Welcome to Musclic. We have with us today Prabir Kulkastar, someone who dons many hats, but I think perhaps one of the oldest hats has been the one of India's leading experts on the power sector. We will talk to him today about the electricity crisis in Texas that followed the winter storms that this part of the United States faced last month. The storms led to multiple failures across the state, various power stations, prompting the regulator to institute or known as rolling blackouts across the state to save the grid itself from failing. But even as the bigger disaster of grid failure was averted, the blackouts themselves led to extensive damage across the state. Prabir, many commentators in the US media have called this a fallout of a natural disaster, because once in a couple of decades kind of event, although arguably the frequency of such events may increase due to global warming, but nevertheless the focus has largely been on the scale of the natural disaster itself that led to the power crisis. Do you think this kind of characterization is correct or appropriate, especially when we are talking about the electricity crisis the state is still facing right now? You know, this is an interesting issue because it's obviously the combination of various things that led to the larger crisis that we can see, because you would take low temperatures. Well, you know, LNG for instance is pumped through subzero temperatures in Siberia. You have the northern Europe, you have Canada, you have northern United States, which routinely faces subzero temperatures during long parts of the winter. So by itself that doesn't stop a grid from functioning or the power utilities from functioning. So obviously there is something else which also led to the grid coming to a near collapse and also not only rolling block blackouts as they have been characterized, but also the economic dislocation which took place subsequently, combined with the fact that electricity being a fundamental necessity, the temperatures meant people were near freezing and there you had no heat because of the gas failure because even the natural gas pipelines froze, the pumping stations stopped working. You also did not provide alternate ways of heating the houses. So you had that and a lot of people, in fact, dozens of people have died in Texas because of that or due to carbon monoxide poisoning, they went and sat in their cars and as you know, then you can get to a rise of carbon monoxide poisoning because of that. Also the utilities, for instance the municipal water systems supplies, there's a notice 8.7 million Californians have been asked now to boil their water, apart from the freezing of their pipes, bursting of the pipes, damaged to homes because a lot of the pipes are uninsulated, they froze and they burst and when the water came back, the temperatures dropped, the houses, the walls, the various other water damage took place. So you have a really much larger disaster that took place. So the question is why wasn't this considered, whether the electrical system or the natural gas system were actually being prepared for winter and so on? And this is something that Texas was known from 2011-2014. They have been near failures of the grid and the fact that you need to winterize the equipment was known. So the question really is why did it not take place even when it was known and the answer really rises the question that you have given that is it because of the kind of regulatory system that was there in Texas and this is something that as we said, we already know that Texas has had earlier near failures of this kind once in 2011-2014. So this was not something which was not known that we have to winterize the equipment, extreme weather conditions do take place and particularly with global warming, you have extreme weather conditions taking place more frequently. So the real issue was that there is no incentive to provide reliability for the grid. The entire deregulation and Texas is one of the most extreme cases of deregulating the electrical system as well as the energy system also. That means natural gas and other systems. So that basically it was left to the retailers, traders, the electricity producers to game the system that if there is a scarcity, if the grid power is short, the grid is short of power, the price goes up. So there is a purpose incentive therefore to have a crisis by which you can then make more money and this particular case, the price of electricity went up from $22 to $9,000 per megawatt hour. That's a huge, huge jump and it remained there for about five days. Now this total amount, that extra payment that had to the grid forced the people to pay to the generators is in the range of $45 billion out to which 33 hours of this kind of extreme pricing was thought to be what Arkad made a mistake, the grid operator, essentially Arkad is really the grid operator, they made a mistake and the persistent with this beyond the period required but nevertheless even the three days which this was really enforced which would have been what according to the regulator, the power utility commission or whatever it's called, that they said it should have been there only three days not five days but even if we take that into account it's still $29 billion extra. Why is this extra money being paid? Because when the electricity supply drops, the prices go up, this is the nature of the electricity market that is created and therefore there is no incentive for people to see there is no scarcity and in fact as I said there's a purpose incentive to actually promote a scarcity and then of course the utilities producers can push in more power at a higher cost. So this is one part of it and of course the fact that Texas had no reserves in terms of interconnection with the larger grid that the US has, it has two grids that operate, national level grids that operate, Texas is isolated from both as a part of its Texas exceptionalism that Texas will do something completely different from all others. So I think if you look at it, this is the key issue why there was no attempt to winterize the equipment, no attempt to look at reliability, the belief was markets