Tuesday, July 9, 2013

Want a data-driven business culture? Start sorting out the BI and big data myths now

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Dell Software.

Debunking myths around big data should be a first step to making better business decisions for improving data analysis and data management capabilities in your company.

As the volume and purpose of data and business intelligence (BI) has dramatically shifted, older notions and misconceptions -- what amount to myths about data infrastructure -- need to updated and corrected, too.

So we're here to pose some better questions about data, and provide up-to-date answers for running data-driven businesses that can efficiently and repeatedly predict dynamic market trends and customer wants in real time.

As the volume and types of data that are brought to bear on business analytics advance, the means to manage and exploit that sea of data needs to be none too costly nor too complex for mid-size companies to master. There are better ways than traditional data architectures.

To help identify what works best around modern big data management, BriefingsDirect interviews Darin Bartik, Executive Director of Products in the Information Management Group at Dell Software. The discussion is conducted by Dana Gardner, Principal Analyst at Interarbor Solutions. [Disclosure: Dell is a sponsor of BriefingsDirect podcasts.]

Here are some excerpts:
Gardner: Are people losing sight of the business value by getting lost in speeds and feeds and technical jargon around big data? Is there some sort of a disconnect between the providers and consumers of big data?

Bartik: You hit the nail on the head with the first question.  We are experiencing a disconnect between the technical side of big data and the business value of big data, and that’s happening because we’re digging too deeply into the technology.

Bartik
With a term like big data, or any one of the trends that the information technology industry talks about so much, we tend to think about the technical side of it. But with analytics, with the whole conversation around big data -- what we've been stressing with many of our customers -- is that it starts with a business discussion. It starts with the questions that you're trying to answer about the business; not the technology, the tools, or the architecture of solving those problems. It has to start with the business discussion.

That’s a pretty big flip. The traditional approach to BI and reporting has been one of technology frameworks, and a lot of things that were owned more by the IT group. This is part of the reason why a lot of the BI projects of the past struggled, because there was a disconnect between the business goals and the IT methods.

So you're right. There has been a disconnect, and that’s what I've been trying to talk a lot about with customers -- how to refocus on the business issues you need to think about, especially in the mid-market, where you maybe don’t have as many resources at hand. It can be pretty confusing.

I've been a part of Dell Software since the acquisition of Quest Software. I was a part of that organization for close to 10 years. I've been in technology coming up on 20 years now. I spent a lot of time in enterprise resource planning (ERP), supply chain, and monitoring, performance management, and infrastructure management, especially on the Microsoft side of the world.

Most recently, as part of Quest, I was running the database management area -- a business very well-known for its products around Oracle, especially Toad, as well as our SQL Server management capabilities. We leveraged that expertise when we started to evolve into BI and analytics.

I started working with Hadoop back in 2008-2009, when it was still very foreign to most people. When Dell acquired Quest, I came in and had the opportunity to take over the Products Group in the ever-expanding world of information management. We're part of the Dell Software Group, which is a big piece of the strategy for Dell over all, and I'm excited to be here.

Part of the hype cycle

Without disparaging the vendors like us, or anyone else, the current confusion is part of the problem of any hype cycle. Many people jumped on the bandwagon of big data. Just like everyone was talking cloud. Everyone was talking virtualization, bring your own device (BYOD), and so forth.

Everyone jumps on these big trends. So it's very confusing for customers, because there are many different ways to come at the problem. This is why I keep bringing people back to staying focused on what the real opportunity is. It’s a business opportunity, not a technical problem or a technical challenge that we start with.
It’s not a size issue. It's really a trend that has happened as a result of digitizing so much more of the information that we all have already.

Gardner: Even the name "big data" stirs up myths right from the get-go, with "big" being a very relative term. Should we only be concerned about this when we have more data than we can manage? What is the relative position of big data and what are some of the myths around the size issue?

Bartik: That’s the perfect one to start with. The first word in the definition is actually part of the problem. "Big." What does big mean? Is there a certain threshold of petabytes that you have to get to? Or, if you're dealing with petabytes, is it not a problem until you get to exabytes

It’s not a size issue. When I think about big data, it's really a trend that has happened as a result of digitizing so much more of the information that we all have already and that we all produce. Machine data, sensor data, all the social media activities, and mobile devices are all contributing to the proliferation of data.

It's added a lot more data to our universe, but the real opportunity is to look for small elements of small datasets and look for combinations and patterns within the data that help answer those business questions that I was referencing earlier.

It's not necessarily a scale issue. What is a scale issue is when you get into some of the more complicated analytical processes and you need a certain data volume to make it statistically relevant. But what customers first want to think about is the business problems that they have. Then, they have to think about the datasets that they need in order to address those problems.

Big-data challenge

That may not be huge data volumes. You mentioned mid-market earlier. When we think about some organizations moving from gigabytes to terabytes, or doubling data volumes, that’s a big data challenge in and of itself.

Analyzing big data won't necessarily contribute to your solving your business problems if you're not starting with the right questions. If you're just trying to store more data, that’s not really the problem that we have at hand. That’s something that we can all do quite well with current storage architectures and the evolving landscape of hardware that we have.

We all know that we have growing data, but the exact size, the exact threshold that we may cross, that’s not the relevant issue.

Gardner: I suppose this requires prioritization, which has to come from the business side of the house. As you point out, some statistically relevant data might be enough. If you can extrapolate and you have enough to do that, fine, but there might be other areas where you actually want to get every little bit of possible data or information relevant, because you don't know what you're looking for. They are the unknown unknowns. Perhaps there's some mythology about all data. It seems to me that what’s important is the right data to accomplish what it is the business wants.

Bartik: Absolutely. If your business challenge is an operational efficiency or a cost problem, where you have too much cost in the business and you're trying to pull out operational expense and not spend as much on capital expense, you can look at your operational data.
There's a lot of variability and prioritization that all starts with that business issue that you're trying to address.

Maybe manufacturers are able to do that and analyze all of the sensor, machine, manufacturing line, and operational data. That's a very different type of data and a very different type of approach than looking at it in terms of sales and marketing.

If you're a retailer looking for a new set of customers or new markets to enter in terms of geographies, you're going to want to look at maybe census data and buying-behavior data of the different geographies. Maybe you want datasets that are outside your organization entirely. You may not have the data in your hands today. You may have to pull it in from outside resources. So there's a lot of variability and prioritization that all starts with that business issue that you're trying to address.

Gardner: Perhaps it's better for the business to identify the important data, rather than the IT people saying it’s too big or that big means we need to do something different. It seems like a business term rather than a tech term at this point.

Bartik: I agree with you. The more we can focus on bringing business and IT to the table together to tackle this challenge, the better. And it does start with the executive management in the organization trying to think about things from that business perspective, rather than starting with the IT infrastructure management team. 

Gardner: What’s our second myth?

Bartik: I'd think about the idea of people and the skills needed to address this concept of big data. There is the term "data scientist" that has been thrown out all over the place lately. There’s a lot of discussion about how you need a data scientist to tackle big data. But “big data” isn't necessarily the way you should think about what you’re trying to accomplish. Instead, think about things in terms of being more data driven, and in terms of getting the data you need to address the business challenges that you have. That’s not always going to require the skills of a data scientist.

