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

October 03, 2018 00:15:02
14: The Big Data Landscape in a Fast-Changing Economy w/ Tom Mack
B2B Revenue Acceleration
14: The Big Data Landscape in a Fast-Changing Economy w/ Tom Mack

Oct 03 2018 | 00:15:02

/

Show Notes

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.

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Episode Transcript

WEBVTT 1 00:00:02.560 --> 00:00:08.349 You were listening to bb revenue acceleration, a podcast dedicated helping software executive stay 2 00:00:08.390 --> 00:00:12.189 on the cutting edge of sales and marketing in their industry. Let's get into 3 00:00:12.189 --> 00:00:17.589 the show. Welcome to be to be a revenue acceleration. My name is 4 00:00:18.070 --> 00:00:21.620 over, I am with you and I'm here today with Tommac from qubold. 5 00:00:21.820 --> 00:00:25.379 Are you doing to Daytam, very well right. How about yourself? I 6 00:00:25.500 --> 00:00:30.899 am doing fantastically well. So today we want to talk about you, about 7 00:00:30.940 --> 00:00:36.009 the big data landscape in a first changing economy. That's a big, big 8 00:00:36.090 --> 00:00:39.810 topic. Probably at the pig that is is is very close to your hearts, 9 00:00:40.289 --> 00:00:43.649 based on the world that you are doing at quebold. But before we 10 00:00:44.689 --> 00:00:48.090 go into the details, can you please tell us a little bit more about 11 00:00:48.090 --> 00:00:52.240 yourself, your role within q bowl and I guess what q bold does as 12 00:00:52.280 --> 00:00:56.840 an organization? Sure. So, I joined you all up four years ago 13 00:00:57.799 --> 00:01:03.039 in the United States and built out the sales team in the western US and 14 00:01:03.240 --> 00:01:08.109 the management team asked me to open up the European operation. So my family 15 00:01:08.189 --> 00:01:12.629 and I moved to London a year ago this time to open up your office 16 00:01:12.790 --> 00:01:19.780 and drive and create a business here in Europe focused out of London and we've 17 00:01:19.980 --> 00:01:23.980 since then built a team and you are on a track to really expand that 18 00:01:25.219 --> 00:01:29.379 business and that opportunity throughout Europe. What q will does is it does provide 19 00:01:29.659 --> 00:01:34.650 essentially big data as a service, with the idea of allowing automation to handle 20 00:01:34.930 --> 00:01:42.290 the life cycle of clusters so that organizations can focus on getting insight and yield 21 00:01:42.489 --> 00:01:47.599 out of the data as opposed to managing the infrastructure associated with those big data 22 00:01:47.680 --> 00:01:53.439 technologies. We're on track for about three hundred customers and growing very well. 23 00:01:53.719 --> 00:01:57.760 A couple of million queries executed against cuble and we're processing PEDA weights of data, 24 00:01:57.879 --> 00:02:01.310 close to an exhibited data per month that we have our clients, but 25 00:02:01.469 --> 00:02:07.549 that sounds like a no full lot of data. That so obviously one of 26 00:02:07.590 --> 00:02:09.550 the conversation point to then, and the reason why we want you to have 27 00:02:09.669 --> 00:02:14.949 utim, is to discuss about the big data landscape. And we know that 28 00:02:15.310 --> 00:02:20.659 big data as became a game change in most modern industries of other last few 29 00:02:20.699 --> 00:02:25.259 years and while the technologies have evolved and there is a lots of data as 30 00:02:25.300 --> 00:02:30.050 well in organization. So I think it's fair to say that organization of more 31 00:02:30.090 --> 00:02:34.210 and more data they've got one more complex data they probably have more and more 32 00:02:34.330 --> 00:02:38.810 systems holding the data together. Could you please share with us a bit about 33 00:02:38.969 --> 00:02:46.240 how the actual big data landscape is looking at the momentum, from yokels pick 34 00:02:46.319 --> 00:02:50.919 teeth, but also what do you believe all the trends for the future? 