Democratizing Advanced Analytics Propels Instant Analysis Results to the Ubiquitous Excel Spreadsheet Edge
Democratizing Advanced Analytics Propels Instant Analysis
Results to the Ubiquitous Excel Spreadsheet Edge
Transcript of a discussion on how HTI Labs in London provides the means and governance with
their Schematiq tool to bring critical data to the interface that users want most.
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Dana Gardner: Hello, and welcome to the next edition to the Hewlett Packard Enterprise
(HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host and moderator for this ongoing discussion on digital
transformation. Stay with us now to learn how agile businesses are fending off
disruption in favor of innovation.
Our next case study highlights how powerful and diverse ﬁnancial information
is delivered to the ubiquitous Excel spreadsheet edge. We'll explore how HTI
Labs in London provides the means and governance with Schematiq to bring
critical data to the interface that users want.
By leveraging the best of instant cloud-delivered information with spreadsheets, Schematiq
democratizes end-user empowerment while providing powerful new ways to harness and access
complex information. To describe how complex cloud to core edge processes and beneﬁts can be
managed and exploited, we're joined by Darren Harris, the CEO and Co-Founder of HTI Labs in
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Darren Harris: Thank you. It's great to be here.
Gardner: We're also here with Jonathan Glass, the CTO and Co-Founder of HTI Labs.
Jonathan Glass: Hi. Thank you.
Gardner: Let's put a little bit of context on this ﬁrst. What were some of the major trends that
you were seeing in the ﬁnancial sector that led you to create HTI Labs, and what are the
problems that you're seeking to solve?
Harris: Obviously, in ﬁnance, spreadsheets are widespread and are being used for a number of
varying problems. A real issue started a number of years ago, where spreadsheets got out of
control. People were using them everywhere, causing lots of operational risk
processes. They wanted to get their hands around it for governance, and there
were loads that we needed to eradicate -- Excel type issues.
That led to the creation of centralized teams that locked down rigid processes and
effectively took away a lot of the innovation and discovery process that traders
are using to spot opportunities and explore data.
Through this process, we're trying to help with governance to understand the tools
to explore and the ability to put the data in the hands of people, but ﬁnding the right
balance with governance was a real gap that we could ﬁll with our experience.
So, taking the best of regulatory scrutiny around what this person needs and some innovation that
we put into Schematiq, we see an opportunity to take Excel to another level, but not sacriﬁce the
control that’s needed.
Gardner: Thank you, Darren. Jonathan, anything to add to the trends that have driven you, or
maybe there are technology trends that allowed you to be able to do this where it may not have
been feasible economically or technically before?
Glass: There are lot of really great back-end technologies that are available now, along with the
ability to either internally or externally scale compute resources. Essentially, the desktop remains
quite similar. Excel has remained quite the same, but the upstream capabilities
have really grown.
So there's a challenge there. Data that people feel they should have access to is
getting bigger, more complex, and less structured. So Excel, which is this great
front-end to come to grips with data, is becoming a bit of bottleneck in terms of
actually keeping up with the data that's out there that people want to get.
Gardner: So, we're going to keep Excel. We're not going to throw the baby out
with the bathwater, so to speak, but we are going to do something a little bit different and
interesting. What is it that we're now putting into Excel and how is that different from what was
available in the past?
Harris: Schematiq extends Excel and allows it to access unstructured data. It also reduces the
complexity and technical limitations that Excel has as an out-of-the-box product.
We have the notion of a data link that's effectively in a single cell that allows you to reference
data that’s held externally on a back-end site. So, where people used to ingest data from another
system directly into Excel and effectively divorce it from the source, we can leave that data
where it is.
It's a paradigm of take a question to the data; don’t pull the data to the question. That means that
we can leverage the power of the big-data platforms and how they process an analytic
database in the back-end, where you can effectively use Excel as the front screen.
Ask questions from Excel, but push that query to the back end.
That's very different in terms of the model that most people are
used to working with Excel.
Gardner: And that's a two-way street. In the past, an XML stream might have
been able to bring in data on a live or recurring basis, but this is a two-way street. It's a bit
different, and you're also looking at the quality, compliance, and regulatory concerns over that
Harris: Absolutely. An end user is able to break down or decompose any workﬂow process with
data and debug it the same way they can in a spreadsheet. The transparency that we add on top of
Excel’s use with Schematiq allows us to monitor what everybody is doing and the function
they're using. So, you can give them agility, but still maintain the governance and the control.
