Transcript of The Open Group podcast, in conjunction with BriefingsDirect, exploring the challenges and ramifications of the Internet of Things, as machines and sensors collect vast amounts of data.

1. The Open Group Panel: Internet of Things – Opportunities
and Obstacles
Transcript of The Open Group podcast, in conjunction with BriefingsDirect, exploring the
challenges and ramifications of the Internet of Things, as machines and sensors collect vast
amounts of data.
Listen to the podcast. Find it on iTunes. Sponsor: The Open Group
Dana Gardner: Hello, and welcome to a special BriefingsDirect thought leadership interview
series coming to you in conjunction with The Open Group Boston 2014 on July
21.
I'm Dana Gardner, principal analyst at Interarbor Solutions, and I'll be your host and moderator
throughout these discussions on Open Platform 3.0 and Boundaryless
Information Flow.
We're going to specifically delve into the Internet of Things with a panel of
experts. The conference has so far examined how Open Platform 3.0 leverages
the combined impacts of cloud, big data, mobile, and social. But to each of these
now we can add a new cresting wave of complexity and scale as we consider the
rapid explosion of new devices, sensors, and myriad endpoints that will be
Gardner
connected using internet protocols or standards and architectural frameworks.
This means more data, more cloud connectivity and management, and an additional tier of
“things” that are now going to be part of the mobile edge -- and extending that mobile edge ever
deeper into even our own bodies.
When we think about inputs to these social networks -- that's going to
increase as well. Not only will people be tweeting, your device could be
very well tweet, too -- using social networks to communicate. Perhaps
your toaster will soon be sending you a tweet about your English muffins being ready.
The Internet of Things is more than the “things” – it means a higher order of software platforms.
For example, if we are going to operate data centers with new dexterity thanks to software-defined
networking (SDN) and storage (SDS) -- indeed the entire data center being software-defined
(SDDC) -- then why not a software-defined automobile, or factory floor, or hospital
operating room -- or even a software-defined city block or neighborhood?
And so how does this actually work? Does it all spin out of control? Does it remain under proper
management and governance? Do we have unknown unknowns about what to expect with this
level of complexity, scale, and volume of input devices?

2. Will architectures arise that support the numbers involved, interoperability, and provide
governance for the Internet of Things -- rather than just letting each type of device do its own
thing?
To help answer some of these questions, The Open Group assembled a distinguished panel to
explore the practical implications and limits of the Internet of Things. So please join me in
welcoming Said Tabet, Chief Technology Officer for Governance, Risk and Compliance Strategy
at EMC, and a primary representative to the Industrial Internet Consortium. Welcome, Said.
Said Tabet: Thank you.
Cities as platforms
Gardner: Penelope Gordon is Emerging Technology Strategist at 1Plug Corporation. Thank
you for being here, Penelope. Jean-Francois Barsoum is Senior Managing Consultant for Smarter
Cities, Water and Transportation at IBM. Thank you for being with us. And, of course, Dave
Lounsbury is Chief Technical Officer at The Open Group.
Jean-Francois, we heard from our speakers earlier about this notion of cities as platforms, and I
think the public sector might offer us some opportunity to look at what is going to happen with
the Internet of Things, and then extrapolate from that to understand what might happen in the
private sector.
Hypothetically, the public sector has a lot to gain. It doesn't have to go through the same confines
of a commercial market development, profit motive, and that sort of thing. Tell us a little bit
about what the opportunity is in the public sector smart cities?
Jean-Francois Barsoum: It's immense. The first thing I want to do is link to something that
Marshall Van Alstyne (Professor at Boston University and Researcher at MIT) had
talked about this morning, because I was thinking about his way of approaching
platforms and thinking about how cities represent an example of that.
You don't have customers; you have citizens. Cities are starting to see themselves
as platforms, as ways to communicate with their customers, their citizens, to get
information from them and to communicate back to them. But the complexity
with cities is that as a good a platform as they could be, they're relatively rigid.
Barsoum
They're legislated into existence and what they're responsible for is written into law. It's not
really a market.
Chris Harding (Forum Director of The Open Group Open Platform 3.0) earlier mentioned, for
example, water and traffic management. Cities could benefit greatly by managing traffic a lot
better.