Data scientists rare

I suspect that a lot of organizations would be happy to hear something like that, because data scientists are very rare today, and they're very expensive, because they are rare. Only certain geographies and certain industries have groomed the true data scientist. That's a unique blend between a data engineer and someone like an applied scientist, who can think quite differently than just a traditional BI developer or BI programmer.

Don’t get stuck on thinking that, in order to take on a data-driven approach, you have to go out and hire a data scientist. There are other ways to tackle it. That’s where you're going to combine people who can do the programming around your information, around the data management principles, and the people who can ask and answer the open-minded business questions. It doesn’t all have to be encapsulated into that one magical person that’s known now as the data scientist.

There are varying degrees of tackling this problem. You can get into very sophisticated algorithms and computations for which a data scientist may be the one to do that heavy lifting. But for many organizations and customers that we talk to everyday, it’s something where they're taking on their first project and they are just starting to figure out how to address this opportunity.
For that, you can use a lot of the people that you have inside your organization, as well potentially consultants that can just help you break through some of the old barriers, such as thinking about intelligence, based strictly on a report and a structured dashboard format.
Often a combination of programming and some open-minded thinking, done with a  team-oriented approach, rather than that single keyhole person, is more than enough to accomplish your objectives.

That’s not the type of approach we want to take nowadays. So often a combination of programming and some open-minded thinking, done with a  team-oriented approach, rather than that single keyhole person, is more than enough to accomplish your objectives.

Gardner: It seems also that you're identifying confusion on the part of some to equate big data with BI and BI with big data. The data is a resource that the BI can use to offer certain values, but big data can be applied to doing a variety of other things. Perhaps we need to have a sub-debunking within this myth, and that is that big data and BI are different. How would you define them and separate them?

Bartik: That's a common myth. If you think about BI in its traditional, generic sense, it’s about gaining more intelligence about the business, which is still the primary benefit of the opportunity this trend of big data presents to us. Today, I think they're distinct, but over time, they will come together and become synonymous.

I equate it back to one of the more recent trends that came right before big data, cloud. In the beginning, most people thought cloud was the public-cloud concept. What’s turned out to be true is that it’s more of a private cloud or a hybrid cloud, where not everything moved from an on-premise traditional model, to a highly scalable, highly elastic public cloud. It’s very much a mix.

They've kind of come together. So while cloud and traditional data centers are the new infrastructure, it’s all still infrastructure. The same is true for big data and BI, where BI, in the general sense of how can we gain intelligence and make smarter decisions about our business, will include the concept of big data.

Better decisions

So while we'll be using new technologies, which would include Hadoop, predictive analytics, and other things that have been driven so much faster by the trend of big data, we’ll still be working back to that general purpose of making better decisions.

One of the reasons they're still different today is because we’re still breaking some of the traditional mythology and beliefs around BI -- that BI is all about standard reports and standard dashboards, driven by IT. But over time, as people think about business questions first, instead of thinking about standard reports and standard dashboards first, you’ll see that convergence.

Gardner: We probably need to start thinking about BI in terms of a wider audience, because all the studies I've seen don't show all that much confidence and satisfaction in the way BI delivers the analytics or the insights that people are looking for. So I suppose it's a work in progress when it comes to BI as well.

Bartik: Two points on that. There has been a lot of disappointment around BI projects in the past. They've taken too long, for one. They've never really been finished, which of course, is a problem. And for many of the business users who depend on the output of BI -- their reports, their dashboard, their access to data -- it hasn’t answered the questions in the way that they may want it to.

One of the things in front of us today is a way of thinking about it differently. Not only is there so much data, and so much opportunity now to look at that data in different ways, but there is also a requirement to look at it faster and to make decisions faster. So it really does break the old way of thinking.
People are trying to make decisions about moving the business forward, and they're being forced to do it faster.

Slowness is unacceptable. Standard reports don't come close to addressing the opportunity in front us, which is to ask a business question and answer it with the new way of thinking supported by pulling together different datasets. That’s fundamentally different from the way we used to do it.

People are trying to make decisions about moving the business forward, and they're being forced to do it faster. Historical reporting just doesn't cut it. It’s not enough. They need something that’s much closer to real time. It’s more important to think about open-ended questions, rather than just say, "What revenue did I make last month, and what products made that up?" There are new opportunities to go beyond that.

Gardner: When it comes to these technology issues, do you also find, Darin, that there is a lack of creativity as to where the data and information resides or exists and thinking not so much about being able to run it, but rather acquire it? Is there a dissonance between the data I have and the data I need. How are people addressing that?

Bartik: There is and there isn’t. When we look at the data that we have, that’s oftentimes a great way to start a project like this, because you can get going faster and it’s data that you understand. But if you think that you have to get data from outside the organization, or you have to get new datasets in order to answer the question that’s in front of us, then, again, you're going in with a predisposition to a myth.

You can start with data that you already have. You just may not have been looking at the data that you already have in the way that’s required to answer the question in front of you. Or you may not have been looking at it all. You may have just been storing it, but not doing anything with it.
Storing data doesn’t help you answer questions. Analyzing it does.

Storing data doesn’t help you answer questions. Analyzing it does. It seems kind of simple, but so many people think that big data is a storage problem. I would argue it's not about the storage. It’s like backup and recovery. Backing up data is not that important, until you need to recover it. Recovery is really the game changing thing.

Gardner: It’s interesting that with these myths, people have tended, over the years, without having the resources at hand, to shoot from the hip and second-guess. People who are good at that and businesses that have been successful have depended on some luck and intuition. In order to take advantage of big data, which should lead you to not having to make educated guesses, but to have really clear evidence, you can apply the same principle. It's more how you get big data in place, than how you would use the fruits of big data.

It seems like a cultural shift we have to make. Let’s not jump to conclusions. Let’s get the right information and find out where the data takes us.

Bartik: You've hit on one of the biggest things that’s in front of us over the next three to five years -- the cultural shift that the big data concept introduces.

We looked at traditional BI as more of an IT function, where we were reporting back to the business. The business told us exactly what they wanted, and we tried to give that to them from the IT side of the fence.

Data-driven organization

But being successful today is less about intuition and more about being a data-driven organization, and, for that to happen, I can't stress this one enough, you need executives who are ready to make decisions based on data, even if the data may be counter intuitive to what their gut says and what their 25 years of experience have told them.

They're in a position of being an executive primarily because they have a lot of experience and have had a lot of success. But many of our markets are changing so frequently and so fast, because of new customer patterns and behaviors, because of new ways of customers interacting with us via different devices. Just think of the different ways that the markets are changing. So much of that historical precedence no longer really matters. You have to look at the data that’s in front of us.

Because things are moving so much faster now, new markets are being penetrated and new regions are open to us. We're so much more of a global economy. Things move so much faster than they used to. If you're depending on gut feeling, you'll be wrong more often than you'll be right. You do have to depend on as much of a data-driven decision as you can. The only way to do that is to rethink the way you're using data.

Historical reports that tell you what happened 30 days ago don't help you make a decision about what's coming out next month, given that your competition just introduced a new product today. It's just a different mindset. So that cultural shift of being data-driven and going out and using data to answer questions, rather than using data to support your gut feeling, is a very big shift that many organizations are going to have to adapt to.