35 00:02:52.080 --> 00:02:55.479 I think I'd probably start with the major trend, of is migration of big 36 00:02:55.520 --> 00:03:01.150 data workloads to the public cloud. We're seeing a significant number of clients here 37 00:03:01.509 --> 00:03:07.189 in Europe already in the cloud or having plans to migrate their data workloads to 38 00:03:07.310 --> 00:03:12.550 the cloud, and that's for two reasons. One is that the resources needed 39 00:03:12.669 --> 00:03:17.219 for big data are typically quite elastic, so the whole on demand elasticity of 40 00:03:17.300 --> 00:03:22.819 the cloud plays very well for that. But there's also this separation of compute 41 00:03:22.860 --> 00:03:27.289 and storage, so the object stores of the respective clouds, or that's Azure, 42 00:03:27.449 --> 00:03:31.490 Google or Amazon. That's a very cost effective way to store pat of 43 00:03:31.490 --> 00:03:38.370 bits of data and then, which is very inexpensive relative to running it as 44 00:03:38.490 --> 00:03:43.919 hdfs and a traditional data center in a traditional data center on an expensive compute 45 00:03:43.960 --> 00:03:47.159 or expensive machine. And so what that means is that you're able to do 46 00:03:47.360 --> 00:03:53.199 in a very cost effective way store your data and then con use the elasticity 47 00:03:53.240 --> 00:03:58.669 of the cloud and the virtual machines in those cloud providers to them process that 48 00:03:58.750 --> 00:04:02.469 data on them as needed data basis. So it allows you to scale up 49 00:04:02.469 --> 00:04:08.430 very quickly and then, with the changes within the cloud to per second billing, 50 00:04:08.789 --> 00:04:12.659 allows you to be very aggressive on the downscaling. So you're constantly trying 51 00:04:12.699 --> 00:04:15.819 to optimize the current cluster based on, you know, the current need. 52 00:04:15.540 --> 00:04:18.339 So that's one trend that we see. The other one is that people are 53 00:04:18.420 --> 00:04:26.050 more and more interested in streaming analytics, so as data is collected in real 54 00:04:26.209 --> 00:04:30.569 time to do as much analytics as as possible, and use cases for that 55 00:04:30.610 --> 00:04:36.490 are a lot around ECOMMERCE, pricing optimization. We see a lot of anomaly 56 00:04:36.610 --> 00:04:42.759 detection within streams to understand how the business is actually prefer me, and I 57 00:04:42.800 --> 00:04:46.639 guess the third trend that we're seeing a lot is more of enabling of self 58 00:04:46.680 --> 00:04:51.480 service analytics, so providing very broad access to that organizations but doing it in 59 00:04:51.600 --> 00:04:56.670 a very fine grained way as kind of that third trend. Okay, so 60 00:04:56.790 --> 00:05:01.949 it's basically doing more for less. So getting the advantage of the public cloud 61 00:05:02.069 --> 00:05:06.029 is really around its cust saving. It would believe, and also probably the 62 00:05:06.180 --> 00:05:11.620 pain of managing it at a center, because we know that that doesn't is 63 00:05:11.660 --> 00:05:15.180 not easy to manage. You need to deal with redundancy and all that sort 64 00:05:15.220 --> 00:05:17.819 of things. We also it seems. It seems from the tools of points 65 00:05:17.899 --> 00:05:23.209 that you've mentioned that it's about the change in conception of the data. It's 66 00:05:23.250 --> 00:05:27.129 about the change of what we use data for. How do we think that 67 00:05:27.209 --> 00:05:31.810 data to make business decisions. So it's really do you see also that the 68 00:05:31.930 --> 00:05:35.959 transition into the business intelligence, the way people are making decision based on the 69 00:05:36.000 --> 00:05:39.720 data they collect? Well, would you say, is the same, but 70 00:05:39.800 --> 00:05:44.240 it just got more granularity. Now I think it's very smaller in that use 71 00:05:44.319 --> 00:05:47.000 case, but they do have larger data sets that they want to use to 72 00:05:47.680 --> 00:05:54.149 whatever you company is goal is to do, is enable operating markets or, 73 00:05:54.350 --> 00:05:58.670 you know, decisions closer to the consumer. You can allow the organization to 