3. Part of the issue is that you might have a state or provincial government that looks after
highways. You might have the central part of the city that looks after arterial networks. You
might have a borough that would look after residential streets, and these different platforms end
up not talking to each other.
They gather their own data. They put in their own widgets to collect information that concerns
them, but do not necessarily share with their neighbor. One of the conditions that Marshall said
would favor the emergence of a platform had to do with how much overlap there would be in
your constituents and your customers. In this case, there's perfect overlap. It's the same citizen,
but they have to carry an Android and an iPhone, despite the fact it is not the best way of dealing
with the situation.
The complexities are proportional to the amount of benefit you could get if you could solve
them.
Gardner: So more interoperability issues?
Barsoum: Yes.
More hurdles
Gardner: More hurdles, and when you say commensurate, you're saying that the opportunity
is huge, but the hurdles are huge and we're not quite sure how this is going to unfold.
Barsoum: That's right.
Gardner: I like to remind our audience, we're going to be taking questions at the end of our
panel discussion. So please go through the same process of jotting them down and holding them
at your table. We'll have someone collect them and will address those questions at the end.
Let us go to an area where the opportunity outstrips the challenge, manufacturing. Said, what is
the opportunity for the software-defined factory floor for recognizing huge efficiencies and
applying algorithmic benefits to how management occurs across domains of supply-chain,
distribution, and logistics. It seems to me that this is a no-brainer. It's such an opportunity that the
solution must be found.
Tabet: That's good Dana. Thank you. When it comes to manufacturing, the opportunities are
probably much bigger. It's where we can see a lot of progress that has already been done and still
work is going on. There are two ways to look at it.
One is the internal side of it, where you have improvements of business processes. For example,
similar to what Jean-Francois said, in a lot of the larger companies that have factories all around
the world, you'll see such improvements on a factory base level. You still have those silos at that
level.

4. Now with this new technology, with this connectedness, those improvements are going to be
made across factories, and there's a learning aspect to it in terms of trying to
manage that data. In fact, they do a better job. We still have to deal with
interoperability, of course, and additional issues that could be jurisdictional, etc.
However, there is that learning that allows them to improve their processes across
factories. Maintenance is one of them, as well as creating new products, and
connecting better with their customers. We can see a lot of examples in the
marketplace. I won't mention names, but there are lots of them out there with the
large manufacturers.
Tabet
Gardner: We've had just-in-time manufacturing and lean processes for quite some time, trying
to compress the supply chain and distribution networks, but these haven't necessarily been done
through public networks, the internet, or standardized approaches.
But if we're to benefit from what we heard about this morning from Marshall Van Alstyne, we're
going to need to be able to be platform companies, not just product companies. How do you go
from being a proprietary set of manufacturing protocols and approaches to this wider,
standardized interoperability architecture?
Tabet: That's a very good question, because now we're talking about that connection to the
customer. With the airline and the jet engine manufacturer, for example, when the plane lands
and there has been some monitoring of the activity during the whole flight, at that moment,
they'll get that data made available. There could be improvements and maybe solutions available
as soon as the plane lands.
Interoperability
That requires interoperability. It requires Platform 3.0 for example. If you don't have open
platforms, then you'll deal with the same hurdles in terms of proprietary technologies and
integration in a silo-based manner.
Gardner: Penelope, you've been writing about the obstacles to decision-making that might
become apparent as big data becomes more prolific and people try to capture all the data about
all the processes and analyze it. That's a little bit of a departure from the way we've made
decisions in organizations, public and private, in the past.
Of course, one of the bigger tenets of Internet of Things is all this great data that will be available
to us from so many different points. Is there a conundrum of some sort? Is there an unknown
obstacle for how we, as organizations and individuals, can deal with that data? Is this going to be
chaos, or is this going to be all the promises many organizations have led us to believe around
big data in the Internet of Things?