will deliver the whatever is required and that reliability was not an objective of the market, the objective of the market is quote unquote efficiency and efficiencies achieved only by the pricing mechanism. This is the underlying philosophy that the Texas grid had and unfortunately as somebody has said electricity is what the laws of nature is what electricity obeys, not the nature of the market, not the laws of the market and I think this is the fundamental contradiction that appeared between the two. One would have thought that you know given what happened in California almost more than 20 years ago now with Enron with a similar kind of situation, market manipulation happening and not just I mean Enron is a big example but if you look across the world in a lot of situations in fact very aggressive market reforms have converted what would essentially reliable surplus systems to systems that function at nearest scarcity levels. So you know why would, why is it do you think that Texas still pushes with this kind of a, I mean at least now hopefully there are so many class action suits etc they would have opened up but I mean it's not just Texas I think there are lessons that we can learn definitely from it but because if you look at what's happening in India right now there seems to be a similar push for deregulation at least a push for including as many retailers, as many traders, distributors and generators as possible so that we have a functioning power market do you think that we have something definitely to learn from Texas at least this should give us some pause in terms of going ahead with this kind of an idea. Well it's an interesting question that you raise because Enron has its fingers in all the three issues even though Enron collapsed in 2001 because of fraud so that was the Enron demise but it was the California market that opened and it is the Texas market which is structured essentially under the painful influence of Enron if you will on the Texans don't forget it was operating out of Houston so it was located in Texas itself and they were the ones who pushed this blueprint for the Texas reforms as well as what they have pushed in California. In fact the California debacle took place earlier and that led to two of the power utilities they are folding up that actually gave pause to the larger US deregulatory processes and most of the states in fact the two major grids did not follow in the footsteps of California taking that as an example of what not to do but unfortunately Texas really did not follow that. Texas went to extreme deregulation now there are two interesting things in Texas itself one is of course what is already unfolded that there is reliability is nobody's baby so that and effectively the price mechanism does not build reliability into the system it's an externality that has to be built into the system otherwise there's a necessity and electricity is a necessity everything collapses if electricity is not available and that's a something which is a part of the social contract that the state has to look after but if we leave that out and the part that I think becomes important is Texans did they actually benefit from deregulation by actually paying less price and there's a Wall Street Journal article which details that actually Texan space paid 28 billion dollars more than what the regulated utilities charged their consumers so the unregulated Texan market forced the consumers to pay 28 billion dollars more the last 20 odd years then what the regulated utilities that there are regulated utilities still in Texas which are there what they charge to their consumers so even deregulation did not help and Texas is also very lucky in having really well endowed energy resources wind is plentiful sun is plentiful it has natural gas in large quantities so these are the you know this is this is all there in abundance so they benefited from being close to the gas reserves of course they had the reverse problem they had no storage because you can always pull it out of the ground so if that stopped because of freezing and so on they had really no reserves from the utilities so but apart from that Texans actually did not benefit from deregulation so if they paid 28 billion dollars in excess who got this money and the answer is obvious it is the utilities it is the traders it is the other people who are gaming the market who got the benefit and if you look at for instance arc and bond then you will find there's heavily loaded in favor of the producers and the electricity utilities as well as of course the traders but it had only one residential consumer on this board as opposed to six from the others eight if you include alternate so it's a heavily skewed in favor of those who had a bed who actually made the money when you talk about Enron in India if you remember that this whole IPP and opening up the power market of course Enron was one of the pushers but let's not forget this starts from actually Magi Thatcher and if you take a step back it really goes back to Chile where the Chicago boys of Milton Friedman actually structured this whole theory of power being a market and how could power become a market if you unbundled of electricity companies made the grid the something which was sort of owned by the state or heavily regulated it but the distributors and the generators would play in this market and there also would be retail electricity operators would be just traders so you created what I would call a regulatory market there's no real market electricity because you don't have storage anything that you produce has to be used immediately if you're short the grid will sink if you are surplus of electricity grid will again sink so because it has to be continuously in balance this is a market which is artificially created by rules of the game that the regulators write and unfortunately this is not helping the consumers we have what you raised in India that what is happening yes because of Enron collapse there was some breaking of the system all those IPPs which had promised the earth did not deliver all that happened but nevertheless the reforms of unbundling have continued and unfortunately the