B2B Revenue Acceleration
B2B Revenue Acceleration

Episode · 3 years ago

14: The Big Data Landscape in a Fast-Changing Economy w/ Tom Mack

ABOUT THIS EPISODE

The data you collect should be speeding you up, not slowing you down.

The only way to make sure this is happening is to leverage the power of the cloud, but how do you keep up with all of the recent trends in this space?

Tom Mack is the RVP of Sales, EMEA at Qubole where he joined four years ago to build out a sales team in the Western United States.  Qubole provides big data as a service, so they understand this landscape well. They focus on allowing automation to handle the life cycle of data clusters so organizations can get insights and yield out of their data as opposed to managing the infrastructure associated with big data technology.

In the time since he joined the team, Tom and his family have moved to London and opened up Qubole’s European sales operation where the team is on track to keep expanding throughout Europe. Tom’s job is to drive sales and create new business opportunities.

Tom joined us for this episode of B2B Revenue Acceleration to talk about the Big Data landscape, different verticals and industries that are benefiting the most from this technology, and differences between the North American and European markets.

You were listening to bb revenue acceleration, a podcast dedicated helping software executive stay on the cutting edge of sales and marketing in their industry. Let's get into the show. Welcome to be to be a revenue acceleration. My name is over, I am with you and I'm here today with Tommac from qubold. Are you doing to Daytam, very well right. How about yourself? I am doing fantastically well. So today we want to talk about you, about the big data landscape in a first changing economy. That's a big, big topic. Probably at the pig that is is is very close to your hearts, based on the world that you are doing at quebold. But before we go into the details, can you please tell us a little bit more about yourself, your role within q bowl and I guess what q bold does as an organization? Sure. So, I joined you all up four years ago in the United States and built out the sales team in the western US and...

...the management team asked me to open up the European operation. So my family and I moved to London a year ago this time to open up your office and drive and create a business here in Europe focused out of London and we've since then built a team and you are on a track to really expand that business and that opportunity throughout Europe. What q will does is it does provide essentially big data as a service, with the idea of allowing automation to handle the life cycle of clusters so that organizations can focus on getting insight and yield out of the data as opposed to managing the infrastructure associated with those big data technologies. We're on track for about three hundred customers and growing very well. A couple of million queries executed against cuble and we're processing PEDA weights of data, close to an exhibited data per month that we have our clients, but...

...that sounds like a no full lot of data. That so obviously one of the conversation point to then, and the reason why we want you to have utim, is to discuss about the big data landscape. And we know that big data as became a game change in most modern industries of other last few years and while the technologies have evolved and there is a lots of data as well in organization. So I think it's fair to say that organization of more and more data they've got one more complex data they probably have more and more systems holding the data together. Could you please share with us a bit about how the actual big data landscape is looking at the momentum, from yokels pick teeth, but also what do you believe all the trends for the future? I think I'd probably start with the major trend, of is migration of big data workloads to the public cloud. We're seeing a significant number of clients here...

...in Europe already in the cloud or having plans to migrate their data workloads to the cloud, and that's for two reasons. One is that the resources needed for big data are typically quite elastic, so the whole on demand elasticity of the cloud plays very well for that. But there's also this separation of compute and storage, so the object stores of the respective clouds, or that's Azure, Google or Amazon. That's a very cost effective way to store pat of bits of data and then, which is very inexpensive relative to running it as hdfs and a traditional data center in a traditional data center on an expensive compute or expensive machine. And so what that means is that you're able to do in a very cost effective way store your data and then con use the elasticity of the cloud and the virtual machines in those cloud providers to them process that data on them as needed data basis. So it allows you to scale up...

...very quickly and then, with the changes within the cloud to per second billing, allows you to be very aggressive on the downscaling. So you're constantly trying to optimize the current cluster based on, you know, the current need. So that's one trend that we see. The other one is that people are more and more interested in streaming analytics, so as data is collected in real time to do as much analytics as as possible, and use cases for that are a lot around ECOMMERCE, pricing optimization. We see a lot of anomaly detection within streams to understand how the business is actually prefer me, and I guess the third trend that we're seeing a lot is more of enabling of self service analytics, so providing very broad access to that organizations but doing it in a very fine grained way as kind of that third trend. Okay, so it's basically doing more for less. So getting the advantage of the public cloud...

...is really around its cust saving. It would believe, and also probably the pain of managing it at a center, because we know that that doesn't is not easy to manage. You need to deal with redundancy and all that sort of things. We also it seems. It seems from the tools of points that you've mentioned that it's about the change in conception of the data. It's about the change of what we use data for. How do we think that data to make business decisions. So it's really do you see also that the transition into the business intelligence, the way people are making decision based on the data they collect? Well, would you say, is the same, but it just got more granularity. Now I think it's very smaller in that use case, but they do have larger data sets that they want to use to whatever you company is goal is to do, is enable operating markets or, you know, decisions closer to the consumer. You can allow the organization to do that. And what they're trying to do is, rather than just basic...

...operation of they that are trying to append that with other social data and other analytrics or data that they might purchase from third party providers to provide a better view of the world so that they ultimately can make better but better business decisions on behalf of the consumer themselves or their consumers are there and customers. Does that mean then, that you see new functions, so marketing for example, or finance so but new function within organization asking for more intelligence, asking for more of that data of or requiring to use more big data solution in order to, I guess, create efficiency or accelerate revenue in down function. Do you see those lanes of business consuming all definitely so. I think that's one of the trends that organizations are trying to do, is provide a larger data set to be able to or data sets and doing curated data sets, but...