In organizations, lots of teams have become disengaged. IT has tried to create some central core
platform that’s quite restricted, and it's not really serving the users. They have just gotten
disengaged and they've created what Gartner referred to as the Shadow BI Team, with databases
under their desk, and stuff like that.
By bringing in Schematiq we add that transparency back and we allow IT and the users to have
an informed discussion, a very analytic conversation, around what they're using, how they are
using it, where the bottlenecks are, and then, work out where the best value is. It's all about
agility and control. You just can't give the self-service tools to an organization and not have the
transparency for any oversight or governance.
To the edge
Gardner: So we have, in a sense, brought this core or cloud to the edge. We've managed it in
terms of compliance and security. Now, we can start to think about how creative we can get with
what's on that back end that we deliver. Tell us a little bit about what you go after, what your
users want to experiment with, and then how you enable that?
Glass: We try to be as agnostic to that as we can, because it's the creativity of the end user that
really drives value.
We have a variety of different data sources, traditional relational databases, object stores, OLAP
cubes, APIs, web queries, and ﬂat ﬁles. People want to bring that stuff together. They want some
way that they can pull this stuff in from different sources and create something that's unique.
This concept of putting together data that hasn't been put together before is where the sparks start
to ﬂy and where the value really comes from.
Gardner: And with Schematiq you're enabling that aggregation and cleansing ability to
combine, as well as delivering it. Is that right?
Harris: Absolutely. It's that discovery process. It may be very early on in a long chain. This
thing may progress to be something more classic, operational, and structured business
intelligence (BI), but allowing end users the ability to cleanse, explore data, and then hand over
an artifact that someone in the core team can work with or use as an asset. The iteration curve is
so much tighter and the cost of doing that is so much less. Users are able to innovate and put
together the scenario of the business case for why this is a good idea.
The only thing I would add to the sources that Jon has just mentioned is with Haven OnDemand,
the unstructured analytics, giving the users the ability to access and leverage all of the IDOL
capabilities. The capability is a really powerful and transformational thing for businesses.
They have such a set of unstructured data available in voice and text, and when you allow
business users access to that data, the things they come up with, their ideas, are just quite
Technologists always try to put themselves in the minds of the users, and we've all historically
done a bad job of making the data more accessible for them. When you allow them the ability to
analyze PDFs without structure, to share that to analyze sentiment, to concepts and entities, or
even enrich a core proposition, you're really starting to create innovation. You've raised the
awareness of all of these analytics that exist in the world today in the back end, shown end users
what they can do, and then put their brains to work discovering and inventing.
Gardner: Many of these ﬁnancial organizations are well-established, many of them for hundreds
of years perhaps. All are thinking about digital transformation, the journey, and are looking to
become more data-driven and to empower more people to take advantage of that. So, it seems to
me you're almost an agent of digital transformation, even in a very technical and sophisticated
sector like ﬁnance.
Making data accessible
Glass: There are a lot of stereotypes in terms of who the business analysts are and who the
people are that come up with ideas and intervention. The true power of democratization is
making data more accessible, lowering the technical barrier, and allowing people to explore and
innovate. Things always come from where you least expect them.
Gardner: I imagine that Microsoft is pleased with this, because there are some people who are a
bit down on Excel. They think that it's manual, that it's by rote, and that it's not the way to go. So,
you, in a sense, are helping Excel get a new lease on life.
Glass: I don’t think we're the whole story in that space, but I love Excel. I've used it for years
and years at work. I've seen the power of what it can do and what it can deliver and I have a bit
of an understanding of why that is. It’s the live nature of it, the fact that people can look at data
in a spreadsheet, see where it’s come from, see where it’s going, they can trust it, and they can
believe in it.
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That’s why what we're trying to do is create these live connections to these upstream data
sources. There are manual steps, download, copy/paste, move around the sheet, which is where
errors creep in. It’s where the bloat, the slowness, and the unreliability can happen, but by
changing that into a live connection to the data source, it becomes instant and it goes back to
being trustable, reliable, and actionable.
Harris: There's something in the DNA, as well, of how people interact with data and so we can
lay out effectively the algorithm or the process of understanding a calculation or a data ﬂow.
That’s why you see a lot of other systems that are more web-based or web-centric replicate an
The user starts to use it and starts to think, "Wow, it’s just like Excel," and it isn’t. They hit a
barrier, they hit a wall, and then they hit the "Export" button. Then, they put it back (into Excel)
and create their own way to work with it. So, there's something in the DNA of Excel and the way
people lay things out. I think of it (Excel) almost like a programing environment for non-
programers, some people describe it as a functional language very much like Haskell, and the
Excel functions they write were effectively then working and navigating through the data.
