Episode Transcript
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You were listening to bb revenue acceleration, a podcast dedicated helping software executive stay
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on the cutting edge of sales and
marketing in their industry. Let's get into
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the show. Welcome to be to
be a revenue acceleration. My name is
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over, I am with you and
I'm here today with Tommac from qubold.
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Are you doing to Daytam, very
well right. How about yourself? I
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am doing fantastically well. So today
we want to talk about you, about
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the big data landscape in a first
changing economy. That's a big, big
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topic. Probably at the pig that
is is is very close to your hearts,
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based on the world that you are
doing at quebold. But before we
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go into the details, can you
please tell us a little bit more about
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yourself, your role within q bowl
and I guess what q bold does as
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an organization? Sure. So,
I joined you all up four years ago
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in the United States and built out
the sales team in the western US and
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the management team asked me to open
up the European operation. So my family
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and I moved to London a year
ago this time to open up your office
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and drive and create a business here
in Europe focused out of London and we've
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since then built a team and you
are on a track to really expand that
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business and that opportunity throughout Europe.
What q will does is it does provide
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essentially big data as a service,
with the idea of allowing automation to handle
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the life cycle of clusters so that
organizations can focus on getting insight and yield
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out of the data as opposed to
managing the infrastructure associated with those big data
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technologies. We're on track for about
three hundred customers and growing very well.
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A couple of million queries executed against
cuble and we're processing PEDA weights of data,
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close to an exhibited data per month
that we have our clients, but
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that sounds like a no full lot
of data. That so obviously one of
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the conversation point to then, and
the reason why we want you to have
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utim, is to discuss about the
big data landscape. And we know that
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big data as became a game change
in most modern industries of other last few
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years and while the technologies have evolved
and there is a lots of data as
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well in organization. So I think
it's fair to say that organization of more
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and more data they've got one more
complex data they probably have more and more
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systems holding the data together. Could
you please share with us a bit about
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how the actual big data landscape is
looking at the momentum, from yokels pick
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teeth, but also what do you
believe all the trends for the future?
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I think I'd probably start with the
major trend, of is migration of big
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data workloads to the public cloud.
We're seeing a significant number of clients here
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in Europe already in the cloud or
having plans to migrate their data workloads to
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the cloud, and that's for two
reasons. One is that the resources needed
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for big data are typically quite elastic, so the whole on demand elasticity of
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the cloud plays very well for that. But there's also this separation of compute
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and storage, so the object stores
of the respective clouds, or that's Azure,
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Google or Amazon. That's a very
cost effective way to store pat of
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bits of data and then, which
is very inexpensive relative to running it as
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hdfs and a traditional data center in
a traditional data center on an expensive compute
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or expensive machine. And so what
that means is that you're able to do
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in a very cost effective way store
your data and then con use the elasticity
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of the cloud and the virtual machines
in those cloud providers to them process that
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data on them as needed data basis. So it allows you to scale up
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very quickly and then, with the
changes within the cloud to per second billing,
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allows you to be very aggressive on
the downscaling. So you're constantly trying
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to optimize the current cluster based on, you know, the current need.
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So that's one trend that we see. The other one is that people are
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more and more interested in streaming analytics, so as data is collected in real
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time to do as much analytics as
as possible, and use cases for that
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are a lot around ECOMMERCE, pricing
optimization. We see a lot of anomaly
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detection within streams to understand how the
business is actually prefer me, and I
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guess the third trend that we're seeing
a lot is more of enabling of self
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service analytics, so providing very broad
access to that organizations but doing it in
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a very fine grained way as kind
of that third trend. Okay, so
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it's basically doing more for less.
So getting the advantage of the public cloud
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is really around its cust saving.
It would believe, and also probably the
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pain of managing it at a center, because we know that that doesn't is
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not easy to manage. You need
to deal with redundancy and all that sort
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of things. We also it seems. It seems from the tools of points
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that you've mentioned that it's about the
change in conception of the data. It's
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about the change of what we use
data for. How do we think that
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data to make business decisions. So
it's really do you see also that the
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transition into the business intelligence, the
way people are making decision based on the
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data they collect? Well, would
you say, is the same, but
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it just got more granularity. Now
I think it's very smaller in that use
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case, but they do have larger
data sets that they want to use to
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whatever you company is goal is to
do, is enable operating markets or,
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you know, decisions closer to the
consumer. You can allow the organization to
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do that. And what they're trying
to do is, rather than just basic
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operation of they that are trying to
append that with other social data and other
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analytrics or data that they might purchase
from third party providers to provide a better
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view of the world so that they
ultimately can make better but better business decisions
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on behalf of the consumer themselves or
their consumers are there and customers. Does
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that mean then, that you see
new functions, so marketing for example,
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or finance so but new function within
organization asking for more intelligence, asking for
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more of that data of or requiring
to use more big data solution in order
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to, I guess, create efficiency
or accelerate revenue in down function. Do
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you see those lanes of business consuming
all definitely so. I think that's one
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of the trends that organizations are trying
to do, is provide a larger data
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set to be able to or data
sets and doing curated data sets, but
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at a much larger scale for the
lines of business and though their internal customers,
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so that people can make a very
informed decision, you know, tactical
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and strategic within an organization. So
it is definitely a situation where both the
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internal customers are asking for more data
sets to be included, which means the
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infrastructure has to change, and that's
where products like ours play a role and
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we typically, in most cases,
will sit next to existing investments. But
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providing that broader access to larger data
sets, because it's focused on big data
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technologies as approves to traditional R DMS
has or relational databases and a price datawarehouses.