Executives who get that and drive it down into the organization, those are the executives and the teams that will succeed with big data initiatives, as opposed to those that have to do it from the bottom up.
It's fair to say that big data is not just a trend; it's a reality. And it's an opportunity for most organizations that want to take advantage of it.

Gardner: Listening to you Darin, I can tell one thing that isn’t a product of hype is just how important this all is. Getting big data right, doing that cultural shift, recognizing trends based on the evidence and in real-time as much as possible is really fundamental to how well many businesses will succeed or not.

So it's not hype to say that big data is going to be a part of your future and it's important. Let's move towards how you would start to implement or change or rethink things, so that you can not fall prey to these myths, but actually take advantage of the technologies, the reduction in costs for many of the infrastructures, and perhaps extend and exploit BI and big data problems.

Bartik: It's fair to say that big data is not just a trend; it's a reality. And it's an opportunity for most organizations that want to take advantage of it. It will be a part of your future. It's either going to be part of your future, or it's going to be a part of your competition’s future, and you're going to be struggling as a result of not taking advantage of it.

The first step that I would recommend -- I've said it a few times already, but I don't think it can't be said too often -- is pick a project that's going to address a business issue that you've been unable to address in the past.

What are the questions that you need to ask and answer about your business that will really move you forward?" Not just, "What data do we want to look at?" That's not the question.

What business issue?

The question is what business issue do we have in front of us that will take us forward the fastest? Is it reducing costs? Is it penetrating a new regional market? Is it penetrating a new vertical industry, or evolving into a new customer set?

These are the kind of questions we need to ask and the dialogue that we need to have. Then let's take the next step, which is getting data and thinking about the team to analyze  it and the technologies to deploy. But that's the first step – deciding what we want to do as a business.

That sets you up for that cultural shift as well. If you start at the technology layer, if you start at the level of let's deploy Hadoop or some type of new technology that may be relevant to the equation, you're starting backwards. Many people do it, because it's easier to do that than it is to start an executive conversation and to start down the path of changing some cultural behavior. But it doesn’t necessarily set you up for success.

Gardner: It sounds as if you know you're going on a road trip and you get yourself a Ferrari, but you haven't really decided where you're going to go yet, so you didn’t know that you actually needed a Ferrari.

Bartik: Yeah. And it's not easy to get a tent inside a Ferrari. So you have to decide where you're going first. It's a very good analogy.
Get smart by going to your peers and going to your industry influencer groups and learning more about how to approach this.

Gardner: What are some of the other ways when it comes to the landscape out there? There are vendors who claim to have it all, everything you need for this sort of thing. It strikes me that this is more of an early period and that you would want to look at a best-of-breed approach or an ecosystem approach.

So are there any words of wisdom in terms of how to think about the assets, tools, approaches, platforms, what have you, or not to limit yourself in a certain way?

Bartik: There are countless vendors that are talking about big data and offering different technology approaches today. Based on the type of questions that you're trying to answer, whether it's more of an operational issue, a sales market issue, HR, or something else, there are going to be different directions that you can go in, in terms of the approaches and the technologies used.

I encourage the executives, both on the line-of-business side as well as the IT side, to go to some of the events that are the "un-conferences," where we talk about the big-data approach and the technologies. Go to the other events in your industry where they're talking about this and learn what your peers are doing. Learn from some of the mistakes that they've been making or some of the successes that they've been having.

There's a lot of success happening around this trend. Some people certainly are falling into the pitfalls, but get smart by going to your peers and going to your industry influencer groups and learning more about how to approach this.

Technical approaches

There are technical approaches that you can take. There are different ways of storing your data. There are different ways of computing and processing your data. Then, of course, there are different analytical approaches that get more to the open-ended investigation of data. There are many tools and many products out there that can help you do that.

Dell has certainly gone down this road and is investing quite heavily in this area, with both structured and unstructured data analysis, as well as the storage of that data. We're happy to engage in those conversations as well, but there are a lot of resources out there that really help companies understand and figure out how to attack this problem.

Gardner: In the past, with many of the technology shifts, we've seen a tension and a need for decision around best-of-breed versus black box, or open versus entirely turnkey, and I'm sure that's going to continue for some time.

But one of the easier ways or best ways to understand how to approach some of those issues is through some examples. Do we have any use cases or examples that you're aware of, of actual organizations that have had some of these problems? What have they put in place, and what has worked for them?
There are a lot of resources out there that really help companies understand and figure out how to attack this problem.

Bartik: I'll give you a couple of examples from two very different types of organizations, neither of which are huge organizations. The first one is a retail organization, Guess Jeans. The business issue they were tackling was, “How do we get more sales in our retail stores? How do we get each individual that's coming into our store to purchase more?”

We sat down and started thinking about the problem. We asked what data would we need to understand what’s happening? We needed data that helps us understand the buyer’s behavior once they come into the store. We don't need data about what they are doing outside the store necessarily, so let's look specifically at behaviors that take place once they get into the store.

We helped them capture and analyze video monitoring information. Basically it followed each of the people in the store and geospatial locations inside the store, based on their behavior. We tracked that data and then we compared against questions like did they buy, what did they buy, and how much did they buy. We were able to help them determine that if you get the customer into a dressing room, you're going to be about 50 percent more likely to close transactions with them.

So rather than trying to give incentives to come into the store or give discounts once they get into the store, they moved towards helping the store clerks, the people who ran the store and interacted with the customers, focus on getting those customers into a dressing room. That itself is a very different answer than what they might have thought of at first. It seems easy after you think about it, but it really did make a significant business impact for them in rather short order.

Now, they're also thinking about other business challenges that they have and other ways of analyzing data and other datasets, based on different business challenges, but that’s one example.

Another example is on the higher education side. In universities, one of the biggest challenges is having students drop out or reduce their class load. The fewer classes they take, or if they dropout entirely, it obviously goes right to the top and bottom line of the organization, because it reduces tuition, as well as the other extraneous expenses that students incur at the university.

Finding indicators

The University of Kentucky went on an effort to reduce students dropping out of classes or dropping entirely out of school. They looked at a series of datasets, such as demographic data, class data, the grades that they were receiving, what their attendance rates were, and so forth. They analyzed many different data points to determine the indicators of a future drop out.

Now, just raising the student retention rate by one percent would in turn mean about $1 million of top-line revenue to the university. So this was pretty important. And in the end, they were able to narrow it down to a couple of variables that strongly indicated which students were at risk, such that they could then proactively intervene with those students to help them succeed.

The key is that they started with a very specific problem. They started it from the university's core mission: to make sure that the students stayed in school and got the best education, and that's what they are trying to do with their initiative. It turned out well for them.

These were very different organizations or business types, in two very different verticals, and again, neither are huge organizations that have seas of data. But what they did are much more manageable and much more tangible examples  many of us can kind of apply to our own businesses.

Gardner: Those really demonstrate how asking the right questions is so important.
What we have today is a set of capabilities that help customers take more of a data-type agnostic view and a vendor agnostic view to the way they're approaching data and managing data.

Darin, we're almost out of time, but I did want to see if we could develop a little bit more insight into the Dell Software road map. Are there some directions that you can discuss that would indicate how organizations can better approach these problems and develop some of these innovative insights in business?

Bartik: A couple of things. We've been in the business of data management, database management, and managing the infrastructure around data for well over a decade. Dell has assembled a group of companies, as well as a lot of organic development, based on their expertise in the data center for years. What we have today is a set of capabilities that help customers take more of a data-type agnostic view and a vendor agnostic view to the way they're approaching data and managing data.