...at a much larger scale for the lines of business and though their internal customers, so that people can make a very informed decision, you know, tactical and strategic within an organization. So it is definitely a situation where both the internal customers are asking for more data sets to be included, which means the infrastructure has to change, and that's where products like ours play a role and we typically, in most cases, will sit next to existing investments. But providing that broader access to larger data sets, because it's focused on big data technologies as approves to traditional R DMS has or relational databases and a price datawarehouses. Okay, that makes sense, I think. I think we see a lot of that in the market Al Self. So that's good. A question about the functions and the line of business and all the people within the organization that comes from all that big data on in Moll that's intelligence refine intelligence from...

...big data. Do you see any specific verity chords of specific industries also that have a small that's requalment f implementing big detest solution and and also it's kind of a two side equation. You have comments. So do you have examples of the kind of resorts they can expect from implementing big big data solutions? I think one of the trends that we see across the company and not just in Europe, for London especially being a heavy retail environment, there's a lot of mobile application and well documented that more and more people are sending and buying to the mobile experience, but that's an iphone, ipad, some sort of device like that of the laptop itself, and so what we see out there is more under of really understanding the user journey within the applications themselves, a...

...lot of ab testing that goes along with that as well, and then really, once they solidify that, executing quickly on price optimizations based on competitors and competitive an analysis as well. So it could be across the board. We have several companies that are in the travel industry that are really focused on obviously maintaining margin, but having a very aggressive pricing strategy to win that business, and the same thing can be said on consumer retail as well. So in certain situation rations, you know, with AB testing, some of the metrics that we've seen from one kind in particular is about a seven percent of lift and revenue as a result of better decisions around how content is surfaced within their mobile application. That's wonderful. So thank you very much for sharing allder that about the big data market and all things are evolving at the moment. It's very useful tree term. One final question that I've got for you. So we know that qubold organization is growing fast, ending into new region. We...

...ownder some from your introduction that you came from North America, from San Francisco, I believe to be to be to be accurate, to London. If I remember correctly, the first time we met you actually fresh of the plane and we met for a coffee in London. So I guess my next question is more it a bit of a personal question to you and I'd like to I'd like to onner so much is your experience. So if you can share your experience as an American coming into the UK and what you've seen as the main differences between the American market? Well, obviously you've been successful and that's the reason why your management team wanted you to come to Europe and use that experience to push the European market and get that to take off, but from your perspective. So I guess it's more of a personal question. was on the business question, but I'm always interested to understand the true jual differences that you've seen the way business is done. So yeah, you get to get to get you with some that's sure. I think, to be fair,...

...when I first started a huble we were very new and we're very targeted towards specific verticals and then as we grew we started to expand and what I'm seeing is that we have to do more of that here. So I think it's I think the answer is kind of twofold. One is that there's a lot more education that has to happen here because the market has changed in the four years I've been with cuble. There's a lot of people that in companies that are coming out or have been out for a while and, you know, everybody's mixing marketing messages promising the world when it comes to analytics and big data. So I think the customers are a little more conservative here in London and in Europe, but they also have, you know, much more to look at these days and really kind of do a lot of fetting. So the process is a little bit slower and people making a decision for or against because there's much more to look at right now and there's a lot to weed through...

...in the actual market itself. A lot of different players out there. So that's one of the challenges that we've seen. It's we still win business, but it just seems to take a little bit longer and the education seems to take a little longer. The other issue, I didn't talk about it in one of the trend conversation that you talked about up but the pace at which all these open source technologies are moving and evolving and, yeah, promise that goes along with them is substantial. So you know, you have this trend to go from machine learning to deep learning right now, with all the tensorflow, cares mx that in the different libraries around the data science world, and those are really challenging to keep up with, and the decisions how to use those in the most appropriate manner are really tough as well, and the skill set needed for those is very difficult. So you have to have setting the proper expectations with clients to say that it's not going to be, you know, a very quick use of these deep learning technologies. There's a learning curve...

...associated with it and then there's the whole migration to production that goes along with it as well. So expectation settings for results is and setting those properly for clients is something that we're really trying to be very specific around, given the pace that with technologies and the evolution of those technologies as well. So that's okay, Great. What's that's good? What's essentially your insight? I'm sure you apology has got to have a lot of taking from from the different things that you discussed today, which we do every single time we ask our guests to give us away or give away to our least, not to get in touch with them up to get in touch with that company. They want to have a bit more of a conversation with you as an individual or, if they won't engage with you, to discuss about your solution. In your case, that would be discussing about cuboard. So toub what is the best way to get in touch with you? Sure best way is just Tom at qubolecom...

...to U Bolcom and is Paya email and happy to respond and answer any questions that people may have. Best ononderful thank you very much. Really appreciates your payment inside today. It's great speaking with you as always. Ready thanks for the time. operatics has redefined the meaning of revenue generation for technology companies worldwide. While the traditional concepts of building and managing inside sales teams inhouse has existed for many years, companies are struggling with a lack of focus, agility and scale required in today's fast and complex world of enterprise technology sales. See How operatics can help your company accelerate pipeline at operatics dotnet. You've been listening. To Be Tob revenue acceleration. To ensure that you never miss an episode, subscribe to the show in your favorite podcast player. Thank you so much for listening. Until next time,.

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