central government is the largest owner of IPP in the country NTPC is really an independent power producer where all the distribution is with the state governments so therefore the policies are further skewed in favor of the producers against the distribution companies and also for the traders again against the distribution companies and finally the state governments who have to subsidize the distribution companies because they cannot pass the burden of high cost electricity on the consumer so this has been the fundamental problem which stems really from the Enron days and it is a part of trying to produce a market which doesn't exist and therefore instead of coming back to the fundamental issue that you want a stable regulated electricity system you have to consider the system as a whole not in its parts and create an artificial market but you know we can leave this talk out for the time being the essential issue is that if you look at Texas you again see the failure of the so-called market driven regularly the market driven system for electricity generation and consumption and I think the biggest lesson to learn from this is electricity is a necessity and if you do not consider the stability of the grid then you are not going to really be able to service the people and if Texas has shown that even with abundance of resources without proper regulation the markets will fail I think that's a clear warning to the rest of us that we have to look at the electricity sector again and how we look at it has to stem from what we are Ambedkar had proposed that essentially electricity is something that the state has to provide to its people it's a necessity it's not a luxury and I think that's even more important today than it ever was and the fact that you are trying to convert it to commodity which can be something that people can play with and make money even you've neither produce or general or consume electricity I think that approach of market driven electricity system is I think in crisis with what has happened in Texas it's also interesting this is what the United States pushes everywhere but apart from Texas and Texas is an extreme example because the republicans have controlled the state for a very long time rest of the United States this did not work after California so they took a step back but nevertheless they have been preaching this to the rest of the world I think that's what we also yeah go ahead we can also say that for example if there's an argument that in India we have the other end of the problem where we have surplus capacity now because so many new generators have come in and a lot of this is because generation was delicensed and you know anybody could put up a power plant and sell it but you have at the same time so many non-performing assets that these have become now with public sector banks because this was again the hubris of the market that you know at a time when there was shortage everybody thought that they did not need contracts to be able to sell their power and then you know the demand has not increased as expected and so now you have all of these this capacity lying around where there is no offtake of power so even in a situation of surplus you have that situation today being created but because you thought that it was a commodity that could be bought and sold and I think it's a it's an important point that you make that it is a necessity and it's also important I think to understand that it's not like any other commodity it's not like buying and selling soap it's not like even like telecom that there has been a attempt to draw that parallel very often that the telecom sector was deregulated of course we now know what's happening with the telecom sector as well but nevertheless there is a very different sector here it requires lines it requires infrastructure and I think we go back to what you said earlier that electricity follows the laws of nature not the laws of the market


Hello and welcome. My name is Shannon Kemp and I'm the Executive Editor of Data Diversity. We'd like to thank you for joining this month's installment of the Monthly Data Diversity Webinar series, CDO Vision. This series is designed to give the year-round education on data strategy topics in addition to our annual face-to-face event from which we just returned. It was a great event this year and we're already underway for planning next year to be held in Atlanta, Georgia. This month, John Lowley and Kelly O'Neill will be discussing a telling statement to corporate leaders why you must address EIM and DG, just a couple of points to get us started. Due to a large number of people that attend these sessions, you will be muted during the webinar. For questions, we'll be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag CDO vision. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper right for that feature. And as always, we will begin the follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce our speakers for today. Well-known industry analyst John Lowley is a business technology thought leader and recognized authority in all aspects of enterprise information management with 30 years experience in planning, project management, improving IT organizations, and successful implementation of information systems. He is the president and chief delivery officer at First San Francisco Partners. Also joining us is Kelly O'Neill. Kelly is the founder and CEO of First San Francisco Partners. Having worked with the software and systems providers key to the formulation of enterprise information management, Kelly has played important roles in many of the groundbreaking initiatives that confirm the value of EIM to the enterprise. Recognizing an unmet need for clear guidance and advice on the intricacies of implementing EIM solutions, she founded First San Francisco Partners in early 2007. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. And good morning, good afternoon. Good evening, wherever you may be, and thank you for joining us. Hello, Kelly, how are you? Hello, hello, everyone. I'm good. How are you? Good. This is going to be a really interesting talk today. So everyone that is on, sharpen your pencils, get your paper ready. You're going to hear a few things that you can use. And let's get started. Why we're here today is we need to address leadership today, and we need to address the messages that we get. A very common question we get, right, Kelly, which is how do I start this? How do I sell it? How do I sustain this? How do I keep people engaged? And we've been examining that on and off, and you'll see how here in a little bit. For almost a year now, kind of as a side research project, and we've got some ideas on the messaging, and we're going to talk about that. The real root problem here is we think that a lot of what we're doing and a lot of where people's heads are, are out of sync, where we're ahead of the ability to absorb some stuff. So we're going to have a practical message for leadership. Very practical. If you were looking for something that will wake them up, startle them and get you fired, you might not hear that today, but you're going to hear something that you may not have heard before. And we have a little vision to develop that message that's suitable for you that Kelly is going to go over. We are going to leave about 10 minutes or so at the end for questions. And as Shannon said, please submit your questions so we can go through those. Any questions we do not get to, we will address afterwards and answer in writing and blog those answers to. We've done that at times as well. The contributors to this are more than John and Kelly. About a year ago, this group of esteemed individuals started to talk on a phone on a regular basis about traction of data management, information management, and data governance. And you can see we have representatives there from Gartner Group, which is Doug, Danette McGilvery, who has her own firm, Kelly and I from First San Francisco. James Price is a brilliant fellow from Australia with his firm, Don Aaron, and of course Dr. Tom Redman, the data quality doctor. And he's been doing this work for a long time. And we've actually tossed a lot of things around, done some research, and we don't need to go into all that details. But a lot of what you're going to hear today comes out of some real serious thinking from a lot of really, really smart people. And we thank them for their time. So let's get moving here.  And the results out of that might be MDM, it might be analytics. But these are business things that are happening to companies and are things they want to do with the data. These are the actions companies want to take from data. Now the issue arrives with what we're talking about here and I can ask Kelly to chime in on this one. Are these really one-off projects or are these part of some type of a holistic strategy? And what we'd like to see is something a little bit more strategic, right? Yeah, absolutely. And I think the other thing is sometimes even if they are a one-off project, they can be a good indicator of what that strategic viewpoint looks like. And maybe the current most urgent need that's part of a bigger issue or problem that can be addressed as more of a philosophy. Absolutely right. And so, I mean, these are the business reasons, but you can still do a one-off data project or something that's more holistic. Or you can approach these holistically and, you know, you can act global and think local. There's lots of ways to do this. But the issue is at the end of the day we have to help companies do these things with our work. But the message we deliver is not quite being received because what we hear and what we see this is when we see an organization that is truly engaged with the data management world and those types of things, what we see is a company where they want to do data right. I mean, and that's kind of a different, an easy way to say that, data-driven. But they also are talking about the value of their data. And we had great interest at Enterprise Data World on how do we actually come up with metrics to measure the contribution of our data. Those organizations that have chief data officers are a top data job. They really take deliberate alignment of their business. And they put culture and business alignment before the technology. And there are many, many examples of success. And we're going to talk about when someone is successful what's going on versus unsuccessful in a little bit. The ones that aren't engaged, they're doing data projects. But what we notice is they apply it directly to a project. If we start to talk holistically, they go, we can't handle that. It's not us. We're not ready for it. Or you hear it's too abstract. We're seeing places that are hiring chief data officers and top data jobs and then getting rid of them within a year or turning them over because they've given them impossible tasks to do. You get to just do it, give me access to all the data type stuff. And the big symptom here is technology before culture. People are buying the tools and buying the technology before they've even addressed any of the organizational aspects. And as Drucker said, culture will have strategy for lunch. And a lot of organizations are learning this the hard way. But the message isn't quite being received because we've got a real dichotomy between successful organizations and organizations that are struggle. And this is a summary of what we see in those two things. So then you, of course, you want to do a root cause analysis here, right? If you want to, what is at the core of things? Well, it's a lack that this stuff is really important. But what creates that? Well, leadership and management doesn't feel they've got justification for it because they're not going through the right steps. They're not doing things for the business. They're doing the technology projects. Things aren't being enabled to support this. You do a big MDM project and you're supposed to have a good stable set of standards and some governance to make it successful. And we find that the data governance is pushed to the side or it takes a local project level flavor versus an enterprise flavor. And lastly, an operating framework or an engagement model.   