You may have 15 tools around BI. You may have tools to look at your Oracle data, maybe new sets of unstructured data, and so forth. And you have different infrastructure environments set up to house that data and manage it. But the problem is that it's not helping you bring the data together and cross boundaries across data types and vendor toolset types, and that's the challenge that we're trying to help address.

We've introduced tools to help bring data together from any database, regardless of where it may be sitting, whether it's a data warehouse, a traditional database, a new type of database such as Hadoop, or some other type of unstructured data store.

We want to bring that data together and then analyze it. Whether you're looking at more of a traditional structured-data approach and you're exploring data and visualizing datasets that many people may be working with, or doing some of the more advanced things around unstructured data and looking for patterns, we’re focused on giving you the ability to pull data from anywhere.

Using new technologies

We're investing very heavily, Dana, into the Hadoop framework to help customers do a couple of key things. One is helping the people that own data today, the database administrators, data analysts, the people that are the stewards of data inside of IT, advance their skills to start using some of these new technologies, including Hadoop.

It's been something that we have done for a very long time, making your C players B players, and your B players A players. We want to continue to do that, leverage their existing experience with structured data, and move them over into the unstructured data world as well.

The other thing is that we're helping customers manage data in a much more pragmatic way. So if they are starting to use data that is in the cloud, via Salesforce.com or Taleo, but they also have data on-prem sitting in traditional data stores, how do we integrate that data without completely changing their infrastructure requirements? With capabilities that Dell Software has today, we can help integrate data no matter where it sits and then analyze it based on that business problem.

We help customers approach it more from a pragmatic view, where you're  taking a stepwise approach. We don't expect customers to pull out their entire BI and data-management infrastructure and rewrite it from scratch on day one. That's not practical. It's not something we would recommend. Take a stepwise approach. Maybe change the way you're integrating data. Change the way you're storing data. Change, in some perspective, the way you're analyzing data between IT and the business, and have those teams collaborate.
But you don't have to do it all at one time. Take that stepwise approach.

But you don't have to do it all at one time. Take that stepwise approach. Tackle it from the business problems that you're trying to address, not just the new technologies we have in front of us.

There's much more to come from Dell in the information management space. It will be very interesting for us and  for our customers to tackle this problem together. We're excited to make it happen.
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Dell Software.

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As Platform 3.0 ripens, expect agile access and distribution of actionable intelligence across enterprises, says The Open Group panel

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: The Open Group.

This latest BriefingsDirect discussion, leading into The Open Group Conference on July 15 in Philadelphia, brings together a panel of experts to explore the business implications of the current shift to so-called Platform 3.0.

Known as the new model through which big data, cloud, and mobile and social -- in combination -- allow for advanced intelligence and automation in business, Platform 3.0 has so far lacked standards or even clear definitions.

The Open Group and its community are poised to change that, and we're here now to learn more how to leverage Platform 3.0 as more than a IT shift -- and as a business game-changer. It will be a big topic at next week's conference.

The panel: Dave Lounsbury, Chief Technical Officer at The Open Group; Chris Harding, Director of Interoperability at The Open Group, and Mark Skilton, Global Director in the Strategy Office at Capgemini. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

This special BriefingsDirect thought leadership interview comes in conjunction with The Open Group Conference, which is focused on enterprise transformation in the finance, government, and healthcare sectors. Registration to the conference remains open. Follow the conference on Twitter at #ogPHL. [Disclosure: The Open Group is a sponsor of this and other BriefingsDirect podcasts.]

Here are some excerpts:
Gardner: A lot of people are still wrapping their minds around this notion of Platform 3.0, something that is a whole greater than the sum of the parts. Why is this more than an IT conversation or a shift in how things are delivered? Why are the business implications momentous?

Lounsbury: Well, Dana, there are lot of IT changes or technical changes going on that are bringing together a lot of factors. They're turning into this sort of super-saturated solution of ideas and possibilities and this emerging idea that this represents a new platform. I think it's a pretty fundamental change.

Lounsbury
If you look at history, not just the history of IT, but all of human history, you see that step changes in societies and organizations are frequently driven by communication or connectedness. Think about the evolution of speech or the invention of the alphabet or movable-type printing. These technical innovations that we’re seeing are bringing together these vast sources of data about the world around us and doing it in real time.

Further, we're starting to see a lot of rapid evolution in how you turn data into information and presenting the information in a way such that people can make decisions on it. Given all that we’re starting to realize, we’re on the cusp of another step of connectedness and awareness.

Fundamental changes

This really is going to drive some fundamental changes in the way we organize ourselves. Part of what The Open Group is doing, trying to bring Platform 3.0 together, is to try to get ahead of this and make sure that we understand not just what technical standards are needed, but how businesses will need to adapt and evolve what business processes they need to put in place in order to take maximum advantage of this to see change in the way that we look at the information.

Harding: Enterprises have to keep up with the way that things are moving in order to keep their positions in their industries. Enterprises can't afford to be working with yesterday's technology. It's a case of being able to understand the information that they're presented, and make the best decisions.

Harding
We've always talked about computers being about input, process, and output. Years ago, the input might have been through a teletype, the processing on a computer in the back office, and the output on print-out paper.

Now, we're talking about the input being through a range of sensors and social media, the processing is done on the cloud, and the output goes to your mobile device, so you have it wherever you are when you need it. Enterprises that stick in the past are probably going to suffer.

Gardner: Mark Skilton, the ability to manage data at greater speed and scale, the whole three Vs -- velocity, volume, and value -- on its own could perhaps be a game changing shift in the market. The drive of mobile devices into lives of both consumers and workers is also a very big deal.

Of course, cloud has been an ongoing evolution of emphasis towards agility and efficiency in how workloads are supported. But is there something about the combination of how these are coming together at this particular time that, in your opinion, substantiates The Open Group’s emphasis on this as a literal platform shift?

Skilton: It is exactly that in terms of the workloads. The world we're now into is the multi-workload environment, where you have mobile workloads, storage and compute workloads, and social networking workloads. There are many different types of data and traffic today in different cloud platforms and devices.

Skilton
It has to do with not just one solution, not one subscription model -- because we're now into this subscription-model era ... the subscription economy, as one group tends to describe it. Now, we're looking for not only just providing the security, the infrastructure, to deliver this kind of capability to a mobile device, as Chris was saying. The question is, how can you do this horizontally across other platforms? How can you integrate these things? This is something that is critical to the new order.

So Platform 3.0 addressing this point by bringing this together. Just look at the numbers. Look at the scale that we're dealing with -- 1.7 billion mobile devices sold in 2012, and 6.8 billion subscriptions estimated according to the International Telecommunications Union (ITU) equivalent to 96 percent of the world population.

Massive growth

We had massive growth in scale of mobile data traffic and internet data expansion. Mobile data is increasing 18 percent fold from 2011 to 2016 reaching 130 exabytes annually.  We passed 1 zettabyte of global online data storage back in 2010 and IP data traffic predicted to pass 1.3 zettabytes by 2016, with internet video accounting for 61 percent of total internet data according to Cisco studies.

