Transcript of "Part 4 of 4: Real-Time Web Data Services in Action at Deutsche Börse"
Part 4 of 4: Real-Time Web Data Services in Action at
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intelligence, one of a series on web data services with Kapow Technologies.
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Dana Gardner: Hello and welcome to a special BrieﬁngsDirect dual webinar and podcast
presentation, "Real-Time Web Data Services in Action at Deutsche Börse." I'm
your host and moderator, Dana Gardner, principal analyst at Interarbor Solutions.
As the culmination of a four-part series on web data services (WDS), we're here
to examine a fascinating use case for data services with Deutsche Börse Group in
Frankfurt, Germany. An innovative information service recently created there
highlights how real-time content and data assembled from various online sources
scattered across the Web provides a valuable analysis service.
The offering supports energy traders seeking to track global ﬂuctuations and micro trends in oil
and other related markets. But, the need for real-time and precise data affects more than energy
traders and ﬁnancial professionals. More than ever, all sorts of businesses need to know what's
going on in and what's being said about their respective markets, products, and services.
In this series with Kapow Technologies, we've examined the need for WDS and ways that WDS
and related tools can be used broadly to solve these problems. Now, we are going to learn the full
story of how Deutsche Börse took web data resources, and not only efﬁciently assembled
knowledge from automated robots, cleansing tools, and analytics management, but from these
capabilities they also created high value and focused WDS offerings onto itself.
Thanks for joining us, as we take an in-depth look at how the market for WDS has shaped up,
quickly recap the major ﬁndings from our series so far, and then hear directly from the leader of
the Deutsche Börse project, as well as from a key supplier that supported them in accomplishing
their web services goal.
So to learn more about WDS as a business, please join me in welcoming our guests, Mario
Schultz, director of Energy Facts at Deutsche Börse Group.
Mario Schultz: Hi. I'm happy to be here and looking forward to the session today.
Gardner: Stefan Andreasen is also with us. He is the CTO at Kapow Technologies in Palo Alto,
California. Welcome back, Stefan.
Stefan Andreasen: Thank you Dana. It's a pleasure to be here.
Gardner: First, let me try to set the stage for how WDS becomes the grist for new analysis
mills. We've been through quite a transition in the past 10 or 15 years. We have moved quickly as
a result of the Web. We started not too long ago with very proprietary content, often bound in
books and distributed by trucks, and it was perhaps six months or a year outdated, in terms of the
facts and ﬁgures, by the time it was fully distributed.
The Web really helped accelerate the time, but was still chaotic in terms of the types of content.
It was really loosely coupled information and not very well structured or organized and wasn’t
necessarily of a business-critical nature.
We quickly saw, during the late '90s and into the 2000s, that the use of middleware and objects
and standards, like SQL, and use of relational databases started to cross over into what became
more considered general content, not necessarily data or content, but what people used to do in
Now, we've moved along through how organizations manage their applications and data together
through use of XML, web services, and service oriented architecture (SOA), to the point where
we are now, at the level of WDS. We're beginning now to manage that much better and bring
automation, low risk, and security to those uses.
It's interesting to me that we've moved beyond a level of static information to dynamic
information and yet we still haven’t taken full advantage of everything that’s being developed
and created across the Web.
But, today’s market turbulence demands that we do that. We have to move into an era, where we
can take quality data and provide agility into how we can consume and distribute it. We're
dealing with more diverse data sources. That means we need to have completeness and we need
to be comprehensive, in order to accomplish the business information challenges each business
The need now is for ﬂexible, agile, and mixed sourcing of services and data together. The content
is often portable. That means it's ubiquitous across mobile devices and social networks in such a
way that real-time analytics becomes extremely important. This cuts across many different
verticals, from retail, to trading and ﬁnance, healthcare, defense, and government.
The use of data as a business is now coming to the fore. We're beginning to see value, not from
just the assimilation of data for use internally, but as more and more businesses are starting to
take advantage of the data that they create and have access to. They share that with their partners,
create ecosystems of value, and then even perhaps sell outright the information, as well as
insights and analysis from that information.
