This is the second Open
Group conference that focuses on the topic of Big Data. This is an architecture
style that is getting a great deal of attention lately. With the emergence of
social and the explosion of data coming from devices there is a surge of opportunities
for companies to monetize on the data that is generated in the public domain. A
great example of a tech company doing this today is Google. Google generates
that vast majority of its revenue on marketing data, not its technology. Other
companies want a piece of this pie.
Above is an IBM
created an infographic I think sums up the opportunities for companies.
So with this as such
a value driver for our companies it’s important for us to understand what this
new technology enables but also be cautious not to abuse this new architecture
The first two
keynotes of Day 1 cover the business opportunities for Big Data and the ways to
make it interoperable in the enterprise.
Big Data at NASA
Chris Gerty presented the views and uses of Big Data at NASA.
What great way to kick off the conference. At least for me, it’s always good starting
off a conference with space ships, distant galaxies and the mars rover.
Outside the pure science geek factor, this was a great
presentation. Chris showed the direct result of architecture or more
specifically, information architecture on providing truly compelling results. I
liked that Chris didn’t call out EA specifically but rather talked about the
value that this new architecture style enables.
NASA is on the cutting edge of technology. What a refreshing
view of the government. They are doing
everything from open sourced solutions to democratizing information to create
some really interesting crowd sourced applications. They even stood up a
private cloud inside NASA before the public cloud really emerged as an option.
They then evolved to a public cloud infrastructure for their big data
The area that Chris talked about that I think has a lot of potential
is the notion of context aware solutions. This is a Gartner term that has been
used for a couple years to describe getting data from devices. NASA is looking
at this to get a better understanding of their Big Data. The assertion here is
that Big Data is often time “context-less” and when you bring in other inputs
from other methods you get truly meaningful information. I believe this
assertion hits the nail on the head.
There was three core takeaways provided at the session.
Below I have provided a bit of commentary on those takeaways to provide
additional insights from a pure EA perspective.
Information - "we believe that oneness, collaboration and
collective insights are the pathways forward to solving humanities
toughest challenges". I thought that this was a very thought
provoking statement not only applied to NASA but as a lesson for EA in
general. I think of the quote from Aristotle quote, "The whole is
greater than the sum of it's parts"
for Opportunities for Big Data - be creative and experiment. In
his talk we learn about all the insist you get in an unexpected way. I
think there should most certainly be an innovation piece to EA some EA
organizations have it but there are still a lot that don't. In creation of
an architecture strategy for your company it is important that EA can get
in front of the challenges facing the business along with exploration of
new business opportunities.
Everyone - While a specific NASA ask it does apply to general Big
Data architectures. This is a lot like the first take away.
Bringing Order to Chaos
David Potter and Ron Schuldt talked about the work that The
Open Group is doing to evolve their standard Quantum Lifecycle Management (QLM)
and the complementary Universal Data Element Framework (UDEF) standard.
This session was complementary to the Big Data architecture
style by leveraging these standards to provide a consistent method for tagging
and exchanging information about anything.
Quantum Lifecycle Management or QLM is a body of
work that was started in 2010 based on the EU-funded PROMISE project in 2005m
is an information life cycle management standard. It is a model that can
describe how to optimally collect and manage big data oriented solutions where
data is feed from multiple different sources.
In Open Group terms QLM has the following characteristics:
- Quantum Lifecycle Management is the next leap beyond
Product Lifecycle Management (PLM)
- Closing instance-level information loops across
all phases of all kinds of lifecycles
- Developing an open, trustworthy and secure information
exchange for whole-of-life lifecycle management.
- Enabling Boundaryless Information Flow™ to reach
trillions of autonomous objects in the “Internet of Things”, making it a
You can find more information on QLM here:
Element Framework UDEF
The UDEF is
based on the concepts of International Standard 11179, and is integrated with
the World-Wide Web Consortium’s Resource Description Framework (RDF). But it is
less complicated than these standards. It is designed for use by the people
that understand an enterprise’s business operations, rather than specialists in
Using a simple process, you can assign an index to any piece
of data, based on the core UDEF vocabulary and imported vocabularies. This
index will be the same as that assigned by other UDEF practitioners in your
enterprise and in other enterprises. This makes it easy to relate new
information to information that you already have stored, which can significantly
reduce the cost of configuring and programming interface software.
You can find more information on UDEF here:
GE was used as a key case study for the Big Data movement.
Below is an overview from Forbes.com on what was described in the session:
When it comes to big data, GE is playing catch-up to IBM.
GE is counting on its expertise making industrial equipment—from gas-fired
electrical turbines to locomotives—to give it an advantage over rivals focused
on exclusively providing data solutions, says Ruh. “If you don’t have deep
expertise in how energy is distributed or generated, if you don’t understand
how a power plant runs, you’re not really going to be able to build an
analytical model and do much with it,” he says. “We have deep insight into
several very specific areas. And that’s where we’re staying focused.”