just from a Teradata perspective, we’ve got a strong business model, which is more insulated from macro environment, given the fact that we’re not consumption-related – a high degree of our revenues are already in a fixed capacity as opposed to a consumption model.
And so a lot of organizations are talking about cloud optimization and macro environments where the customers are trying to reduce spend. We have a very predictable capability in site Teradata in terms of squeezing out every part of compute and storage for our customers for some of the mission-critical workloads. So we’re kind of insulated from those macro environments because every customer needs to continue to close their books. But we’re certainly seeing a lot of tailwind from data analytics, AI, large language models in terms of driving interest and demand in the market.
Erik Woodring: Great. That’s really helpful. Thank you, Steve. And then, Claire, maybe if I just ask my second question to you, sorry to beat a dead horse here. At least it’s not – it’s not an ARR question. It’s a free cash flow question. Just taking into account your comment on 3Q free cash flow, it does imply the strongest 4Q free cash flow kind of as far back as I can track the data. I know you have confidence in it, that’s not what I’m questioning, but maybe just give us more comfort in why 4Q is so big relative to history understanding linearity looks different, but really what comes in 4Q versus, and then versus normal historical seasonality and why specifically 4Q versus other quarters? And that’s it for me. Thank you so much.