According to Forrester Research, WDS describes the end-to-end analytic information pipelining
process, a stream of liquid intelligence. It's palatable and consumable. I've also looked at the
Wikipedia deﬁnition, and it seems to me that we have gone well into the ability of mashup and
reuse of information. It's really about the technologies around discovery extraction, moving into
consolidation and access, and then external source and distribution.
To me, WDS really means the lifecycle of content use and reuse across the Web, not in a chaotic
fashion, but in a managed fashion, with security permissions, access control, and the ability to
bring it into play with other analytic applications and business intelligence (BI) processes.
I want to go now to Mario. When you think of WDS, how has this deﬁnition really impacted you
and your business?
At the beginning
Schultz: Since the beginning, I worked on the on-exchange information that we have in our own
systems. We were proceeding with our ideas of enhancing our services and
designing new products and services. We were looking into the Web and trying to
get more information from the data that we gather from websites or somewhere
else on the global Web to integrate this with our company internal information.
Everything we do focuses on the real-time aspect. Our WDS is not stable, but it's
always focusing on the real-time aspects of this side of the story.
Gardner: Before we get into the fuller Deutsche Börse story, I'd like to revisit our
podcast series so far. In our ﬁrst podcast we talked with Howard Dresner, a real leader and
thought developer in BI. He told us quite a bit about the need for bringing more sources, just as
Mario pointed out, both internal and external, into an analytic process.
The idea of extended data sources forms strong components of forecast and analytic activities
that are now underway, according to Howard, and BI needs to be not constrained or limited by
the need for timely and relevant information from any web source. Howard really reinforced the
notion for me that the Web has become where structured data was 10 or 15 years ago and is
important for enterprises doing analytic activity.
In the second podcast in our series, Forrester's Jim Kobielus talked about the need to know
what's going on and how important it is for organizations to have a sense of what the people in
the organization and outside the organization across the spectrum of their supplied chain and/or
distribution networks and actual end-users are doing and saying.
We've really seen an increase in networking, social networks, and social media. There's all this
buzz going on about business activities, products, and services, all of which can be extremely
valuable. You can think of it as a massive real-time focus group, but only if you can access the
information that's relevant. People are willing to tell you what they think, if you're able to scoop
it up. And, it was about this ability to scoop up the data and information and inference that Jim
Kobielus really honed in on.
He told us a lot about the identity gathering, cleansing, and the ability to then exercise the
content in some sort of meaningful way. He also emphasized the need to
manage this in terms of marts and warehouses. A lot of infrastructure has
been put in place. But, again, the value of the infrastructure is only as good
as the value of the actual content that's involved.
In the third part of the series -- we are now in the fourth and last part -- Seth
Grimes, another thought leader in terms of web analytics and text analytics, talked about the
need to analyze in real-time. He emphasized the need of structured data as important, but real-
time data as being the next big thing to move us to the era of advanced analytics. We're not just
telling what happened before in the pipeline or supply chain, but what's going to happen next.
This, I think, bears quite a bit on what Mario is going to discuss.
So, let’s move along now to Deutsche Börse. Mario, I want to hear more about this organization
for our listeners in North America. Tell us a little bit about your company, your organization, and
what you do.
Small business lines
Schultz: Deutsche Börse is the German stock exchange in Frankfurt, Germany, and we offer all
kinds of products and services around on-exchange trading and the adjacent processes. That
means we have made small business lines at Deutsche Börse.
We have something that’s called Xetra, our electronic trading system for cash products. We have
Eurex, our derivative business line, which is worldwide, well-known, where you can trade other
derivatives on that platform.
We have a subsidiary that’s called Clearstream doing all the custody and clearing services after
you have done your trade. And, we have the Market Data & Analytics (MD&A) business line,
where I've been working for 10 years. The MD&A business line is responsible for the real-time
delivery of information to the world outside.
We have a main system called CEF. It is our backbone IT solution for delivering data in real-time
with milliseconds optimization. The data is mainly coming from our internal IT systems, like
Xetra and Eurex, and we deliver this data to the outside world.