Here's a book. Be a steward. And that's not how to do data governance, all right? You've got to engineer some type of communications model. There's a lot of root causes within those, but just kind of summarize it. Kelly, anything to add to that before we start to dive into some of our data here? I think maybe just to call out specifically data quality is not a root cause or a barrier. And so I think that that might be just something to explicitly call out here is that the challenges that we're facing aren't rooted in data. They're rooted in organizational issues. They're rooted in justification and business cases and business sponsorship and all of that. So just wanted to make that obvious. That is a really good point. What is missing from this slide? What is missing is data quality. What is missing is easy access to data. Those are requirements that we have to meet to be successful. Absolutely. Those aren't the root causes. And for those of you who've said keep your pencil sharp and your paper handy, that's something to write down, all right? Those are requirements. What most people have driving them as a goal or an objective is a requirement. And this messaging we're going to talk about has to address these root causes. So let's go into some of the research behind this. And there's a concept called diffusion. And we're all familiar with it, even if we don't know the name. And we'll get to that in a second. But first, Kelly's going to talk about how you get an organization to act different. Because that's kind of what we're talking about here. And I'm going to let Kelly have the floor here for a slide and a half or so. And so take it away, Kelly. Great. Thanks, John. Yeah, so what we're trying to think about is truly how do we act differently in what's driving this behavior or lack of behavior, right? That we have, that is impacting our ability to progress these data programs. And as John talked about, both within First San Francisco and with the group of other thought leaders that we participated with, we always come back to the why. Why is this hard? Why isn't it happening? Why isn't this so difficult? And so I wanted to pull in a thought process from a man named Dan Barnett. And Dan Barnett has a business consulting organization that helps companies to achieve their most optimum results. And I thought that this concept was really pertinent to the way that we think about data, because one of the biggest blockers is our culture. So how do we drive the culture? Well, ultimately, culture is based on the way that people believe and behave within their organization. So culture is that conglomeration of beliefs, behaviors, activities, unspoken norms that show how an organization both responds to their employees as well as they respond to their clients. And ultimately, beliefs are really what drive everything. And the way that people believe about their company is how leads to how they behave. And there's a lot of research behind this. And one of the things that gets, you know, John and I excited is it starts to get a little bit more interesting when we look into these root causes. So this research is based on a series of experiments that were done by a variety of universities, but they were all published in a Harvard Business Review article called Decisions in Desire. And essentially, they applied electrodes to these people's brains that are part of the experiments to see what parts of the brain become active when they make decisions.  It is where all of your fight-and-flight activities and how your, I guess, unplanned responses occur. So your decisions are made both in your limbic system and in your neocortex. And your neocortex is that human part of your brain that we all think about as what makes us different. This is our planned behavior. It is our predictive movement and proactive movement, not automatic movement. It is where we think that we make all of our important strategic and analytical decisions. But what they found is that it's actually between your limbic system and your neocortex. So that means when we make a decision, we're actually calling into some of these things around fight-or-flight. Anyway, the idea is that this link between your limbic system and your neocortex shows that our most inherent behavioral responses are also where we keep our beliefs. And our core beliefs drive our behavior in a very, very real way, not just from an external way, but the way that our brain processes these transmissions in our brain. Therefore, if we want to impact culture, we need to impact our beliefs. And this is kind of where we got back to, okay, so how do we impact the beliefs around data? And so this is a lot of what has led up to this conversation, is trying to impact the belief system that occurs within a company around data, as well as just within the industry as a whole. So the takeaway from this slide is that your beliefs drive your behavior, and your beliefs and your behavior together create the culture. And if we don't address the beliefs, the behavior and the culture around data, we will never get to the results that we want, and focusing just on the results is going to be a non-productive exercise. So what we're going to talk about now is kind of diffusion of beliefs, behaviors, and culture. Yeah, exactly.  Oh, okay, okay. Are you done? I'm done just because I think that you're much more knowledgeable about the Gartner hype cycle, and it's another application of how diffusion occurs and the way that people believe around technology and how they adopt it. Yeah, and something to tell our audience here is these two things are connected, because now someone's gone, wow, this is this really cool thing about limbic and neocortex, and now I'm looking at the Gartner hype cycle. And someone's elbow just slipped off the table with that, but they are connected, and here is why. First of all, if we take a topic like analytics, where everyone says we're going to be data-driven, we're going to do analytics, and then you understand that even with all of that perfect analytics, most business decisions are a factor of a very different process than what we saw here. You go, okay, then maybe we have to learn how to be data-driven. The other thing we have to learn is in terms of adopting data management and data