These studies also predict data center traffic combining network and internet based storage will reach 6.6 zettabytes annually, and nearly two thirds of this will be cloud based by 2016.  This is only going to grow as social networking is reaching nearly one in four people around the world with 1.7 billion using at least one form of social networking in 2013, rising to one in three people with 2.55 billion global audience by 2017 as another extraordinary figure from an eMarketing.com study.

It is not surprising that many industry analysts are seeing growth in technologies of mobility, social computing, big data and cloud convergence at 30 to 40 percent and the shift to B2C commerce passing $1 trillion in 2012 is just the start of a wider digital transformation.

These numbers speak volumes in terms of the integration, interoperability, and connection of the new types of business and social realities that we have today.

Gardner: Why should IT be thinking about this as a fundamental shift, rather than a modest change?
There's no point giving someone data if it's not been properly managed or if there's incorrect information.

Lounsbury: A lot depends on how you define your IT organization. It's useful to separate the plumbing from the water. If we think of the water as the information that’s flowing, it's how we make sure that the water is pure and getting to the places where you need to have the taps, where you need to have the water, etc.

But the plumbing also has to be up to the job. It needs to have the capacity. It needs to have new tools to filter out the impurities from the water. There's no point giving someone data if it's not been properly managed or if there's incorrect information.

What's going to happen in IT is not only do we have to focus on the mechanics of the plumbing, where we see things like the big database that we've seen in the open-source  role and things like that nature, but there's the analytics and the data stewardship aspects of it.

We need to bring in mechanisms, so the data is valid and kept up to date. We need to indicate its freshness to the decision makers. Furthermore, IT is going to be called upon, whether as part of the enterprise IP or where end users will drive the selection of what they're going to do with analytic tools and recommendation tools to take the data and turn it into information. One of the things you can't do with business decision makers is overwhelm them with big rafts of data and expect them to figure it out.

You really need to present the information in a way that they can use to quickly make business decisions. That is an addition to the role of IT that may not have been there traditionally -- how you think about the data and the role of what, in the beginning, was called data scientist and things of that nature.

Shift in constituency

Skilton: I'd just like to add to Dave's excellent points about, the shape of data has changed, but also about why should IT get involved. We're seeing that there's a shift in the constituency of who is using this data.

We have the Chief Marketing Officer and the Chief Procurement Officer and other key line of business managers taking more direct control over the uses of information technology that enable their channels and interactions through mobile, social and data analytics. We've got processes that were previously managed just by IT and are now being consumed by significant stakeholders and investors in the organization.

We have to recognize in IT that we are the masters of our own destiny. The information needs to be sorted into new types of mobile devices, new types of data intelligence, and ways of delivering this kind of service.

I read recently in MIT Sloan Management Review an article that asked what is the role of the CIO. There is still the critical role of managing the security, compliance, and performance of these systems. But there's also a socialization of IT, and this is where  the  positioning architectures which are cross platform is key to  delivering real value to the business users in the IT community.

Gardner: How do we prevent this from going off the rails?
This is where The Open Group can really help things along by being a recipient and a reflector of best practice and standard.

Harding: This a very important point. And to add to the difficulties, it's not only that a whole set of different people are getting involved with different kinds of information, but there's also a step change in the speed with which all this is delivered. It's no longer the case, that you can say, "Oh well, we need some kind of information system to manage this information. We'll procure it and get a program written" that a year later that would be in place in delivering reports to it.

Now, people are looking to make sense of this information on the fly if possible. It's really a case of having the platforms be the standard technology platform and also the systems for using it, the business processes, understood and in place.

Then, you can do all these things quickly and build on learning from what people have gone in the past, and not go out into all sorts of new experimental things that might not lead anywhere. It's a case of building up the standard platform in the industry best practice. This is where The Open Group can really help things along by being a recipient and a reflector of best practice and standard.

Skilton: Capgemini has been doing work in this area. I break it down into four levels of scalability. It's the platform scalability of understanding what you can do with your current legacy systems in introducing cloud computing or big data, and the infrastructure that gives you this, what we call multiplexing of resources. We're very much seeing this idea of introducing scalable platform resource management, and you see that a lot with the heritage of virtualization.
Companies needs to think about what online marketplaces they need for digital branding, social branding, social networks, and awareness of your customers, suppliers, and employees.

Going into networking and the network scalability, a lot of the customers have who inherited their old telecommunications networks are looking to introduce new MPLS type scalable networks. The reason for this is that it's all about connectivity in the field. I meet a number of clients who are saying, "We’ve got this cloud service," or "This service is in a certain area of my country. If I move to another parts of the country or I'm traveling, I can't get connectivity." That’s the big issue of scaling.

Another one is application programming interfaces (APIs). What we’re seeing now is an explosion of integration and application services using API connectivity, and these are creating huge opportunities of what Chris Anderson of Wired used to call the "long tail effect." It is now a reality in terms of building that kind of social connectivity and data exchange that Dave was talking about.

Finally, there are the marketplaces. Companies needs to think about what online marketplaces they need for digital branding, social branding, social networks, and awareness of your customers, suppliers, and employees. Customers can see that these four levels are where they need to start thinking about for IT strategy, and Platform 3.0 is right on this target of trying to work out what are the strategies of each of these new levels of scalability.

Gardner: We're coming up on The Open Group Conference in Philadelphia very shortly. What should we expect from that? What is The Open Group doing vis-à-vis Platform 3, and how can organizations benefit from seeing a more methodological or standardized approach to some way of rationalizing all of this complexity? [Registration to the conference remains open. Follow the conference on Twitter at #ogPHL.]

Lounsbury: We're still in the formational stages of  "third platform" or Platform 3.0 for The Open Group as an industry. To some extent, we're starting pretty much at the ground floor with that in the Platform 3.0 forum. We're leveraging a lot of the components that have been done previously by the work of the members of The Open Group in cloud, services-oriented architecture (SOA), and some of the work on the Internet of things.

First step

Our first step is to bring those things together to make sure that we've got a foundation to depart from. The next thing is that, through our Platform 3.0 Forum and the Steering Committee, we can ask people to talk about what their scenarios are for adoption of Platform 3.0?

That can range from things like the technological aspects of it and what standards are needed, but also to take a clue from our previous cloud working group. What are the best business practices in order to understand and then adopt some of these Platform 3.0 concepts to get your business using them?

What we're really working toward in Philadelphia is to set up an exchange of ideas among the people who can, from the buy side, bring in their use cases from the supply side, bring in their ideas about what the technology possibilities are, and bring those together and start to shape a set of tracks where we can create business and technical artifacts that will help businesses adopt the Platform 3.0 concept.

Harding: We certainly also need to understand the business environment within which Platform 3.0 will be used. We've heard already about new players, new roles of various kinds that are appearing, and the fact that the technology is there and the business is adapting to this to use technology in new ways.

For example, we've heard about the data scientist. The data scientist is a new kind of role, a new kind of person, that is playing a particular part in all this within enterprises. We're also hearing about marketplaces for services, new ways in which services are being made available and combined.
What are the problems that need to be resolved in order to understand what kind of shape the new platform will have?

We really need to understand the actors in this new kind of business scenario. What are the pain points that people are having? What are the problems that need to be resolved in order to understand what kind of shape the new platform will have? That is one of the key things that the Platform 3.0 Forum members will be getting their teeth into.