In addition, we calculate all the relevant indices, like the DAX, the ﬂagship index for the German
markets with 30 instruments, and more than 2,000 -- or nearly 3,000 -- indices that are
distributed over the well-known data vendors, for example, Bloomberg or Reuters. They are our
main distribution networks, where we are delivering all our information.
For several years now, I've been responsible for developing new products and services around
information for on-exchange or off-exchange trading. This is why we've invented and developed
the Energy Facts service that is part of our discussion today.
Gardner: When you were thinking about the challenges around this opportunity, it strikes me
you had many different sources of information you had to bring together. What were the
challenges that you encountered as you started to pipeline these information sources together?
Schultz: One-and-a-half years ago, the idea was to develop new products and services, where we
could transform our know-how and this real-time connection, aggregation, and dissemination of
data to other business lines where we were not currently working. This is why we looked into the
energy trading sector, mainly focused on the power trading here in Europe.
Energy markets really got liberalized over the last years. It started with the Nordic area, Sweden
and Norway. Ten or 12 years ago, they started with liberalizing the energy trading markets, and
Germany is the next country that followed this trend. Germany is currently the most important
market for energy and power trading in the middle of Europe.
We started to analyze the information needs in this sector, and recognized that it's a
fundamentals-driven market. Traders are looking into the fundamental factors that affect the
price of the energy or the power that you trade, whether it’s oil or whatever. That’s how we
started with power trading.
You have the wind and other weather factors. You have temperature. You have the availability of
power plants. So, you try to categorize and summarize these sectors. It's called the supply and the
demand side regarding this energy trading.
Fundamental data models
By talking to well-known players in the market, we quickly recognized what they were doing on
their trading and analytic side, and that we could build up a very powerful and fundamental data
models. You have to collect all the relevant information to get an overview and to get an estimate
about the price, in this case, where power could develop and in which direction it could develop.
The main issue and main task in the beginning was to collect the relevant data. Quite quickly, we
were able to set up a big list of all relevant data sets or sources, especially for Germany and some
adjacent countries. We came up with something around 70, 80, or even 100 different sources on
the Web to grab information from. So, the main issue was how to collect and grab all this data in
a manageable way into one data base. That was the ﬁrst step.
In the second step, Kapow came into this play. We recognized that it’s really important to have a
one-stop shopping inbound channel that collects all the information from these sources, so that
to get the information.
I wanted to have a responsible product manager for this project or for this new product. From the
beginning, I had to have a good technology in place that would be able to handle all these kind of
sources from the Web.
Gardner: Let me go to Stefan now at Kapow. When you heard about Deutsche Börse and some
of these issues that they were facing and the challenges that they were trying to solve, what came
to your mind in terms of how Kapow might apply?
Andreasen: It came to mind that, if these data sources exist somewhere on the Web, we can
actually grab them where they are. What you traditionally do with information
gathering is that you call every company or every entity that has data and ask
them, "Will you please provide the data in this or this format?" But, with Kapow
Wed Data Services, you can just grab the data, wherever it is on the Web, and
assemble this valuable data source much easier and much faster.
Gardner: Let’s go back to Mario. Tell us, as you progressed through the
solution, what was the experience?
Schultz: Just to go back one step. We recognized that there are so many different data formats
that we had to grab. There are all these different providers of information in Germany and other
European countries. They have their own websites. Some give the data in HTML format. Others
use XLS, CSV, or even PDFs.