Gardner: No need to worry that if you build it, will they come; they're already there.
Gardner: Tell us a bit about HTI Labs. Let’s get off of the data discussion for just a bit. Tell us
about your background, how your company came about, and where you are on your evolution.
Harris: HTI labs was founded in 2012. The core backbone of the team actually worked for the
same Tier 1 investment bank, and we were building risk and trading systems for front-ofﬁce
teams. We were really, I suppose, the cutting edge of all the big-data technologies that were
being used at the time -- real time, disputed graphs and cubes, and everything.
As a core team, it was about taking that expertise and bringing it to other industries. Using
Monte Carlo farms in risk calculations, the ability to export data at speed and real-time risk.
These things were becoming more centric to other organizations, which was an opportunity.
At the moment, we're focusing predominately on energy trading. Our software is being used
across a number of other sectors and our largest client has installed Schematiq on 120 desktops,
which is great. That’s a great validation of what we're doing. We're also a member of the London
Stock Exchange Elite Program, based in London for high-growth companies.
Gardner: Jonathan, your background.
Glass: Darren and I met when we were working for the same company. I started out as a quant
doing the modeling, the map behind pricing, but I found that my interest lay more in the
engineering. Rather than doing it once, can I do it a million times, can I do these things reliably
and scale them?
Because I started in a front-ofﬁce environment, it was very spreadsheet-dominated, it was very
VBA-dominated. There's good and bad in that. A lot of those lessened, and Darren and I met up.
We crossed the divide together from the top-down, big IT systems and the bottom-up end-user
best-developed spreadsheets and so on. We found a middle ground together, which we feel is a
quite powerful combination.
Gardner: Back to where this leads. We're seeing more-and-more companies using data services
like Haven OnDemand and starting to employ machine learning, artiﬁcial intelligence (AI), and
bots to augment what the humans do so well. Is there an opportunity for that to play here or
maybe it already is? The question basically is, how does AI come to bear on what you can deliver
in terms of that higher quality product out to those Excel edges?
Harris: I think what you see is that out of the box, you have a base unit of capability. The
algorithms are built but the key to making them so much more improved is the feedback loop
between your domain users, your business users, and how they can enrich and train effectively
So, we see a future where the self-service BI tools that they use to interact with data and explore
would almost become the same mechanism where people will see the results from the algorithms
and give feedback to send back to the underlying algorithm.
Gardner: And Jonathan, where do you see the use of bots, particularly perhaps with an API
model like Haven OnDemand?
The role of bots
Glass: The concept for bots is replicating an insight or a process that somebody might already
be doing manually. When people create these data ﬂows and analyses that they maybe run once
so it’s quite time-consuming to run, the real exciting possibility there is that you make these
things run 24×7. So, you start receiving notiﬁcations, rather than having to pull from the data
source. You start receiving notiﬁcations from your own mailbox that you have created. You look
at those and you decide whether that's a good insight or a bad insight, and you can then start to
train it and reﬁne it.
The training and reﬁning is that loop that potentially goes back to IT, gets back through a
development loop, and it’s about closing that loop and tightening that loop. That's the thing that
really adds value to those opportunities.
Gardner: Perhaps we should unpack Schematiq a bit to understand how one might go back and
do that within the context of your tool. Are there several components of the tool, one of which
might lend itself to going back and automating on that more bot level?
Glass: Absolutely. You can imagine the spreadsheet has some inputs and some outputs. One of
the components within the Schematiq architecture is the ability to take a spreadsheet, to take the
logic and the process that’s embedded in our spreadsheet, and turn it into an executable module
of code, which you can host on your server, you can schedule, you can run as often as you like,
and you can trigger based on events.
It’s a way of emitting code from a spreadsheet. You take some of the insight, you take without a
business analysis loop and a development loop, and you take the exact thing that the user, the
analyst, has programmed. You make it into something that you can run, commoditize, and scale.
That’s quite an important way in which we reduce that development loop. We create that cycle
that’s tight and rapid.
Gardner: Darren, would you like to explain the other components that make-up Schematiq?
Harris: There are four components of Schematiq architecture. There's the workbench that
extends Excel and allows the ability to have large structured data analytics. We have the asset
manager, which is really all about governance. So, you can think of it like source control for
Excel, but with a lot more around metadata control, transparency, and analytics on what people
are using and how they are using it.
There's a server component that allows you just to off-load and scale analytics horizontally, if
they do that, and build repeatable or overnight processes. The last part is the portal. This is really
about allowing end users to instantly share their insights with other people. Picking up from
Jon’s point about the compound executable, but it’s deﬁned in Schematiq. That can be off-loaded
to a server and exposed as another API to a computer, the mobile, or even a function.