Gardner: Looking to the future, when we think about the ability of the data to be so powerful when processed properly, when recommendations can be delivered to the right place at the right time, but we also recognize that there are limits to a manual or even human level approach to that, scientist by scientist, analysis by analysis.

When we think about the implications of automation, it seems like there were already some early examples of where bringing cloud, data, social, mobile, interactions, granularity of interactions together, that we've begun to see that how a recommendation engine could be brought to bear. I'm thinking about the Siri capability at Apple and even some of the examples of the Watson Technology at IBM.
In the future, we'll be talking about a multiplicity of information that is not just about services at your location or your personal lifestyle or your working preferences.

So to our panel, are there unknown unknowns about where this will lead in terms of having extraordinary intelligence, a super computer or data center of super computers, brought to bear almost any problem instantly and then the result delivered directly to a center, a smart phone, any number of end points?

It seems that the potential here is mind boggling. Mark Skilton, any thoughts?

Skilton: What we're talking about is the next generation of the Internet.  The advent of IPv6 and the explosion in multimedia services, will start to drive the next generation of the Internet.

I think that in the future, we'll be talking about a multiplicity of information that is not just about services at your location or your personal lifestyle or your working preferences. We'll see a convergence of information and services across multiple devices and new types of “co-presence services” that interact with your needs and social networks to provide predictive augmented information value.

When you start to get much more information about the context of where you are, the insight into what's happening, and the predictive nature of these, it becomes something that becomes much more embedding into everyday life and in real time in context of what you are doing.

I expect to see much more intelligent applications coming forward on mobile devices in the next 5 to 10 years driven by this interconnected explosion of real time processing data, traffic, devices and social networking we describe in the scope of platform 3.0. This will add augmented intelligence and is something that’s really exciting and a complete game changer. I would call it the next killer app.

First-mover benefits

Gardner: There's this notion of intelligence brought to bear rapidly in context, at a manageable cost. This seems to me a big change for businesses. We could, of course, go into the social implications as well, but just for businesses, that alone to me would be an incentive to get thinking and acting on this. So any thoughts about where businesses that do this well would be able to have significant advantage and first mover benefits?

Harding: Businesses always are taking stock. They understand their environments. They understand how the world that they live in is changing and they understand what part they play in it. It will be down to individual businesses to look at this new technical possibility and say, "So now this is where we could make a change to our business." It's the vision moment where you see a combination of technical possibility and business advantage that will work for your organization.

It's going to be different for every business, and I'm very happy to say this, it's something that computers aren’t going to be able to do for a very long time yet. It's going to really be down to business people to do this as they have been doing for centuries and millennia, to understand how they can take advantage of these things.

So it's a very exciting time, and we'll see businesses understanding and developing their individual business visions as the starting point for a cycle of business transformation, which is what we'll be very much talking about in Philadelphia. So yes, there will be businesses that gain advantage, but I wouldn’t point to any particular business, or any particular sector and say, "It's going to be them" or "It's going to be them."
Pick your industry, and there is huge amount of knowledge base that humans must currently keep on top of.

Gardner: Dave Lounsbury, a last word to you. In terms of some of the future implications and vision, where could this could lead in the not too distant future?

Lounsbury: I'd disagree a bit with my colleagues on this, and this could probably be a podcast on its own, Dana. You mentioned Siri, and I believe IBM just announced the commercial version of its Watson recommendation and analysis engine for use in some customer-facing applications.

I definitely see these as the thin end of the wedge on filling that gap between the growth of data and the analysis of data. I can imagine in not in the next couple of years, but in the next couple of technology cycles, that we'll see the concept of recommendations and analysis as a service, to bring it full circle to cloud. And keep in mind that all of case law is data and all of the medical textbooks ever written are data. Pick your industry, and there is huge amount of knowledge base that humans must currently keep on top of.

This approach and these advances in the recommendation engines driven by the availability of big data are going to produce profound changes in the way knowledge workers produce their job. That’s something that businesses, including their IT functions, absolutely need to stay in front of to remain competitive in the next decade or so.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: The Open Group.

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Monday, July 8, 2013

The Open Group July conference seeks to better contain cybersecurity risks with FAIR structure

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: The Open Group.

We recently assembled a panel of experts to explore new trends and solutions in the area of anticipating business risk, to help organization gain a foothold on better managed processes and structure for staying clear of identifiable weaknesses.

The goal: To help enterprises better deliver risk assessment and, one hopes, defenses, in the current climate of challenging cybersecurity and against other looming business threats. By predicting risks and potential losses accurately, IT organizations can gain agility via thoughtful priorities and thereby repeatably reduce the odds of losses.

The panel consists of Jack Freund, Information Security Risk Assessment Manager at TIAA-CREF; Jack Jones, Principal at CXOWARE and an inventor of the FAIR risk analysis framework, and Jim Hietala, Vice President, Security, at The Open Group. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

This special BriefingsDirect thought leadership interview comes in conjunction with The Open Group Conference to be held held beginning July 15 in Philadelphia. The conference is focused on enterprise transformation in the finance, government, and healthcare sectors. Registration to the conference remains open. Follow the conference on Twitter at #ogPHL. [Disclosure: The Open Group is a sponsor of this and other BriefingsDirect podcasts.]

Here are some excerpts:
Freund: We're entering a phase where there is going to be increased regulatory oversight over very nearly everything. When that happens, all eyes are going to turn to IT and IT risk management functions to answer the question of whether we're handling the right things.

Without quantifying risk, you're going to have a very hard time saying to your board of directors that you're handling the right things the way a reasonable company should.

As those regulators start to see and compare among other companies, they'll find that these companies over "here" are doing risk quantification, and you're not. You're putting yourself at a competitive disadvantage by not being able to provide those same sorts of services.

Gardner: So you're saying that the market itself hasn’t been enough to drive this, and that regulation is required?

Freund
Freund: It’s probably a stronger driver than market forces at this point. But especially in information security, if you're not experiencing primary losses as a result of these sorts of things, then you have to look to economic externalities, which are largely put in play by regulatory forces here in the United States.

Jones: To support Jack’s statement that regulators are becoming more interested in this too, just in the last 60 days, I've spent time training people at two regulatory agencies on FAIR. So they're becoming more aware of these quantitative methods, and their level of interest is rising.

Hietala: Certainly, in the cybersecurity world in the past six or nine months, we've seen more and more discussion of the threats that are out there. We’ve got nation-state types of threats that are very concerning, very serious, and that organizations have to consider.

Hietala
With what’s happening, you've seen that the US Administration and President Obama direct the National Institute of Standards and Technology (NIST) to develop a new cybersecurity framework. Certainly on the government side of things, there is an increased focus on what can we do to increase the level of cybersecurity throughout the country in critical infrastructure. So my short answer would be yes, there is more interest in coming up with ways to accurately measure and assess risk so that we can then deal with it.

Gardner: Please give us the high-level overview of FAIR, also know as Factor Analysis of Information Risk.

Jones: First and foremost, FAIR is a model for what risk is and how it works. It’s a decomposition of the factors that make up risk. If you can measure or estimate the value of those factors, you can derive risk quantitatively in dollars and cents.

Risk quantification

You see a lot of “risk quantification” based on ordinal scales -- 1, 2, 3, 4, 5 scales, that sort of thing. But that’s actually not quantitative. If you dig into it, there's no way you could defend a mathematical analysis based on those ordinal approaches. So FAIR is this model for risk that enables true quantitative analysis in a very pragmatic way.