Kapow tells us how to get this information from these different sources in quite different
formats. This is a manageable way, with a process-driven or graphical user interface (GUI)
driven tool, that would use the effort, the personal, the manpower efforts to collect and grab the
At our starting point, one-and-a-half years ago, there were a lot of things underway here in
Germany or the other European counties with the Copenhagen Conference, the carbon-emission
discussion, and the liberalization. There were discussions about the big players with the
transmission nets and power plants and whether they had to split up these things. So really there
are a lot of changes. If you have source a or website source known once, you can just take it,
program the script, and then leave it. We have to always check it, and they are changing the
New companies are built, and some transmission lines are transformed. So, other companies are
building up a new website. There are a lot of things underway. You have 70, 80, or even 100
sources, I don't know. You always have to recognize change and then check whether you have to
I started to work with an internal solution that I thought could handing all that. After a few weeks
of developing and discussing, we recognized that our internal solution was not appropriate and
not capable of doing all that kind of stuff. We quickly came across Kapow and evaluated their
possibilities. We decided, nearly from the beginning or just a few weeks into starting the project,
that we had to use the Kapow tool to collect all these data from the websites.
Gardner: As I understand, you were involved with programming some robots and setting them
up, and then you were able to adjust them dynamically to whatever the needs were of the
Schultz: The main focus in the beginning was to get all these different formats, even, for
example, go into a PDF and describe the relevant data that we want to grab, not as text, but a
ﬁgure that we needed for our further processing.
the switch, and then, with a right-hand click on the mouse, get the dataset in. We were able to do
all these kind of things with the Kapow tool and these robots within Kapow to grab this kind of
Gardner: What have been some of the results? What business-development activities have you
had? What's been the value add?
Schultz: The value add was to grab all this data into one common data format, one database, so
we would be able to deliver this data to the vendors via web tool, web terminal, or even our
existing CEF data feeds. A lot of the players in the market are trying to collect this data by
themselves, or even manually, to get an overview of where the power price would develop over
the next day, hours, weeks, months, whatever.
There are some other providers in the market focusing on the real-time delivery of data. In the
general on-exchange or off-exchange business, we're talking millisecond optimization. That’s not
the timing that we have here, but it's getting from a once-a-day PDF analyst commentary via
email in the morning, to a real-time terminal, or even to go to Bloomberg or Reuters screen
where you get our Energy Facts data for on-time and real-time information set for trading.
Gardner: I'm really intrigued by your ability to manage so many different sources in real time
and, as you say, coming from all different sources, interfaces, and application formats. Can you
give us a little demonstration and show us the application in action?
Schultz: Okay. You should see on your screen our Energy Facts web terminal. This is one of our
delivery possibilities to bring this data in real-time to the end users.
In the ﬁrst phase, we're just focusing on the German market -- Belgium, France, and the
Netherlands. We decided to start with four European countries. I don’t want to go to other pages.
I just decided to take two or maybe three of them to give a view of what's going on in this
Energy Facts terminal.
Not only websites
Currently, we have 70 or 80 sources that we're grabbing. It's not only websites, but we have
some third-party providers that are delivering information, for example, weather, temperature,
and things like that. We have providers giving data via FTP service, and we even use Kapow for
grabbing data from these third-party players. As I said, it's a one-stop shopping solution to get
everything via one channel.
For example, an interesting thing in the energy trading space is availability. When a company is
looking into the future, they want to know the availability of different power plants. You can see
on the right hand side there will be a summary of nuclear power, for example, and lignite, hard
coal, and water.
There are various sources in Germany giving all this information in different formats. We grab
everything into one database, do quality checks, and then compile the information to the front-
end that you can see down there with a graphical presentation. We have a table with all the
ﬁgures and we even do some kind of analytic enrichments, so we have a deviation from what has
been published the day before.
You can see, for example, that we have some changes in the hard coal availability for the next 30
days. We're taking those sources, collecting the information, doing quality checks and quality
assurance, aggregating everything into one database, one data format, and then presenting it on
Gardner: If I'm a consumer of this, if I am trader that subscribes to your service, and I
encountered some other form of information that I wanted to bring in the mix, do I have the
option of approaching you and asking for you to bring that in, or is that out of the question?
Schultz: No. This is just our starting point.As I said, this is something where we tried to create a
complete new business in the energy sector. We started with these four countries and datasets and
we will enhance it to other countries. If you ask us to add other kinds of data, we can integrate it
quite quickly into our service. No problem.