5. Penelope Gordon: It's something that has just been accelerated. This is not a new problem in
terms of the decision-making styles not matching the inputs that are being
provided into the decision-making process.
Former US President Bill Clinton was known for delaying making decisions.
He's a head-type decision-maker and so he would always want more data and
more data. That just gets into a never-ending loop, because as people collect data
for him, there is always more data that you can collect, particularly on the
quantitative side. Whereas, if it is distilled down and presented very succinctly
and then balanced with the qualitative, that allows intuition to come to fore, and
you can make optimal decisions in that fashion.
Gordon
Conversely, if you have someone who is a heart-type or gut-type decision-maker and you present
them with a lot of data, their first response is to ignore the data. It's just too much for them to
take in. Then you end up completely going with whatever you feel is correct or whatever you
have that instinct that it's the correct decision. If you're talking about strategic decisions, where
you're making a decision that's going to influence your direction five years down the road, that
could be a very wrong decision to make, a very expensive decision, and as you said, it could be
chaos.
It just brings to mind to me Dr. Seuss’s The Cat in the Hat with Thing One and Thing Two. So,
as we talk about the Internet of Things, we need to keep in mind that we need to have some sort
of structure that we are tying this back to and understanding what are we trying to do with these
things.
Gardner: So, openness is important, and governance is essential. Then, we can start moving
towards higher-order business platform benefits. But, so far, our panel has been a little bit
cynical. We've heard that the opportunity and the challenges are commensurate in the public
sector and that in manufacturing we're moving into a whole new area of interoperability, when
we think about reaching out to customers and having a boundary that is managed between
internal processes and external communications.
And we've heard that an overload of data could become a very serious problem and that we
might not get benefits from big data through the Internet of Things, but perhaps even stumble
and have less quality of decisions.
So Dave Lounsbury of The Open Group, will the same level of standardization work? Do we
need a new type of standards approach, a different type of framework, or is this a natural path
and course what we have done in the past?
Different level
Dave Lounsbury: We need to look at the problem at a different level than we institutionally
think about an interoperability problem. Internet of Things is riding two very powerful waves,

6. one of which is Moore's Law, that these sensors, actuators, and network get smaller and smaller.
Now we can put Ethernet in a light switch right, a tag, or something like that.
Also, Metcalfe's Law that says that the value of all this connectivity goes up with
the square of the number of connected points, and that applies to both the
connection of the things but more importantly the connection of the data.
The trouble is, as we have said, that there's so much data here. The question is
how do you manage it and how do you keep control over it so that you actually
get business value from it. That's going to require us to have this new concept of
a platform to not only to aggregate, but to just connect the data, aggregate it,
Lounsbury
correlate it as you said, and present it in ways that people can make decisions however they
want.
Also, because of the raw volume, we have to start thinking about machine agency. We have to
think about the system actually making the routine decisions or giving advice to the humans who
are actually doing it. Those are important parts of the solution beyond just a simple "How do we
connect all the stuff together?"
Gardner: So we might need a higher order of intelligence, now that we have reached this border
of what we can do with our conventional approaches to data, information, and process?
Thinking about where this works best first in order to then understand where it might end up
later, I was intrigued again this morning by Professor Van Alstyne. He mentioned that in
healthcare, we should expect major battles, that there is a turf element to this, that the
organization, entity or even commercial corporation that controls and manages certain types of
information and access to that information might have some very serious platform benefits.
The openness element now is something to look at, and I'll come back to the public sector. Is
there a degree of openness that we could legislate or regulate to require enough control to
prevent the next generation of lock-in, which might not be to a platform to access to data
information and endpoints? Where is it in the public sector that we might look to a leadership
position to establish needed openness and not just interoperability.
Barsoum: I'm not even sure where to start answering that question. To take healthcare as an
example, I certainly didn't write the bible on healthcare IT systems and if someone did write that,
I think they really need to publish it quickly.
We have a single-payer system in Canada, and you would think that would be relatively easy to
manage. There is one entity that manages paying the doctors, and everybody gets covered the
same way. Therefore, the data should be easily shared among all the players and it should be easy
for you to go from your doctor, to your oncologist, to whomever, and maybe to your pharmacy,
so that everybody has access to this same information.

7. We don't have that and we're nowhere near having that. If I look to other areas in the public
sector, areas where we're beginning to solve the problem are ones where we face a crisis, and so
we need to address that crisis rapidly.
Possibility of improvement
In the transportation infrastructure, we're getting to that point where the infrastructure we have
just doesn't meet the needs. There's a constraint in terms of money, and we can't put much more
money into the structure. Then, there are new technologies that are coming in. Chris had talked
about driverless cars earlier. They're essentially throwing a wrench into the works or may be
offering the possibility of improvement.
On any given piece of infrastructure, you could fit twice as many driverless cars as cars with
human drivers in them. Given that set of circumstances, the governments are going to find they
have no choice but to share data in order to be able to manage those. Are there cases where we
could go ahead of a crisis in order to manage it? I certainly hope so.
Gardner: How about allowing some of the natural forces of marketplaces, behavior, groups,
maybe even chaos theory, where if sufficient openness is maintained there will be some kind of a
pattern that will emerge? We need to let this go through its paces, but if we have artificial
barriers, that might be thwarted or power could go to places that we would regret later.
Barsoum: I agree. People often focus on structure. So the governance doesn't work. We should
find some way to change the governance of transportation. London has done a very good job of
that. They've created something called Transport for London that manages everything related to
transportation. It doesn't matter if it's taxis, bicycles, pedestrians, boats, cargo trains, or whatever,
they manage it.
You could do that, but it requires a lot of political effort. The other way to go about doing it is
saying, "I'm not going to mess with the structures. I'm just going to require you to open and share
all your data." So, you're creating a new environment where the governance, the structures, don't
really matter so much anymore. Everybody shares the same data.
Gardner: Said, to the private sector example of manufacturing, you still want to have a global
fabric of manufacturing capabilities. This is requiring many partners to work in concert, but with
a vast new amount of data and new potential for efficiency.
How do you expect that openness will emerge in the manufacturing sector? How will
interoperability play when you don't have to wait for legislation, but you do need to have
cooperation and openness nonetheless?
Tabet: That's a good question. It comes back to the question you asked Dave about standards. I'll
just give you some examples. For example, in the automotive industry, there have been some
activities in Europe around specific standards for communication.