Jones
For example, one organization I worked with recently had certain deficiencies from the security perspective that they were aware of, but that were going to be very problematic to fix. They had identified technology and process solutions that they thought would take them a long way toward a better risk position. But it was a very expensive proposition, and they didn't have money in the IT or information security budget for it.

So, we did a current-state analysis using FAIR, how much loss exposure they had on annualized basis. Then, we said, "If you plug this solution into place, given how it affects the frequency and magnitude of loss that you'd expect to experience, here's what’s your new annualized loss exposure would be." It turned out to be a multimillion dollar reduction in annualized loss exposure for a few hundred thousand dollars cost.

When they took that business case to management, it was a no-brainer, and management signed the check in a hurry. So they ended up being in a much better position.

If they had gone to executive management saying, "Well, we’ve got a high risk and if we buy this set of stuff we’ll have low or medium risk," it would've been a much less convincing and understandable business case for the executives. There's reason to expect that it would have been challenging to get that sort of funding given how tight their corporate budgets were and that sort of thing. It can be incredibly effective in those business cases.

Gardner: There's lots going on in the IT world. Perhaps IT's very nature, the roles and responsibilities, are shifting. Is doing such risk assessment and management becoming part and parcel of core competency of IT, and is that a fairly big departure from the past?

Hietala: It's becoming kind of a standard practice within IT. When you look at outsourcing your IT operations to a cloud-service provider, you have to consider the security risks in that environment. What do they look like and how do we measure them?

It's the same thing for things like mobile computing. You really have to look at the risks of folks carrying tablets and smart phones, and understand the risks associated with those same things for big data. For any of these large-scale changes to our IT infrastructure you’ve got to understand what it means from a security and risk standpoint.
We have to find a way to embed risk assessment, which is really just a way to inform decision making and how we adapt all of these technological changes to increase market position and to make ourselves more competitive.

Freund: We have to find a way to better embed risk assessment [into businesses], which is really just a way to inform decision making and how we adapt all of these technological changes to increase market position and to make ourselves more competitive. That’s important.

Whether that’s an embedded function within IT or it’s an overarching function that exists across multiple business units, there are different models that work for different size companies and companies of different cultural types. But it has to be there. It’s absolutely critical.

Gardner: Jack Jones, how do you come down this role of IT shifting in the risk assessment issues, something that’s their responsibility. Are they embracing that or maybe wishing it away?

Jones: Some of them would certainly like to wish it away. I don't think IT’s role in this idea for risk assessment and such has really changed. What is changing is the level of visibility and interest within the organization, the business side of the organization, in the IT risk position.

Board-level interest

Previously, they were more or less tucked away in a dark corner. People just threw money at it and hoped bad things didn't happen. Now, you're getting a lot more board-level interest in IT risk, and with that visibility comes a responsibility, but also a certain amount of danger. If they’re doing it really badly, they're incredibly immature in how they approach risk.

They're going to look pretty foolish in front of the board. Unfortunately, I've seen that play out. It’s never pretty and it's never good news for the IT folks. They're realizing that they need to come up to speed a little bit from a risk perspective, so that they won't look the fools when they're in front of these executives.

They're used to seeing quantitative measures of opportunities and operational issues of risk of various natures. If IT comes to the table with a red, yellow, green chart, the board is left to wonder, first how to interpret that, and second, whether these guys really get it. I'm not sure the role has changed, but I think the responsibilities and level of expectations are changing.

Gardner: Is there a synergistic relationship between a lot of the big-data and analytics investments that are being made for a variety of reasons, and also this ability to bring more science and discipline to risk analysis?

Are we seeing the dots being connected in these large organizations; that they can take more of what they garner from big data and business intelligence (BI) and apply that to these risk assessment activities? Is that happening yet?

Jones: It’s just beginning to. It’s very embryonic, and there are only probably a couple of organizations out there that I would argue are doing that with any sort of effectiveness. Imagine that -- they’re both using FAIR.
There are some models out there that that frankly are just so badly broken that all the data in the world isn’t going to help you.

But when you think about BI or any sort of analytics, there are really two halves to the equation. One is data and the other is models. You can have all the data in the world, but if your models stink, then you can't be effective. And, of course, vise versa. If you’ve got great model and zero data, then you've got challenges there as well.

Being able to combine the two, good data and effective models, puts you in much better place. As an industry, we aren’t there yet. We've got some really interesting things going on, and so there's a lot of potential there, but people have to leverage that data effectively and make sure they're using a model that makes sense.

There are some models out there that that frankly are just so badly broken that all the data in the world isn’t going to help you. The models will grossly misinform you. So people have to be careful, because data is great, but if you’re applying it to a bad model, then you're in trouble.


Gardner: We're coming up very rapidly on The Open Group Conference, beginning July 15. What should we expect? [ Registration to the conference remains open. Follow the conference on Twitter at #ogPHL.]

Jones: We're offering FAIR training as a part of a conference. It's a two-day session with an opportunity afterwards to take the certification exam.

If history is any indication, people will go through the training. We get a lot of very positive remarks about a number of different things. One, they never imagined that risk could be interesting. They're also surprised that it's not, as one friend of mine calls it "rocket surgery." It's relatively straightforward and intuitive stuff. It's just that as a profession, we haven't had this framework for reference, as well as some of the methods that we apply to make it practical and defensible before.
Once you learn how to do it right, it's very obvious which are the wrong methods and why you can't use them to assess risk.

So we've gotten great feedback in the past, and I think people will be pleasantly surprised at what they experienced.

Freund: One of the things I always say about FAIR training is it's a real red pill-blue pill moment -- in reference to the old Matrix movies. I took FAIR training several years ago with Jack. I always tease Jack that it's ruined me for other risk assessment methods. Once you learn how to do it right, it's very obvious which are the wrong methods and why you can't use them to assess risk and why it's problematic.

It's really great and valuable training, and now I use it every day. It really does open your eyes to the problems and the risk assessment portion of IT today, and gives a very practical and actionable things to do in order to be able to fix that, and to provide value to your organization.

Gardner: Are there any updates that we should be aware of in terms of activities within The Open Group and other organizations working on standards, taxonomy, and definitions when it comes to risk?
In government, clearly there has been a lot of emphasis on understanding risk and mitigating it throughout various government sectors.

Hietala: At The Open Group we originally published a risk taxonomy standard based on FAIR four years ago. Over time, we've seen greater adoption by large companies and we've also seen the need to extend what we're doing there. So we're updating the risk taxonomy standard, and the new version of that should be published by the end of this summer.

We also saw within the industry, the need for a certification program for risk analysts, and so they'd be trained in quantitative risk assessment using FAIR. We're working on that program and we'll be talking more about it in Philadelphia. Follow the conference on Twitter at #ogPHL.

Along the way, as we were building the certification program, we realized that there was a missing piece in terms of the body of knowledge. So we created a second standard that is a companion to the taxonomy. That will be called the Risk Analysis Standard that looks more at some of that the process issues and how to do risk analysis using FAIR. That standard will also be available by the end of the summer and, combined, those two standards will form the body of knowledge that we'll be testing against in the certification program when it goes live later this year.