Just two other examples. One, for example, is something that in Germany is called Urgent
Market Messages. In Germany, we have four big power plant providers or transmission-system
operators. The power plant providers push out, in real time, as fast as possible, Urgent Market
Messages, when a power plant has to go into maintenance mode or has an accident and they have
to repair something.
We grab all these different kind of sources from all those power plant providers and then
aggregate all these Urgent Market Messages in the table that you can see down there. If you go to
other pages on our screen, you can see them on the left hand side, where you always get this
Urgent Market Messages, the latest ones. If, for example, a nuclear power plant goes off the grid
unexpectedly, this could dramatically change the power price on the market. This is another
example of collecting data for Urgent Market Messages.
I don’t want to stretch this too much, but the last point is cross border. Germany is somewhat in
the middle of all this trading in Europe, so we have a lot of connection points to the other
countries. We have Denmark, Sweden, Poland, France, Belgium, and Luxembourg. So, we have
so many grid lines going over the border to the other countries.
You always have to collect the data from these different transmission lines to the other countries,
because they are auctioning the power, to transport power, for example, from France, Germany,
or the way around. You have to get all this kind of information and a better understanding of
For example, in this case, it's either Germany to France and to connection point. Down there,
you can see then how much of power has been allocated for a speciﬁc hour in a day. The red line
is the price for the transportation in this case. In addition, you could show the price difference,
for example, between Paris and Leipzig, the two exchanges for energy. Everything is collected
and then put into one view, where you show the interesting ﬁgures on the one screen.
Gardner: Sufﬁce it to say that there is an awful lot going on behind this little red line. It's not
that easy to put this together. This is reﬂecting an awful lot of information and processing.
Schultz: This slide is from one of our pages in used for one provider for Germany to France.
Now, I'll go to this button and show you the other ones, like the ones to Germany, then the
These are the four countries we're currently covering, and you can see all the connection points
for this. Later on, we'll go on with Denmark and the other one. This is really the power of having
all this kind of data in one tool, where the aggregation, quality check, and everything comes into
Gardner: Mario, I have to imagine that there are external forces that can come to bear on this,
perhaps a massive snowstorm or some other disruption in the price of a major commodity, and
that’s something that you can bring into this picture almost immediately, right?
Schultz: Yes. For example, in what I just showed, if we go to this weather page, you see
temperature. This is very interesting. Generally, in Germany we have something, as you see on
the yellow curve, between 1 and 3 degrees Celsius as the general temperature in winter. You see
the other forecast for temperature is something around -5. Some time ago, it was even -7 degrees.
It's really big. This is normally an indication for higher power prices, because people will
demand more power for heating their buildings or ofﬁces. So, this has really changed. This
weather aspect has changed every six hours within our service.
Gardner: If these traders also wanted to try to ﬁnd out why they were seeing certain effects in
these analytic graphs, is there a way for them to then quickly go out and look at the news feeds
or other information, so that they could determine what’s behind the curves?
Schultz: Currently, it's not part of our service, and we didn’t do that because there are other
providers for this information. Generally, you have the on-exchange and off-exchange prices that
are normally available from the existing data vendors. For example, they use Bloomberg or other
service providers. Energy Facts focuses on the fundamental data collected in real-time, and
aggregated into one service, the place where we saw it as the missing piece in Europe. If you
want to go to the news site, traders have other providers for the news on their desks.
Gardner: I see. So, this is really focused on numeric, algorithmic, programmable types of
information and data.
Schultz: This is what we call the fundamental data sets, what is fundamentally behind driving
the power price -- the demand and supply side factors behind the price. The analysts or traders
can get this information in real-time in one service to do better estimation of the pricing
Gardner: That’s really impressive, I appreciate your walking us through it. I wonder if we can
go back now to Stefan and talk a little bit about what Kapow and its values and services brought
to the table to help support this really impressive application and service.
Andreasen: Sure, Dana. This is an extremely impressive service that Mario just showed us
here, and I'm sure, if you're dealing with buying and selling energy, this is a must for you to be
sure you made the right decision.