74 00:05:58.750 --> 00:06:00.750 do that. And what they're trying to do is, rather than just basic 75 00:06:00.829 --> 00:06:06.019 operation of they that are trying to append that with other social data and other 76 00:06:06.060 --> 00:06:12.579 analytrics or data that they might purchase from third party providers to provide a better 77 00:06:12.660 --> 00:06:16.699 view of the world so that they ultimately can make better but better business decisions 78 00:06:16.819 --> 00:06:23.410 on behalf of the consumer themselves or their consumers are there and customers. Does 79 00:06:23.449 --> 00:06:29.449 that mean then, that you see new functions, so marketing for example, 80 00:06:29.490 --> 00:06:34.879 or finance so but new function within organization asking for more intelligence, asking for 81 00:06:35.079 --> 00:06:41.879 more of that data of or requiring to use more big data solution in order 82 00:06:42.160 --> 00:06:47.029 to, I guess, create efficiency or accelerate revenue in down function. Do 83 00:06:47.189 --> 00:06:51.709 you see those lanes of business consuming all definitely so. I think that's one 84 00:06:51.709 --> 00:06:57.350 of the trends that organizations are trying to do, is provide a larger data 85 00:06:57.430 --> 00:07:01.620 set to be able to or data sets and doing curated data sets, but 86 00:07:01.779 --> 00:07:08.660 at a much larger scale for the lines of business and though their internal customers, 87 00:07:09.019 --> 00:07:13.579 so that people can make a very informed decision, you know, tactical 88 00:07:13.660 --> 00:07:18.449 and strategic within an organization. So it is definitely a situation where both the 89 00:07:18.649 --> 00:07:25.050 internal customers are asking for more data sets to be included, which means the 90 00:07:25.329 --> 00:07:29.930 infrastructure has to change, and that's where products like ours play a role and 91 00:07:30.079 --> 00:07:34.240 we typically, in most cases, will sit next to existing investments. But 92 00:07:34.399 --> 00:07:40.360 providing that broader access to larger data sets, because it's focused on big data 93 00:07:40.439 --> 00:07:46.550 technologies as approves to traditional R DMS has or relational databases and a price datawarehouses. 94 00:07:46.230 --> 00:07:48.870 Okay, that makes sense, I think. I think we see a 95 00:07:48.910 --> 00:07:53.670 lot of that in the market Al Self. So that's good. A question 96 00:07:53.709 --> 00:07:59.660 about the functions and the line of business and all the people within the organization 97 00:07:59.819 --> 00:08:05.620 that comes from all that big data on in Moll that's intelligence refine intelligence from 98 00:08:05.699 --> 00:08:11.500 big data. Do you see any specific verity chords of specific industries also that 99 00:08:11.620 --> 00:08:16.649 have a small that's requalment f implementing big detest solution and and also it's kind 100 00:08:16.649 --> 00:08:20.129 of a two side equation. You have comments. So do you have examples 101 00:08:20.170 --> 00:08:26.889 of the kind of resorts they can expect from implementing big big data solutions? 102 00:08:28.000 --> 00:08:31.360 I think one of the trends that we see across the company and not just 103 00:08:31.519 --> 00:08:37.200 in Europe, for London especially being a heavy retail environment, there's a lot 104 00:08:37.240 --> 00:08:43.190 of mobile application and well documented that more and more people are sending and buying 105 00:08:43.549 --> 00:08:48.350 to the mobile experience, but that's an iphone, ipad, some sort of 106 00:08:48.429 --> 00:08:52.149 device like that of the laptop itself, and so what we see out there 107 00:08:52.750 --> 00:09:00.340 is more under of really understanding the user journey within the applications themselves, a 108 00:09:01.299 --> 00:09:05.259 lot of ab testing that goes along with that as well, and then really, 109 00:09:05.299 --> 00:09:13.889 once they solidify that, executing quickly on price optimizations based on competitors and 110 00:09:13.610 --> 00:09:18.169 competitive an analysis as well. So it could be across