So, it’s very much all about empowering the end-user to connect, create, govern, share instantly
and then allow consumption from anybody on any device.
Market for data services
Gardner: I imagine, given the sensitive nature of the ﬁnancial markets and activities, that you
have some boundaries that you can’t cross when it comes to examining what’s going on in
between the core and the edge, but there might be some metadata and interesting patterns that
you could delve into and explore that then might give you an opportunity to see a marketplace
for data services.
Tell me about how you, as an organization, can look at what’s going on with the Schematiq and
your backend, what the democratization and the users are then exercising that democracy with,
and whether that creates another market for data services when you see what the demand entails.
Harris: It’s deﬁnitely the case that people have internal datasets they create and that they look
after. People are very precious about them because they are hugely valuable, and one of the
things that we strive to help people do is to share those things.
Across the trading ﬂoor, you might effectively have a dozen or more different IT infrastructures,
if you think of what’s existing on the desk as being a miniature infrastructure that’s been created.
So, it's about making easy for people to share these things, to create master datasets that they
gain value from, and to see that they gain mutual value from that, rather than feeling closed in,
and don’t want to share this with their neighbors.
If we work together and if we have the tools that enable us to collaborate effectively, then we can
all get more done and we can all add more value.
Gardner: It's interesting to me that the more we look at the use of data, the more it opens up
new markets and innovation capabilities that we hadn’t even considered before. And, as an
analyst, I expect to see more of a marketplace of data services. You strike me as an accelerant to
Harris: Absolutely. As the analytics are coming online and exposed by API’s, the underlying
store that’s used is becoming a bit irrelevant. If you look at what the analytics can do for you,
that’s how you consume the insight and you can connect sources that exist. You can connect from
Twitter, you connect from Facebook, you can connect PDFs, whether it’s NoSQL, structured,
columnar, rows it doesn’t really matter. You don’t see that complexity. The fact that you can just
create an API key, access it as consumer, and can start to work with it is really powerful.
There was the recent example in the UK of a report on the Iraq War. It’s 2.2 million words, it
took seven years to write, and it’s available online, but there's no way any normal person could
consume or analyze that. That’s three times the complete works of Shakespeare.
Using these APIs, you can start to pull out mentions, you can pull out countries, locations and
really start to get into the data and provide anybody with Excel at home, in our case, or any other
tool, the ability to analyze and get in there and share those insights. We're very used to media
where we get just the headline, and that spin comes into play. People turn things on their, head
and you really never get to delve into the underlying detail.
What’s really interesting is when democratization and sharing of insights and collaboration
comes, we can all be informed. We can all really dig deep, and all these people that work there,
the great analysts, could start to collaborate and delve and ﬁnd things and ﬁnd new discoveries
and share that insight.
Gardner: All right, a little light bulb just went off in my head whereas we would go to a
headline and a new story and we might have a hyperlink to a source. I could get a headline and a
news story, open up my Excel spreadsheet, get to the actual data source behind the entire story
and then probe and plumb and analyze that any which way I wanted to.
Harris: Yes, Exactly. I think the most savvy consumer now, the analyst, is starting to demand
that transparency. We've seen in the UK, words, election messages and quotes and even ﬁnancial
stats where people just don’t believe the headlines. They're demanding transparency in that
process, governance can only be really a good thing.
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Gardner: I'm afraid we will have to leave it here. We've been exploring how powerful and
diverse ﬁnancial information is delivered to the ubiquitous Excel spreadsheet edge and we have
learned how HTI Labs in London provides the means and governance with their Schematiq tool
to bring critical data to the interface that users want most.
So, please join me in thanking our guests. We have been here with Darren Harris, the CEO and
Co-Founder of HTI Labs. Thank you, Darren.
Harris: Thank you.
Gardner: And also we have been here with Jonathan Glass, the CTO and Co-Founder of HTI
Labs. Thank you, Jonathan.
Glass: Thanks very much.
Gardner: And a big thank you to our audience as well, for joining us for this Hewlett Packard
Enterprise Voice of the Customer digital transformation discussion.
I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this
ongoing series of HPE sponsored interviews. Thanks again for listening, and please do come
back next time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor: Hewlett
Transcript of a discussion on how HTI Labs in London provides the means and governance with
their Schematiq tool to bring critical data to the interface that users want most. Copyright
Interarbor Solutions, LLC, 2005-2016. All rights reserved.
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