If we go back to what I talked about earlier, businesses are relying more and more on data to
make the right decision, and their focus is on quality, completeness, and agility. Let's be more
practical here and ask how you actually get this data.
There is a term, data integration, which is about accessing the data and providing it in standard
API, so that you can actually leverage the measure of business application.
Energy Facts is accessing this data at the 70-80 different data sources, as Mario said, and
providing it as a feed that depends on the volatility of the different data sources. Some of the data
delivers every minute, and some deliver every four hours, etc., based on how quickly the data
source changes. WDS is all about getting access to this data where it resides.
There are really two different kinds of data sources. One set of data sources is more like a real-
time source data source. Let's say you go to a patent directory, and there are probably millions of
patents. In that case you would use Kapow Data Server to wrap that data source into a service
layer, and then you would be able to do real-time, as soon as you get real-time results back. So,
that's real-time access, where you have vast amount of information.
The other scenario, and I think that's more what we see in the Energy Facts example here, is
where you have a more limited data source, and you are actually trying to do a consolidation of
the data into a database, and then you use that database to serve different customers or different
With Kapow, you can actually go in and access the data, if you can see them on your browser.
That's one thing. The other thing you need to do to make this data available to your business
application is to transform and enrich the data, so that it actually matches the format that you
For example, on the website, it might have the date saying, "2 hours ago" or "3 minutes ago" and
so on. That's really not useful. What you really want is a timestamp with the hour, the second, the
minute, the months, the day, the year, so you can actually start comparing these. So, data
cleansing is an extremely important part of data extraction and access.
The last thing, of course, is serving the data in the format you need. That can be a database, if
you're doing consolidation, or it can be as an API, if you are doing more of a federated access to
data, and leaving the data where it is.
Actually, all styles exist, but there is a tendency for many companies to actually access the data
where it is, rather than trying to consolidate it to a new place.
Schultz: Dana, I have a very good example for this one. I talked about Urgent Market
Messages, where the power plant providers are sending out, as quickly as possible after an
incident occurs, an Urgent Market Message regarding changes in the power plant availability.
This is something that is a good aggregate, using Kapow, because we can schedule all these
robots in a very good way.
Currently we're checking these Urgent Market Messages sources every minute. On all
aggregated levels, we always can state whether this message is valid or invalid. I didn't focus on
this is my presentation.
If we ﬁnd the message on the website, we put it on our service. Maybe in the next minute the
message disappears on the website. We have it still in our service, but then we ﬂag this message
as invalid. The user knows that this message had been on the source website, but now it
disappeared. We still have the information, but we can separate between these two statuses, valid
or invalid Urgent Market Message.
This is accomplished by accessing the source, enriching it into the database, doing some
scheduling, and then giving feedback and checking the website again. By doing these three steps,
we're able to offer this part of our Urgent Market Message presentation layer.
Gardner: Mario, I think you're really a pioneer in this. What intrigues me is how far this can go
in addition to what you have done with it, and how this could affect the number of other
industries and vertical businesses as well.
From your perspective, Stefan, how are other types of business, enterprises, and service
providers likely to start using this and providing WDS-based, value added services as well?
Andreasen: That’s a very good question. Kapow Technologies today has more than 400
customers. For them, our technology becomes a business critical part of what they do. Let me try
to explain that. Most information providers sell data to all the businesses. In the U.S., for
example, there is a big business around background checking, both of people and companies. It's
a fact in the U.S. that if you go into a bank to get a credit card, they're going to run a background
check on you, before you can get this credit card.
One of the things they check are a lot of resources on the Web, for example, criminal records. On
the Web, every courthouse has a website, where you can log in and search for criminal records
for a certain person.
Most of these companies that are doing the background checking are Kapow customers, using
Kapow's Web Data Services to service enable all these courthouses. When they go in and want a
credit card in the background check, Kapow automatically goes out and gets that information
from these courthouse websites and a lot of other data sources in real-time. Otherwise, they
would have to have 50 or 60 people manually typing in, and they wouldn’t get the results until
two days later.