the board. We 111 00:09:18.289 --> 00:09:22.809 have several companies that are in the travel industry that are really focused on obviously 112 00:09:22.850 --> 00:09:28.840 maintaining margin, but having a very aggressive pricing strategy to win that business, 113 00:09:28.399 --> 00:09:33.360 and the same thing can be said on consumer retail as well. So in 114 00:09:33.519 --> 00:09:37.039 certain situation rations, you know, with AB testing, some of the metrics 115 00:09:37.080 --> 00:09:41.269 that we've seen from one kind in particular is about a seven percent of lift 116 00:09:41.309 --> 00:09:46.070 and revenue as a result of better decisions around how content is surfaced within their 117 00:09:46.190 --> 00:09:50.549 mobile application. That's wonderful. So thank you very much for sharing allder that 118 00:09:50.629 --> 00:09:54.340 about the big data market and all things are evolving at the moment. It's 119 00:09:54.539 --> 00:09:58.460 very useful tree term. One final question that I've got for you. So 120 00:09:58.580 --> 00:10:03.740 we know that qubold organization is growing fast, ending into new region. We 121 00:10:03.820 --> 00:10:07.970 ownder some from your introduction that you came from North America, from San Francisco, 122 00:10:09.009 --> 00:10:11.529 I believe to be to be to be accurate, to London. If 123 00:10:11.610 --> 00:10:16.049 I remember correctly, the first time we met you actually fresh of the plane 124 00:10:16.129 --> 00:10:18.970 and we met for a coffee in London. So I guess my next question 125 00:10:20.009 --> 00:10:22.240 is more it a bit of a personal question to you and I'd like to 126 00:10:24.120 --> 00:10:26.799 I'd like to onner so much is your experience. So if you can share 127 00:10:26.960 --> 00:10:31.679 your experience as an American coming into the UK and what you've seen as the 128 00:10:31.799 --> 00:10:37.590 main differences between the American market? Well, obviously you've been successful and that's 129 00:10:37.590 --> 00:10:41.389 the reason why your management team wanted you to come to Europe and use that 130 00:10:41.509 --> 00:10:46.309 experience to push the European market and get that to take off, but from 131 00:10:46.389 --> 00:10:48.990 your perspective. So I guess it's more of a personal question. was on 132 00:10:50.100 --> 00:10:54.259 the business question, but I'm always interested to understand the true jual differences that 133 00:10:54.340 --> 00:10:56.980 you've seen the way business is done. So yeah, you get to get 134 00:10:58.179 --> 00:11:01.179 to get you with some that's sure. I think, to be fair, 135 00:11:01.299 --> 00:11:05.009 when I first started a huble we were very new and we're very targeted towards 136 00:11:05.289 --> 00:11:11.250 specific verticals and then as we grew we started to expand and what I'm seeing 137 00:11:11.370 --> 00:11:13.970 is that we have to do more of that here. So I think it's 138 00:11:15.210 --> 00:11:18.000 I think the answer is kind of twofold. One is that there's a lot 139 00:11:18.039 --> 00:11:22.720 more education that has to happen here because the market has changed in the four 140 00:11:22.759 --> 00:11:28.080 years I've been with cuble. There's a lot of people that in companies that 141 00:11:28.279 --> 00:11:31.080 are coming out or have been out for a while and, you know, 142 00:11:31.279 --> 00:11:37.990 everybody's mixing marketing messages promising the world when it comes to analytics and big data. 143 00:11:39.429 --> 00:11:43.669 So I think the customers are a little more conservative here in London and 144 00:11:43.789 --> 00:11:46.909 in Europe, but they also have, you know, much more to look 145 00:11:46.909 --> 00:11:50.019 at these days and really kind of do a lot of fetting. So the 146 00:11:50.100 --> 00:11:54.419 process is a little bit slower and people making a decision for or against because 147 00:11:54.460 --> 00:11:58.700 there's much more to look at right now and there's a lot to weed through 148 00:12:00.379 --> 00:12:03.169 in the actual market itself. A lot of different players out there. So 149 