In this session, Bill Tennant, Chief Revenue Officer at BlueCloud, shares how the company partners with Snowflake to help enterprises transition from legacy systems to modern, AI-enabled data environments. Through a series of real-world case studies—from global manufacturers to airlines and financial service firms—Bill illustrates how BlueCloud enables agility, productivity, and innovation by centralizing data, optimizing infrastructure, and implementing enterprise AI strategies.
Attendees learn why data innovation is crucial for staying competitive, how to overcome common modernization challenges, and how to maximize ROI using Snowflake’s scalable ecosystem. The talk also highlights BlueCloud's proprietary tools like BlueInsights and BlueTalent, showcasing how business users can harness unstructured data and AI with natural language tools. The session wraps up with practical lessons learned, emphasizing the importance of user adoption, governance, and strategic alignment in successful digital transformations.
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I'm Bill Tennent, Chief Revenue Officer at BlueCloud. We're a TrueBlue Snowflake partner, and we've brought about 45 to 50 customers to this event to share their experiences—specifically, how we've supported them in migrating from legacy systems and enabling enterprise AI initiatives within their organizations.
In this session, I'll share a few real-world case studies that highlight our successes and how we help other organizations achieve similar results.
Let me ask: Is anyone here already a Snowflake customer? Are you considering Snowflake? Okay, that sounds like a mix. So, as we go through, please feel free to ask questions at any point. I'll pause regularly to keep it conversational.
We're going to talk about:
- Why data innovation matters
- A few real-world case stories
- The path to enabling enterprise AI
- And finally, some lessons learned—because, of course, not everything we do goes perfectly.
We're all in the business of people, and we help other people implement technology that—while ideally bug-free—still comes with its challenges.
Let's start with why data innovation matters.
Why Data Innovation Matters
I'm incredibly passionate about generative AI and the broader AI initiatives happening in the organizations we work with. But here's the reality—and I just mentioned this to someone earlier: if your organizational strategy isn't clearly defined and doesn't align with transformation goals, then centralizing your data is a good step—but only that.
You also need to think critically about the nuances of your data strategy. Ask yourself: How do we use this to gain a competitive advantage? How do we get more agile? Because if you're not thinking about those things, a two-person startup using AI effectively can leapfrog much larger, more established companies.
Data isn't just a competitive edge—it's a vital initiative for everyone. At BlueCloud, our role as a Snowflake implementation partner is to help organizations centralize data and use it to become more agile and competitive. We help translate foundational data efforts into an agile framework that drives real value.
When it comes to data integration, we've all been there. I've been in the data and analytics space for over 15 years—back in the days of SQL Server, Analysis Services, and Integration Services. Back then, putting together data packages and enabling self-service BI felt like a major innovation.
And then, the business users got access.
I'm a business user, so I can say this: getting access to data without understanding its context or how it was derived often leads to more questions than answers. Over time, we've evolved from legacy reporting to self-service BI and are now able to ask natural language questions out of our data.
Lately, I've been saying: "If you can dream it, you can build it." But that doesn't always mean you should.
Now, let's talk about legacy challenges.
If you're here—or considering Snowflake—you're probably dealing with everyday challenges: siloed data, slow processing, legacy system limitations, and difficulty scaling.
We've all talked about "future-proof architectures" for years. But let's be honest—they often feel outdated two years after being built.
That's where Snowflake is different. It allows us to implement a scalable, flexible, and easy-to-use solution that lives up to the idea of future-proofing. Snowflake taps into a whole ecosystem, which reduces maintenance and adds long-term value.
BlueCloud's role as a systems integrator is to help you take advantage of everything you've already done—the good stuff—and migrate it into Snowflake. Then, we help you build on that migration to enable true enterprise AI.
Let's move into case stories.
Real-World Case Stories
This one involves a global manufacturer and distributor. While I can't share the name, the situation was clear: they were working with an old SAP data warehouse and a traditional BI tool. Sales teams had access to data, but they downloaded it into Excel, manipulated it for two weeks, and only then arrived at the insights they needed.
That two-week lag is a massive problem in a global supply chain. So we stepped in with Snowflake, dbt, and some BI ecosystem partners. We removed the limitations of SAP and BI tools and enabled near-instant access to insights.
The result? Hundreds of thousands of hours of additional productivity, thanks to real-time, natural-language data access for the sales team and business users.