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Okay, that makes sense, I
think. I think we see a
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lot of that in the market Al
Self. So that's good. A question
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about the functions and the line of
business and all the people within the organization
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that comes from all that big data
on in Moll that's intelligence refine intelligence from
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big data. Do you see any
specific verity chords of specific industries also that
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have a small that's requalment f implementing
big detest solution and and also it's kind
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of a two side equation. You
have comments. So do you have examples
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of the kind of resorts they can
expect from implementing big big data solutions?
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I think one of the trends that
we see across the company and not just
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in Europe, for London especially being
a heavy retail environment, there's a lot
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of mobile application and well documented that
more and more people are sending and buying
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to the mobile experience, but that's
an iphone, ipad, some sort of
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device like that of the laptop itself, and so what we see out there
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is more under of really understanding the
user journey within the applications themselves, a
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lot of ab testing that goes along
with that as well, and then really,
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once they solidify that, executing quickly
on price optimizations based on competitors and
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competitive an analysis as well. So
it could be across the board. We
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have several companies that are in the
travel industry that are really focused on obviously
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maintaining margin, but having a very
aggressive pricing strategy to win that business,
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and the same thing can be said
on consumer retail as well. So in
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certain situation rations, you know,
with AB testing, some of the metrics
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that we've seen from one kind in
particular is about a seven percent of lift
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and revenue as a result of better
decisions around how content is surfaced within their
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mobile application. That's wonderful. So
thank you very much for sharing allder that
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about the big data market and all
things are evolving at the moment. It's
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very useful tree term. One final
question that I've got for you. So
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we know that qubold organization is growing
fast, ending into new region. We
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ownder some from your introduction that you
came from North America, from San Francisco,
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I believe to be to be to
be accurate, to London. If
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I remember correctly, the first time
we met you actually fresh of the plane
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and we met for a coffee in
London. So I guess my next question
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is more it a bit of a
personal question to you and I'd like to
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I'd like to onner so much is
your experience. So if you can share
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your experience as an American coming into
the UK and what you've seen as the
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main differences between the American market?
Well, obviously you've been successful and that's
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the reason why your management team wanted
you to come to Europe and use that
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experience to push the European market and
get that to take off, but from
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your perspective. So I guess it's
more of a personal question. was on
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the business question, but I'm always
interested to understand the true jual differences that
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you've seen the way business is done. So yeah, you get to get
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to get you with some that's sure. I think, to be fair,
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when I first started a huble we
were very new and we're very targeted towards
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specific verticals and then as we grew
we started to expand and what I'm seeing
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is that we have to do more
of that here. So I think it's
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I think the answer is kind of
twofold. One is that there's a lot
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more education that has to happen here
because the market has changed in the four
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years I've been with cuble. There's
a lot of people that in companies that
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are coming out or have been out
for a while and, you know,
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everybody's mixing marketing messages promising the world
when it comes to analytics and big data.
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So I think the customers are a
little more conservative here in London and
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in Europe, but they also have, you know, much more to look
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at these days and really kind of
do a lot of fetting. So the
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process is a little bit slower and
people making a decision for or against because
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there's much more to look at right
now and there's a lot to weed through
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in the actual market itself. A
lot of different players out there. So
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that's one of the challenges that we've
seen. It's we still win business,
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but it just seems to take a
little bit longer and the education seems to
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take a little longer. The other
issue, I didn't talk about it in
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one of the trend conversation that you
talked about up but the pace at which
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all these open source technologies are moving
and evolving and, yeah, promise that
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goes along with them is substantial.
So you know, you have this trend
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to go from machine learning to deep
learning right now, with all the tensorflow,
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cares mx that in the different libraries
around the data science world, and
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those are really challenging to keep up
with, and the decisions how to use
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those in the most appropriate manner are
really tough as well, and the skill
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set needed for those is very difficult. So you have to have setting the
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proper expectations with clients to say that
it's not going to be, you know,
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a very quick use of these deep
learning technologies. There's a learning curve
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associated with it and then there's the
whole migration to production that goes along with
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it as well. So expectation settings
for results is and setting those properly for
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clients is something that we're really trying
to be very specific around, given the
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pace that with technologies and the evolution
of those technologies as well. So that's
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okay, Great. What's that's good? What's essentially your insight? I'm sure
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you apology has got to have a
lot of taking from from the different things
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that you discussed today, which we
do every single time we ask our guests
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to give us away or give away
to our least, not to get in
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touch with them up to get in
touch with that company. They want to
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have a bit more of a conversation
with you as an individual or, if
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they won't engage with you, to
discuss about your solution. In your case,
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that would be discussing about cuboard.
So toub what is the best way
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to get in touch with you?
Sure best way is just Tom at qubolecom
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to U Bolcom and is Paya email
and happy to respond and answer any questions
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that people may have. Best ononderful
thank you very much. Really appreciates your
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payment inside today. It's great speaking
with you as always. Ready thanks for
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the time. operatics has redefined the
meaning of revenue generation for technology companies worldwide.
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While the traditional concepts of building and
managing inside sales teams inhouse has existed
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00:14:28.509 --> 00:14:33.870
for many years, companies are struggling
with a lack of focus, agility and
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00:14:33.070 --> 00:14:39.580
scale required in today's fast and complex
world of enterprise technology sales. See How
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operatics can help your company accelerate pipeline
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