00:12:03.690 --> 00:12:07.809 that's one of the challenges that we've seen. It's we still win business, 150 00:12:07.850 --> 00:12:11.490 but it just seems to take a little bit longer and the education seems to 151 00:12:11.529 --> 00:12:16.080 take a little longer. The other issue, I didn't talk about it in 152 00:12:16.159 --> 00:12:18.919 one of the trend conversation that you talked about up but the pace at which 153 00:12:20.440 --> 00:12:24.320 all these open source technologies are moving and evolving and, yeah, promise that 154 00:12:24.399 --> 00:12:30.309 goes along with them is substantial. So you know, you have this trend 155 00:12:30.350 --> 00:12:35.870 to go from machine learning to deep learning right now, with all the tensorflow, 156 00:12:35.110 --> 00:12:41.389 cares mx that in the different libraries around the data science world, and 157 00:12:41.990 --> 00:12:45.580 those are really challenging to keep up with, and the decisions how to use 158 00:12:45.620 --> 00:12:50.059 those in the most appropriate manner are really tough as well, and the skill 159 00:12:50.139 --> 00:12:54.460 set needed for those is very difficult. So you have to have setting the 160 00:12:54.620 --> 00:12:58.139 proper expectations with clients to say that it's not going to be, you know, 161 00:12:58.259 --> 00:13:03.330 a very quick use of these deep learning technologies. There's a learning curve 162 00:13:03.370 --> 00:13:07.730 associated with it and then there's the whole migration to production that goes along with 163 00:13:07.929 --> 00:13:13.759 it as well. So expectation settings for results is and setting those properly for 164 00:13:13.960 --> 00:13:18.480 clients is something that we're really trying to be very specific around, given the 165 00:13:18.759 --> 00:13:24.440 pace that with technologies and the evolution of those technologies as well. So that's 166 00:13:24.279 --> 00:13:28.710 okay, Great. What's that's good? What's essentially your insight? I'm sure 167 00:13:28.750 --> 00:13:31.909 you apology has got to have a lot of taking from from the different things 168 00:13:31.950 --> 00:13:37.710 that you discussed today, which we do every single time we ask our guests 169 00:13:37.870 --> 00:13:39.870 to give us away or give away to our least, not to get in 170 00:13:39.990 --> 00:13:43.059 touch with them up to get in touch with that company. They want to 171 00:13:43.620 --> 00:13:46.419 have a bit more of a conversation with you as an individual or, if 172 00:13:46.460 --> 00:13:50.220 they won't engage with you, to discuss about your solution. In your case, 173 00:13:50.259 --> 00:13:54.700 that would be discussing about cuboard. So toub what is the best way 174 00:13:54.700 --> 00:14:00.250 to get in touch with you? Sure best way is just Tom at qubolecom 175 00:14:00.809 --> 00:14:07.049 to U Bolcom and is Paya email and happy to respond and answer any questions 176 00:14:07.330 --> 00:14:11.440 that people may have. Best ononderful thank you very much. Really appreciates your 177 00:14:11.519 --> 00:14:16.159 payment inside today. It's great speaking with you as always. Ready thanks for 178 00:14:16.279 --> 00:14:22.720 the time. operatics has redefined the meaning of revenue generation for technology companies worldwide. 179 00:14:22.720 --> 00:14:28.429 While the traditional concepts of building and managing inside sales teams inhouse has existed 180 00:14:28.509 --> 00:14:33.870 for many years, companies are struggling with a lack of focus, agility and 181 00:14:33.070 --> 00:14:39.580 scale required in today's fast and complex world of enterprise technology sales. See How 182 00:14:39.620 --> 00:14:46.259 operatics can help your company accelerate pipeline at operatics dotnet. You've been listening. 183 00:14:46.299 --> 00:14:50.500 To Be Tob revenue acceleration. To ensure that you never miss an episode, 184 00:14:50.779 --> 00:14:54.490 subscribe to the show in your favorite podcast player. Thank you so much for 185 00:14:54.610 --> 00:14:56.090 listening. Until next time,

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