And that was just the start.
We then moved into their product manuals—these were dense, complex documents. Regulations often required the organization to roll out hundreds of these manuals at once, whereas before, they only handled one every few weeks.
We ingested those manuals—unstructured data—into Snowflake and enabled their customer service agents to query them directly using natural language. No more flipping through manuals or calling field technicians with partial answers. Now, they get instant answers, reduce costs, and improve customer experience.
This doesn't replace jobs—it empowers existing teams to scale without adding hundreds of new people during large changes.
In another case, we worked with a large project management organization to integrate Salesforce and NetSuite data. As you've probably noticed, a recurring theme in our work is helping clients bring together sales, customer, and ERP data.
By integrating financials and customer data, we give organizations 360-degree visibility. In fact, we have another session later today focused on a similar case.
For this client, we didn't just integrate data internally. We also enabled external data integration and built mechanisms for data monetization. And no, that doesn't necessarily mean selling data—it can mean offering a new dashboard module to your customers. That's an upsell opportunity that supports revenue growth.
Again, it all comes back to ROI: How do we grow the top line, reduce costs, and use technology effectively? The use case should drive the technology, not the other way around.
Another case story I want to share is about modernization at scale.
We worked with a client leveraging a legacy SQL data warehouse and Oracle database. We helped them modernize through cloud migration and integrate with the broader ecosystem around Snowflake—including AWS, Microsoft, and Google Cloud.
You're at Snowflake Summit, and you'll hear a lot about the layers of Snowflake, but remember: it's not just about implementing Snowflake. It's also about stitching together all the layers—storage, compute, cloud services—and working with a partner who understands the technology and funding mechanisms supporting integration.
That's what we do. We help combine cloud ecosystems, data strategy, and business goals to create a long-term solution.
So our customers come to us to implement Snowflake and help them understand: "How can I reduce my costs associated with Google and move everything onto AWS? How can AWS help fund that migration while also implementing Snowflake? And can Snowflake help fund that migration as well?"
And then, on top of that, they can leverage that data and take it to the next level from an enterprise AI standpoint. So, to me, innovation and growth are not just about asking, "How do we get your data into Snowflake?" How do we innovate and leverage all the different mechanisms available so that when talking about return on investment, we're not just talking about a one-to-one comparison?
You don't just write down the cost and try to figure out how we're going to make money from it. Instead, we write down the cost, reduce it through the funding mechanisms that we secure for you, and then scale that appropriately to support your initiatives.
Later today, we're doing a customer reference video with one of our fantastic customers. Now, this isn't their exact case story. Still, that kind of example allowed them to almost entirely pay for a massive transformation—one that required moving across multiple geographies, multiple clouds, and into Snowflake.
Then you get to the fun stuff. The stuff that business users love. A lot of DevOps work, and you know, going down the path of making sure that what we put into production will be viable for them—and will be helpful. So that people can trace where that data came from. That's vital to the success of any transformation we discuss.
Our team is not only jumping in to help with the legacy assessment, migration, cloud adoption, and cloud framework—we're also leveraging the adoption mechanisms associated with all the different components that go into not just giving them data but data they can trust and rely upon.
This is especially true when you consider the new mechanisms where people can ask questions, right? Think about the ChatGPTs of the world—people ask questions, and they're okay with the answers not being perfect. The reality is that that is fine for some business units.
But if you ask our CFOs in the room—if you ask him if he's comfortable with our financial reports not being accurate because we used ChatGPT instead of building out the solution—my guess is his answer will not be the same as mine.
Infinite scalability—so, Snowflake. For those who are using Snowflake, we can help you not only with its infinite scalability but also with mitigating the cost impact that comes from trying to drive everything into Snowflake. Because if you just dump everything in and leverage all your future—or your legacy—technology in the same way with Snowflake, all you're doing is shifting the cost to a different bucket. You're not taking full advantage of the entire solution.
Now, doing just a quick migration into Snowflake is fantastic. Getting the data in so you can be successful is critical. But that optimization work and the scalability associated with it is the best way to take full advantage of those lasting resources.
Alright—my favorite. I could spend the rest of the time on AI-driven insights.
The path to Enabling Enterprise AI
I think Snowflake is ahead of the curve when it comes to what they're trying to achieve—and what they are achieving—regarding AI. They give access to the underlying systems, so you're not just developing or buying a black box.