Gardner: I suppose another effect also over the past 10 or 15 years, from my timeline earlier in
the presentation, is that these web standards have kicked in, not only for looking up information
across the Web, but it has also become a standardized way of accessing information internally.
What about the use of this for corporate performance management and other aspects of the web
data that’s inside of companies?
Andreasen: I encourage everybody to go to our website and download a white paper from one
of our customers, called Fiserv. It's a large ﬁnancial services company in the U.S. Fiserv has a lot
of business partners, actually they have more than 300 banks in more than 10 countries as
business partners. Because they're selling services, it's incredibly important for them to also
monitor their customers to understand what's happening.
They had lot of people who logged into these 300 partner banks every day and grabbed some
ﬁnancial information, such as interest rates, etc., into an Excel spreadsheet, put it into a database,
and then got it up on a dashboard.
The thing about this is that, ﬁrst, you have a lot of human labor, which can cause human errors,
and so on. You can only do it once a day, and it's a tedious process. So what they did is got
Kapow in and automated the extraction of this data from all their business partners -- 300 banks
in more than 10 countries.
They can now get that data in near real-time, so they don’t have to wait for data. They don’t have
to go without on the weekend, because people are not working. They get that very business
critical insights to the market and their partners instantly through our product.
I can give you another example. A large car manufacturer is spending almost a billion dollars a
year in advertising on television. Of course, there are several parameters that are important for
them to understand about how should they spend the advertising money the best possible way.
These data sources are, for example, the lead reporting, understanding what are the leads they're
getting in and understanding the market data they're are getting from business information
providers about trends in the markets and so on. What is the reporting they get from ad
campaigns? How can they see how many people clicked on this or watched these television
shows? Also, how many cars are getting registered, their models versus their competitors?
By using Kapow, they could hook up to all of these data sources in real time and suddenly get
complete insights to the effectiveness of how they spend their advertising dollar, and having
very, very good return on the investment.
So, it's just another example again about how WDS can help the market analyst, the product
manager, and a lot of people who have to make very vital business decisions in the companies
Gardner: Great. I appreciate your input Stefan. Today’s discussion on how the Deutsche Börse
Group in Frankfurt, Germany is using Kapow Technologies for a real-time web data analysis
service comes as a culmination of a four-part series on WDS.
We have seen how an innovative information service that’s been created rapidly elegantly
demonstrates how real-time content and data, assembled from various online sources, provides a
valuable service and an analysis capability as a business.
What's happening with WDS is that it's gone beyond an internal enterprise focus. It's become a
business onto itself. So, there are lots of value opportunities. We can sell new value across
business solutions. We can look for ways that strategies internally are enhanced, and we can
create ecosystems of partnership.
I think what we are going to see, when cloud computing starts to really take off, rather than be
discussed so much, is the opportunity for companies that are in partnership to really encourage
competitive advantage by sharing data and analytics effectively. It also drives more business
strategy and execution and creates new and additional revenue streams as a result.
So, I want to thank Mario at Deutsche Börse for his participation here. I think they're a real
poster child for how real-time analytics can be brought together. So, thanks to you, Mario, for
Schultz: It was a pleasure, Dana. Thank you.
Gardner: And, certainly, I also want to give the opportunity for viewers and listeners to learn
more about some of the topics we have discussed from Kapow. There are a lot of different
resources available there in order to take some next steps or continue to educate yourselves on
some of these issues.
This is Dana Gardner, principal analyst at Interarbor Solutions, your host and moderator. I also
want to thank Stefan Andreasen. He is the CTO of Kapow.
Andreasen: Thank you very much, Dana.
Gardner: You've been enjoying a BrieﬁngsDirect presentation. Thanks again for joining us, and
come back next time.
Listen to the podcast. Download the podcast. Find it on iTunes/iPod and Podcast.com. Download
the transcript. Learn more. Sponsor: Kapow Technologies.
Transcript of a sponsored BrieﬁngsDirect podcast on information management for business
intelligence, one of a series on web data services with Kapow Technologies. Copyright
Interarbor Solutions, LLC, 2005-2010. All rights reserved.
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