8. The Europeans came to the US and started to have discussions, and the Japanese have interest, as
well as the Chinese. That shows, because there is a common interest in creating these new
models from a business standpoint, that these challenges they have to be dealt with together.
Managing complexity
When we talk about the amounts of data, what we call now big data, and what we are going to
see in about five years or so, you can't even imagine. How do we manage that complexity, which
is multidimensional? We talked about this sort of platform and then further, that capability and
the data that will be there. From that point of view, openness is the only way to go.
There's no way that we can stay away from it and still be able to work in silos in that new
environment. There are lots of things that we take for granted today. I invite some of you to go
back and read articles from 10 years ago that try to predict the future in technology in the 21st
century. Look at your smart phones. Adoption is there, because the business models are there,
and we can see that progress moving forward.
Collaboration is a must, because it is a multidimensional level. It's not just manufacturing like jet
engines, car manufacturers, or agriculture, where you have very specific areas. They really they
have to work with their customers and the customers of their customers.
Gardner: Any reaction to that? Dave, I have a question for both you and Penelope. I've seen
some instances where there has been a cooperative endeavor for accessing data, but then making
it available as a service, whether it's an API, a data set, access to a data library, or even analytics
applications set. The Ocean Observatories Initiative is one example, where it has created a sensor
network across the oceans and have created data that then they make available.
Do you think we expect to see an intermediary organization level that gets between the sensors
and the consumers or even controllers of the processes? Is there's a model inherent in that that we
might look to -- something like that cooperative data structure that in some ways creates
structure and governance, but also allows for freedom? It's sort of an entity that we don't have yet
in many organizations or many ecosystems and that needs to evolve.
Lounsbury: Dana, I'm going assert that we're already seeing that in the marketplace. If you look
at the commercial and social Internet of Things area, we're starting to see intermediaries or
brokers cropping up that will connect the silo of my android ecosystem to the ecosystem of
package tracking or something like that. There are dozens and dozens of these cropping up.
In fact, you now see APIs even into a silo of what you might consider a proprietary system and
what people are doing is to to build a layer on top of those APIs that intermediate the data.
This is happening on a point-to-point basis now, but you can easily see the path forward. That's
going to expand to large amounts of data that people will share through a third party. I can see

9. this being a whole new emerging market much as what Google did for search. You could see that
happening for the Internet of Things.
Gardner: Penelope, do you have any thoughts about how that would work? Is there a mutually
assured benefit that would allow people to want to participate and cooperate with that third
entity? Should they have governance and rules about good practices, best practices for that
intermediary organization? Any thoughts about how data can be managed in this sort of
hierarchical model?
Nothing new
Gordon: First, I'll contradict it a little bit. To me, a lot of this is nothing new, particularly
coming from a marketing strategy perspective, with business intelligence (BI). Having various
types of intermediaries, who are not only collecting the data, but then doing what we call data
hygiene, synthesis, and even correlation of the data has been around for a long time.
It was an interesting, when I looked at recent listing of the big-data companies, that some notable
companies were excluded from that list -- companies like Nielsen. Nielsen's been collecting data
for a long time. Harte-Hanks is another one that collects a tremendous amount of information
and sells that to companies.
That leads into the another part of it that I think there's going to be. We're seeing an increasing
amount of opportunity that involves taking public sources of data and then providing synthesis
on it. What remains to be seen is how much of the output of that is going to be provided for
“free”, as opposed to “fee”. We're going to see a lot more companies figuring out creative ways
of extracting more value out of data and then charging directly for that, rather than using that as
an indirect way of generating traffic.
Gardner: We've seen examples of how this has been in place. Does it scale and does the
governance or lack of governance that might be in the market now sustain us through the
transition into Platform 3.0 and the Internet of Things.
Gordon: That aspect is the lead-on part of “you get what you pay for”. If you're using a free
source of data, you don't have any guarantee that it is from authoritative sources of data. Often,
what we're getting now is something somebody put it in a blog post, and then that will get
referenced elsewhere, but there was nothing to go back to. It's the shaky supply chain for data.
You need to think about the data supply and that is where the governance comes in. Having
standards is going to increasingly become important, unless we really address a lot of the data
illiteracy that we have. A lot of people do not understand how to analyze data.
One aspect of that is a lot of people expect that we have to do full population surveys, as opposed
representative sampling to get much more accurate and much more cost-effective collection of
data. That's just one example, and we do need a lot more in governance and standards.