They give you a solution that allows you to take advantage of all the frontier LLM solutions that are coming to market—all the models. They don't force you into a bucket that puts you in a bad position if there's a massive transformation within the industry.
From my personal perspective, this is a very real concern—because things within the entire space are moving so fast that it's hard to keep up on a monthly basis. The reality is that the only way you can keep up is if you have that foundational data link in place.
The data layer is there. Snowflake provides the ability to leverage its AI tools from a data collection and repository standpoint. But BlueCloud has built individual processing mechanisms to support it.
Now, I'll give you some rationale behind this.
Two years ago, our team built out—in conjunction with Snowflake—a pretty advanced (and I don't see it in the room) causal inference machine learning model that supported ad optimization across multiple teams over the years.
Our AI team sat down with me and tried to explain it to me—showed me code, talked about the statistics they were using, explained why it was valuable—and this blank stare came over my face, right?
I said, "Can you give me a dashboard? Can you give me something that tells me what we're doing?"
Right? The statistical concepts are great, but this is why AI initiatives aren't always successful. There are extraordinarily intelligent people developing these models and the ability actually to use them. And then you've got a business user on the other side.
So instead of a data scientist explaining it to an analyst, who then explains it to a business user, who then hopefully understands—through that game of telephone—what they're actually getting, we gave them access to what we call BlueInsights. This was the first iteration of a chatbot that allowed us to ask questions of an inference model.
And now, instead of us saying, "Hey, here's this great machine learning model that says you should change the set to do this," you can open this up and ask, "How did I perform last quarter?" Right? Basic question. "Where should I allocate that if I had access to additional funds? Based on what's happening in the market, what demographics are actually the ones that I should be focusing on?"
You're asking the next "what if" question. You're not asking, "Tell me information about a month ago, three months ago, or six months ago." Right? So you're not making decisions based on past trends—you're making decisions based on what's going to happen in a predictive case.
So, we've taken that one step further. There's a visualization component to that, but our incredible team has been able to pull together that model and that chat interface and create the ability for us to leverage that and extend it into all different types of use cases.
So, if you come down to our booth, we're talking about what we call BlueTalent.
BlueTalent is our ability to take unstructured data—so think CVs, past projects, emails, and notes associated with a project. Some of our people are tremendous. But if I send you their CV, you'll think that they're junior employees—because we don't try to offer them to customers, but people ask for them by name regularly.
So, suppose I use the CV from five years ago. In that case, it doesn't necessarily tell you about the 25 advanced certifications and 15 different projects across every industry you can think of—where they could leverage their talents to provide that to you.
BlueTalent is our internal solution for that. But where I see that going—and this is what I encourage every customer to think about—is: Think about the use cases that you're developing, and then how can that be applied to five different other areas? Because what we're building with Snowflake is that foundational layer.
So, if you think about a talent solution—whether you're a health system and you need to understand which of my nursing staff have gotten certifications in these technologies or these different products—I can actually move them from one area to another without having to hire.
Right? Let's create an efficient resourcing mechanism for internal HR that allows us to leverage our internal talents and be successful—not remove people and hire new people just because we have a new need and we don't understand where the data is.
We're working with an airline right now. One of the challenges they have is when you use a dropdown to say, "Hey, the flight is delayed," and the reason selected is "weather delay." I don't know about anybody in this room, but "weather delay" isn't helpful for me—right? especially when there are no clouds in the sky.
But the notes are associated with what the pilots have discussed with the maintenance crew—right? That could be helpful.
And so, taking that unstructured data and leveraging that to be able to ask questions as a business user—without having to worry about "Does this data exist?" Because now it does. It's all there. It's just a matter of how we find it and how quickly we can get to it to make decisions and succeed.
Make sense?
Okay, so BlueCloud–Snowflake advantage. One of the things that I mentioned early on is that we are a true-blue partner. We won Innovation Partner of the Year this year. We were their fastest-growing partner last year.
What is the value to you?
I always think about these things and think, you know, "Data Cloud—now they say we came with new hardware," and words are great, but what does that mean to the customers that we work with? What does that mean for the interactions we have in the market?
It allows us to integrate more closely with not only the product teams but also the engineering and solution engineering teams, the sales teams, and the professional services teams.
BlueCloud works incredibly closely with the Snowflake professional services teams to support the architecture's development.