Gardner: For those organizations that are looking to get started, in addition to attending The Open Group Conference or watching some of the plenary sessions online, what tips do you have? Are there some basic building blocks that should be in place or ways in which to get the ball rolling when it comes to a better risk analysis?

Freund: Strong personality matters in this. They have to have some sort of evangelist in the organization who cares enough about it to drive it through to completion. That’s a stake on the ground to say, "Here is where we're going to start, and here is the path that we are going to go on."

Strong commitment

When you start doing that sort of thing, even if leadership changes and other things happen, you have a strong commitment from the organization to keep moving forward on these sorts of things.

I spend a lot of my time integrating FAIR with other methodologies. One of the messaging points that I keep saying all the time is that what we are doing is implementing a discipline around how we choose our risk rankings. That’s one of the great things about FAIR. It's universally compatible with other assessment methodologies, programs, standards, and legislation that allows you to be consistent and precise around how you're connecting to everything else that your organization cares about.

Concerns around operational risk integration are important as well. But driving that through to completion in the organization has a lot to do with finding sponsorship and then just building a program to completion. But absent that high-level sponsorship, because FAIR allows you to build a discipline around how you choose rankings, you can also build it from the bottom up.

You can have these groups of people that are FAIR trained that can build risk analyses or either pick ranges -- 1, 2, 3, 4 or high, medium, low. But then when questioned, you have the ability to say, "We think this is a medium, because it met our frequency and magnitude criteria that we've been establishing using FAIR."
Different organizations culturally are going to have different ways to implement and to structure quantitative risk analysis.

Different organizations culturally are going to have different ways to implement and to structure quantitative risk analysis. In the end it's an interesting and reasonable path to get to risk utopia.

Jones: A good place to start is with the materials that The Open Group has made available on the risk taxonomy and that soon to be published risk-analysis standard.

Another source that I recommend to everybody I talk to about other sorts of things is a book called How to Measure Anything by Douglas Hubbard. If someone is even least bit interested in actually measuring risk in quantitative terms, they owe it to themselves to read that book. It puts into layman’s terms some very important concepts and approaches that are tremendously helpful. That's an important resource for people to consider too.

As far as within organizations, some organizations will have a relatively mature enterprise risk-management program at the corporate level, outside of IT. Unfortunately, it can be hit-and-miss, but there can be some very good resources in terms of people and processes that the organization has already adopted. But you have to be careful there too, because with some of those enterprise risk-management programs, even though they may have been in place for years, and thus, one would think over time and become mature, all they have done is dig a really deep ditch in terms of bad practices and misconceptions.

So it's worth having the conversation with those folks to gauge how clueful are they, but don't assume that just because they have been in place for a while and they have some specific title or something like that that they really understand risk at that level.
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: The Open Group.

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Sunday, July 7, 2013

Managing transformation to Platform 3.0 a major focus of The Open Group Philadelphia conference on July 15

Taken as a whole, the converging IT and business mega trends of big data, cloud, mobile and social amount to more than a mere infrastructure or device shift.

Businesses and organizations often embrace some, but not all, of these activities. Their legacy and experience with them individually varies greatly. Each business and vertical industry has its own essential variables. And rarely are the trends embraced in unison, with a plan for how to cross-reference and exploit the others in concert.

Moreover, there are even more elements to the current upheaval: the Internet of things, aka machine-to-machine (M2M), and consumerization of IT (CoIT) implications, as well as the building interest in bring your own device (BYOD). There's clearly a lot of change afoot.

It's no wonder that the coordinated path to so-called Platform 3.0 that includes all these trends and their inter-relatedness is marked by uncertainty -- despite the opportunity for significant disruption.
rarely are the trends embraced in unison, with a plan for how to cross-reference and exploit the others in concert.

So how should organizations factor standardization, planning, governance, measurement and even leadership over the productive adoption of Platform 3.0? The topic was initially outlined in an earlier blog post by Dave Lounsbury, Chief Technical Officer at The Open Group.

These questions will certainly play a big part of the upcoming The Open Group conference beginning July 15 in Philadelphia. While the theme of the conference is Enterprise Transformation and an emphasis on the finance, government, and healthcare sectors, The Open Group is working with a number of IT experts, analysts and thought leaders to better understand the opportunities available to businesses, and the steps they need to take to best transform amid the Platform 3.0 uptake. Follow the conference on Twitter at #ogPHL.

The Open Group vision of Boundaryless Information Flow™ to me forms a large ingredient to helping enterprises take advantage of these convergent technologies. A working group within the consortium will analyze the use of cloud, social, mobile computing and big data, and describe the business benefits that enterprises can gain from them. The forum will then proceed to describe the new IT platform in the light of this analysis, with an eye to repeatable methods, patterns and standards.

Registration open

Registration to the conference remains open to attend in person, and many parts of the event will be streamed or available to watch later. [Disclosure: The Open Group is a sponsor of BriefingsDirect podcasts.]

In a lead-up to the conference, The Open Group also organized a Tweet Jam last month around that hashtags #ogP3 and #ogChat to investigate how the early patterns for Platform 3.0 use and adoption are unfolding. I was happy to be the moderator.

Among some of the salient take-aways from the various discussion and the online Twitter chat:
  • Speed of technology and business innovation will rapidly change the focus from asset ownership to the usage of services, requiring more agile architecture models to adapt to the rate and impact of such change
  • New value networks will result from the interaction and growth of the "Internet of things" and multiple devices and the expected new connectivity that targets specific vertical industry sector needs
  • Expect exponential growth of data inside and outside organizations, converging with increased end-point usage in mobile devices, coupled with powerful analytics all amid hybrid-cloud-hosted environments
  • Leaders will need to incorporate new sources of data, including social media and sensors in the Internet of Things and rapidly turn the data into usable information through correlation, fusion, analysis and visualization
  • Performance and security implications will develop from cross-technology platforms across more federated environments
  • Social behavior and market channel changes will result in multiple ways to search and select IT and business services, engendering new market drivers and effects
And some Tweets of interest from the chat:
  • Vince Kuraitis ‏@VinceKuraitis -- Great term. RT @NadhanAtHP: @technodad #ogP3 principle of "Infonomics" introduced by @doug_laney #ogChat http://bit.ly/YnxXwe
  • jim_hietala ‏@jim_hietala -- RT @nadhanathp: @VinceKuraitis Agreed.  Introducing new definition for ROI - Return on Information http://bit.ly/VAsuAK  #ogP3 #ogChat
  • E.G.Nadhan ‏@NadhanAtHP -- Boundaryless Information Flow to be introduced into Healthcare @theopengroup conference in July' 13 http://blog.opengroup.org/2013/06/06/driving-boundaryless-information-flow-in-healthcare/ … #ogChat #ogP3
  • E.G.Nadhan ‏@NadhanAtHP -- Say hello to the Data Scientist - Sexiest job in the world of #bigdata in the 21st century http://bit.ly/V62TcG  #ogChat #ogP3
  •  Vince Kuraitis ‏@VinceKuraitis -- Business strategy and IT strategy converge @ Platform 3.0 #ogp3 #ogChat
Again, registration to the conference remains open to attend in person. I hope to see you there. We'll also be conducting some BriefingsDirect podcasts from the conference, so watch for those in future posts. Follow the conference on Twitter at #ogPHL.

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