10. Gardner: We are going to be moving to the questions from the audience shortly, but I have one
last series of questions for the panel. What would you like to see changed most in order for the
benefits and rewards of the Internet of Things to develop and overcome the drawbacks, the risks,
the downside? What, in your opinion, would you like to see happen to make this a positive, rapid
outcome. Let's start with you Jean-Francois?
Barsoum: There are things that I have seen cities start to do now. There are couple of examples:
Philadelphia is one and Barcelona does this too. Rather than do the typical request for proposal
(RFP), where they say, "This is the kind of solution we're looking for, and here are our
parameters. Can l you tell us how much it is going to cost to build," they come to you with the
problem and they say, "Here is the problem I want to fix. Here are my priorities, and you're at
liberty to decide how best to fix the problem, but tell us how much that would cost."
If you do that and you combine it with access to the public data that is available -- if public
sector opens up its data -- you end up with a very powerful combination that liberates a lot of
creativity. You can create a lot of new business models. We need to see much more of that. That's
where I would start.
Gardner: Said.
More education
Tabet: I agree with Jean-Francois on that. What I'd like to add is that I think we need to push
the relation a little further. We need more education, to your point earlier, around the data and the
capabilities.
We need these platforms that we can leverage a little bit further with the analytics, with machine
learning, and with all of these capabilities that are out there. We have to also remember, when we
talk about the Internet of Things, it is things talking to each other.
So it is not human-machine communication. Machine-to-machine automation will be further than
that, and we need more innovation and more work in this area, particularly more activity from
the governments. We've seen that, but it is a little bit frail from that point of view right now.
Gardner: Dave Lounsbury, thoughts about what need to happen in order to keep this on the
tracks?
Lounsbury: We've touched on lot of them already. Thank you for mentioning the machine-to-machine
part, because there are plenty of projections that show that it's going to be the dominant
form of Internet communication, probably within the next four years.
So we need to start thinking of that and moving beyond our traditional models of humans talking
through interfaces to set of services. We need to identify the building blocks of capability that

11. you need to manage, not only the information flow and the skilled person that is going to produce
it, but also how you manage the machine-to-machine interactions.
Gardner: Penelope?
Gordon: I'd like to see not so much focus on data management, but focus on what is the data
managing and helping us to do. Focusing on the machine-to-machine and the devices is great,
but it should be not on the devices or on the machines… it should be on what can they
accomplish by communicating; what can you accomplish with the devices and then have a
reverse engineer from that.
Gardner: Let's go to some questions from the audience. The first one asks about a high order of
intelligence which we mentioned earlier. It could be artificial intelligence, perhaps, but they ask
whether that's really the issue. Is the nature of the data substantially different, or we are just
creating more of the same, so that it is a storage, plumbing, and processing problem? What, if
anything, are we lacking in our current analytics capabilities that are holding us back from
exploiting the Internet of Things?
Gordon: I've definitely seen that. That has a lot to do with not setting your decision objectives
and your decision criteria ahead of time so that you end up collecting a whole bunch of data, and
the important data gets lost in the mix. There is a term "data smog."
Most important
The solution is to figure out, before you go collecting data, what data is most important to you.
If you can't collect certain kinds of data that are important to you directly, then think about how
to indirectly collect that data and how to get proxies. But don't try to go and collect all the data
for that. Narrow in on what is going to be most important and most representative of what you're
trying to accomplish.
Gardner: Does anyone want to add to this idea of understanding what current analytics
capabilities are lacking, if we have to adopt and absorb the Internet of Things?
Barsoum: There is one element around projection into the future. We've been very good at
analyzing historical information to understand what's been happening in the past. We need to
become better at projecting into the future, and obviously we've been doing that for some time
already.
But so many variables are changing. Just to take the driverless car as an example. We've been
collecting data from loop detectors, radar detectors, and even Bluetooth antennas to understand
how traffic moves in the city. But we need to think harder about what that means and how we
understand the city of tomorrow is going to work. That requires more thinking about the data, a
little bit like what Penelope mentioned, how we interpret that, and how we push that out into the
future.