It is impossible—no partner in the world can keep up the way an internal resource at Snowflake can with all of the nuances and changes that are happening within their product. I'm going to misquote this, but I think there were 125 GA releases in the last few months or years.
The reality is, I don't care how good you are—unless you're sitting and focusing on what's happening with that piece of it at all times or you're getting fed that information (which it's tough to feed people 125 new releases and all the nuances associated with them)—
The only way to keep up with that is by leveraging a mix of an SI and Snowflake. And so, we work very closely with the Snowflake professional services teams and all their innovation teams to create things that aren't going to be a GA release next month that we happen to create this month, right?
We need to align with that and create a roadmap. That strategic advantage—both from a collaboration standpoint and the fact that we have hundreds of certified individuals on our team—and we're the ones helping (and I can't say all of it), but we're the ones helping to create some of the certification paths for some of the future state architectures that are being put in place—allows us to help drive the success of our customers working with Snowflake.
Lessons Learned
Okay, so I promised you some lessons learned. As I said, we have a mix of people on Snowflake and those who do not. If you are on Snowflake and everything you do in your life is in Snowflake—then fantastic. You can probably tune this out.
If you're not, or you have something that's out there—whether it's unstructured or structured data—I think having a centralized strategy and understanding how your organization is going to take that strategy and implement it toward the next goal (versus starting from technology and working backward) gives you a very clear picture of how to centralize that, how to have that strategy be effective, and how to achieve the milestones associated with the ROI that you're presenting internally.
Prioritize user adoption. A lot of people forget that change management is hard, but it is important. People struggle with change. I personally think I like change. One of my sales leaders in the room told me yesterday that she knows I hate change. Um, I didn't realize that. Right? It could be that change for me—I have to recognize that.
So, start small and scale fast. Instead of creating—the days of the two-year data warehouse transformation project should be behind us. We should be thinking about: How can we start small and scale this to something that makes sense so that you're getting value and not just paying for a solution that hasn't contributed anything to the business?
Because I think most people know: the odds of you being around at the end of that transformation are a little bit lower if you do it that way.
Governance matters. Being able to trust the data is critical. Anyone who's been in this space for any length of time knows that—right? We don't want to put out reports without rigorous testing. We don't want to actually deliver something that supposedly implements the business logic until we've thought it through and done that rigorous testing.
But AI is coming in to help support that and remove some of the human errors that come from—even things as simple as testing and as complex as the actual migration. We all saw, for example, Convert AI, which is allowing us to move faster.
Now, once we do move faster, even if it gets us 99% of the way there, trust still matters. So if we're 99% of the way there, and we have to go back over that 99% of the code to understand what's actually going to work and what might still have an error in it, then we've got a problem.
But we're now fast enough, with the technology that is being used today, that the current architectures allow us to get 99% of the way there. That dramatically scales down the size of some of these implementations and helps you migrate faster. So, we get to the real value associated with an AI data platform and the value associated with having all that foundational data in one place.
How can BlueCloud help?
If you're interested in having us support you through some of this, talk to us, right? We can do data assessments and strategy roadmaps. I'm happy to come out and sit down with you for a full business value mapping session and walk through: What are the different areas that we should be targeting? What are the different things that could be valuable?
We can also do your end-to-end implementation by working with Snowflake. We're going to invest directly. We can help you to map that out. We can work with the cloud providers to help get them to help support an evaluation of the different toolings and make sure that the technology fits the use case.
But this is exactly where we shine, and we have people all over the world who can help support that.
So, I promised I would try to get through this quickly so that you guys can get lunch. I do want to answer any questions, and I will be here for the next 20 minutes to help with any questions that come up after the fact.
But please—any questions before you go to Booth 2902?
Audience: So the question was: Is BlueCloud essentially up to speed on some of the announcements and the new features that came out?
You would've noticed on our website we were listed as one of the launch partners for Snowflake OpenFlow. So we are fully up to speed with all of that.
We have an internal COE who dedicates their time to keeping us up to date, and we integrate closely with the Snowflake teams to ensure alignment. In fact, we have our own—as what's called a "TrueBlue" partner, we're kind of that top-tier partner—assigned individuals across all areas, whether it's solution engineering, architecture, marketing, or sales.
So, all the features you're hearing about—we're up to speed, for sure. Good question.
Alright. Thank you so much for the time. Again, I'll be here. It's great to see everybody.
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