New Knowledge Dimensions | Enterprise Journey Information

The Data Exchange
collects, tracks and analyzes data on $60 billion in business travel spend
annually. It provides data and analysis to its subscribers, which may be
financial institutions, business and marketing concerns, governments and, yes,
some corporate travel buyers. The data it collects, reports on and analyzes can
come directly from travel suppliers, but also from a variety of government and
industry sources as well as from individual corporations that contribute data
to the company’s “data lake”. As such, founder and CEO Susan Hopley has a
bird’s eye view of advancement in data security, data sharing and data
interrogation and analysis. BTN editorial director Elizabeth West caught up
with Hopley to talk about how key technologies like blockchain and artificial
intelligence are changing the data world. In BTN’s extended online piece, she
talks about how those developments could fundamentally change travel
management.

How long have you
been working with blockchain, and how has it changed your business?

Susan Hopley: The pillars of data are really privacy,
security and access. The challenge has been sharing that data while working
within those parameters. [The data industry] hasn’t really been able to do
that. We began working with blockchain technology about two years ago,
assigning digital signatures or tokens to the data sources, such that each
piece of data is ostensibly de-identified from its source but also permanently
appended with that signature. On the blockchain, each piece of certified,
verified and de-identified data can be shared with verified subscribers to that
[individual] blockchain. We know everywhere each piece of data goes. That’s an
exciting place to be. 

The Data Exchange
operates on the idea that if you contribute data, you should be compensated for
it. Tell me more about that. What that could enable for travel buyers?

Hopley: Data isn’t a monetary commodity, but
understanding its value and creating a fairer sharing of that value is
important. If I want to understand my carbon footprint, for example, I probably
have to pay somebody to do that. Fair enough. But I also want something for the
data that I’m sharing that contributes to that calculation. It’s possible you
don’t want to share your own data and you just want to know what’s going on
“out there,” and that’s fine. But if you contribute data to the pie, you want a
piece of the revenue that comes from it. It’s easy to value the data, but you
need to know your data is appropriately controlled, distributed and compensated
for—and it would be compensated each time it is shared and consumed, not just
in one transaction.

That is also now
facilitated by a blockchain because that digital signature associated with the
data can be tracked?

Hopley: Absolutely. It’s the only way to do it.

Let’s talk about
AI and how it’s contributing to better business travel data. What are
AI-powered processes doing now that they weren’t doing a few years ago?

Hopley: It may be horrible to think that
machines are brighter than we are, but in certain ways, they can be. Travel
data is very rich data, and there’s so much valuable information embedded in
it. Our financial house clients correlate it to the economy, to stocks.
Governments use it for development decisions. But it has to be good quality
data, and that’s where AI can really come into play. It obviously needs to be
taught, and we have written many scripts for it. But at this point, the
algorithms display this kind of hexagonal “thinking” that is able to ping out
on so many different dimensions and return to us the possible data aberrations
that we need to address. It does this in ways that humans really can’t do
because we have to look at it in a linear fashion—first, this, and then this
and then this. But the machine doesn’t have to do that. So the capability is
kind of exploding and showing us ways to improve our industry data.
  

What will that
improved data and AI layers begin to bring to travel programs?

Hopley: There are so many ways you can look at
data and analyze it, but people aren’t doing it because they’re in their little
silos. And even if you are benchmarking with a TMC, maybe you are looking at
five “like” companies or maybe 10. But why not look at a much bigger data
universe and see what’s really going on in the market—especially if you can
apply AI layers to it? AI will take all the data and information we’ve got, and
it will be able to tell you—probably accurately, but we’ll have to make sure
the models are right—which carriers you should be doing business with and what
rates, or how your contracts should be different. It’s not going to be
comparing one company’s negotiated rate against another. Rather, it will
understand the market and the individual program and be able to deliver
insights. That’s where we’re going with it. And not just for airline contracts,
but that’s an example, and I find it absolutely fascinating. 

You will need the
data from corporations to do that.

Hopley: Yes, but why not contribute the data if 1)
it’s completely de-identified and 2) the corporate stands to be compensated for
it each time another company pings against it?

Is there a point
at which all this just becomes available in a booking tool in real time? Or,
for that matter, it’s able to project a future rate to inform bookings at the
point of sale?

Hopley: I think decision-making in corporate travel
has been relatively myopic based on too little information at the time of
purchase. So currently it may be better to have a rule in place and a
negotiated rate to control spend to a known level, rather than making the best
decision with the information you have in the moment.

So what happens
to the concept of negotiated rates if there’s real-time data visibility? Are
you saying there won’t be a need for them?

Hopley: I think that’s the right question, but I think
it’s going to evolve because we don’t have validated proof that it will work.
We assume it will work, so we move into this process, and we assume it will be
better, but it’s too early to say. However, I’m going to go down that path [of
trying to provide decision-making data at the point of sale] because I think it
will, and I think it will make life a lot easier.

Is it all about
rate, or how would you facilitate other decision-making factors?

Hopley: Take a hotel, for example. Does it have a
restaurant? Does it have a swimming pool? Does it have a club floor or a
meeting space? There are all types of decision-making factors when you are
booking a hotel room. Does it have the amenities you need for the trip, and
what is the price? Travel managers may want to think that a hotel is booked
because it’s company policy and, therefore, it’s the sensible thing to do. It
might be that it’s not the sensible thing to do. What if an adjacent hotel has
better amenities, a meeting room and costs less? In an AI system, they could
collect all the individual pieces of data quickly and assemble them in a way in
which someone, instead of making a decision based on policy or—sometimes—on
points, they can make the decision based on the value to them. Then, looking at
that data, again aided by AI, the buyer could learn what specifically their
travelers value and, potentially, drive better and more effective deals if they
do continue to negotiate.

Thank you so much
for spending the time to speak with me, Susan.

Hopley: It’s been a pleasure. Bye from the U.K.
It’s time for tea.