12. Lounsbury: I have to agree with both. It's not about statistics. We can use historical data. It
helps with lot of things, but one of the major issues we still deal with today is the question of
semantics, the meaning of the data. This goes back to your point, Penelope, around the relevance
and the context of that information – how you get what you need when you need it, so you can
make the right decisions.
Gardner: Our last question from the audience goes back to Jean-Francois’s comments about the
Canadian healthcare system. I imagine it applies to almost any healthcare system around the
world. But it asks why interoperability is so difficult to achieve, when we have the power of the
purse, that is the market. We also supposedly have the power of the legislation and regulation.
You would think between one or the other or both that interoperability, because the stakes are so
high, would happen. What's holding it up?
Barsoum: There are a couple of reasons. One, in the particular case of healthcare, is privacy, but
that is one that you could see going elsewhere. As soon as you talk about interoperability in the
health sector, people start wondering where is their data going to go and how accessible is it
going to be and to whom.
You need to put a certain number of controls over top of that. What is happening in parallel is
that you have people who own some data, who believe they have some power from owning that
data, and that they will lose that power if they share it. That can come from doctors, hospitals,
anywhere.
So there's a certain amount of change management you have to get beyond. Everybody has to
focus on the welfare of the patient. They have to understand that there has to be a priority, but
you also have to understand the welfare of the different stakeholders in the system and make sure
that you do not forget about them, because if you forget about them they will find some way to
slow you down.
Gardner: Dave Lounsbury, your thoughts.
Use of an ecosystem
Lounsbury: To me, that's a perfect example of what Marshall Van Alstyne talked about this
morning. It's the change from focus on product to a focus on an ecosystem. Healthcare
traditionally has been very focused on a doctor providing product to patient, or a caregiver
providing a product to a patient. Now, we're actually starting to see that the only way we're able
to do this is through use of an ecosystem.
That's a hard transition. It's a business-model transition. I will put in a plug here for The Open
Group Healthcare vertical, which is looking at that from architecture perspective. I see that our
Forum Director Jason Lee is over here. So if you want to explore that more, please see him.

13. Gardner: I'm afraid we will have to leave it there. We've been discussing the practical
implications of the Internet of Things and how that is now set to add a whole new dimension to
Open Platform 3.0 and Boundaryless Information Flow.
We've heard how new thinking about interoperability will be needed to extract the value and
orchestrate out the chaos with such vast new scales of inputs and a whole new categories of
information.
So with that, a big thank you to our guests, Said Tabet, Chief Technology Officer for
Governance, Risk and Compliance Strategy at EMC; Penelope Gordon, Emerging Technology
Strategist at 1Plug Corporation; Jean-Francois Barsoum, Senior Managing Consultant for
Smarter Cities, Water and Transportation at IBM, and Dave Lounsbury, Chief Technology
Officer at The Open Group.
This is Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator
throughout these discussions on Open Platform 3.0 and Boundaryless Information Flow at The
Open Group Conference on July 21 in Boston. For BriefingsDirect, thanks again for listening,
and come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: The Open Group
Transcript of a BriefingsDirect podcast exploring the challenges and ramifications of the Internet
of Things, as machines and sensors collect vast amounts of data. Copyright The Open Group and
Interarbor Solutions, LLC, 2005-2014. All rights reserved.
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Information Flow Improvement
•
Gaining Dependability Across All Business Activities Requires Standard of Standards to
Tame Dynamic Complexity, Says The Open Group CEO
•
Big Data success depends on better risk management practices like FAIR, say conference
panelists
•
Improving signal-to-noise in risk management
•
CSC and HP team up to define the new state needed for comprehensive enterprise
cybersecurity
•
The Open Group Conference to Emphasize Healthcare as Key Sector for Ecosystem-
Wide Interactions
•
Managing transformation to Platform 3.0 a major focus of The Open Group Philadelphia
Conference on July 15

14. •
Platform 3.0 Ripe to Give Standard Access to Advnaced Intelligence and Automation,
Bring Commercial Benefits to Enterprises