governance, which is our topic here. What's that message? We've tried to measure this before. So we have these, what in hindsight seem to be limbic, because they're measures of behavior. There is no engagement here. These are great tools. We're not disparaging the tools at all here. There are terrific ways, these are terrific metrics. One great adoption thing is the Gartner height curve, and we all love the trough of disillusionment. I wish I'd invented that. That's just really cool. But it talks about how things ebb and flow until a new idea gets stable. But we're measuring the reactions to this, and the reactions to this, remember, as we talked about, are a function of two parts of the way people do things. We also have the famous adoption curve, which was set out by Everett Rogers. You have your innovator, early majority, late majority laggers. And then that came out in the late 70s, 80s, and then Jeffrey Moore in his classic business book, Crossing the Chasm, took that. And when you go from early adoption to the early majority, that's when something really takes off. So people say, oh, that's great. That's when we've adopted the technology, because we've gone from early adopters to early maturity. The thing is when you look at the Gartner height cycle, and you look at these, you're looking at a historical thing. It doesn't tell you how to do that. It just tells you what you've done and how events might pan out. So what we've got to do is move ourselves to a process of what's going on at these various phases. Because there's one thing that all of these both leave out. When you have an innovator and an early adopter, or you're at the beginning of a height cycle, somebody always has a success. And somebody takes that success and then becomes an innovator. And then an early adopter says, I can do that, and they do that. And then the early majority says, well, we can do that, and they do that, and et cetera, et cetera. What makes within each one of these steps, whether it's the height cycle or the diffusion curve from Rogers, what makes success? Where do people engage in the idea? And our esteemed group started to kick that around, and we did some research and found another set of ideas. And I hope I'm not making anyone sleepy here, but we're going to touch this really quick and then move on to the juicy stuff. And there's another group of research called the Technology Acceptance Model. And it is based on shifting your idea from feelings to action and moving your thinking into a more methodical way.  And this is more what we would think a rational type measurement of how people do things. So in terms of adoption, it's not an adoption curve. That's a look back. What we need is to master the diffusion of an idea in your organization no matter what maturity or part you are.  If you're an early adopter, fine. But it doesn't matter. You still have to go through a process to think more analytically and think more about data.  There's some things that have to occur. Some things have to change. All innovators go through an acceptance process as do all others on the adoption curve. The intent is to go from idea to practice no matter where you are. At the bottom of this little matrix here, we have our innovator through Laggard, which is the diffusion model from Rogers. But we also have the technology acceptance model up the left where something triggers you and then you actually use it. And I can put an X in every box on that matrix and find somebody out there in data governance and data management land that's done something in that box at that level of maturity and that intent and adoption of an idea. So you need to address acceptance of the idea no matter where you are. Here's the next thing to write down with your sharp pencil to Mr. Message. It doesn't matter that your organization is a Laggard. It doesn't matter that your organization is an early adopter or an early majority. It doesn't matter. You won't be successful unless you work diligently to get people to use the material that they've got. And it isn't a matter of turning it on and telling them Monday morning it's ready to go. It takes a little bit of work. We actually did a little bit of a survey amongst our peers. We picked out our contributors, the six people we talked about, a few other folks we know, and then some people that we just said, you know, take this and tell us what you think. This is not an exotic survey. Actually, at the end of the presentation, there are questions. I will show an example of the actual survey. But what it is, it's measured the acceptance of an idea. And to make it easy on people, we made it in an urban planning model. So if you're going to adapt data governance, all right, and your organization is a pioneer, all right, that would be an intensity of embracing the idea of like a one or something like that. If you're a more mature organization, how would you adapt it? So we asked a series of questions, and look how this is skewed. This is skewed way to the pioneer refugee settler. People who think that they're embarking on this is going to be a struggle, that it's going to take an extraordinary amount of work. Now, how do you sell that idea to management if you say, we've got this great idea, we're going to build a civilization, but it's going to take 100 years and have enormous costs and use an enormous amount of resources. We have to get people to think more as this is more of a community building or an urban metaphor. So you've got to change people's thinking. And this is from people who know this business, answered it this way. So we've got some strong evidence here that we just got to kind of change the way we think. We have to move to data-driven. We have to think data-driven. We have to think using data, all right? We just can't think about projects. The projects might even be getting in the way. Someone told me that the secret to a really good golf swing was ignore the golf ball, then put the golf ball down and let the golf ball get in the way. We're probably thinking about something like that along here, too. So what is the process here? Kelly's going to walk us through our five-step process and then we're going to dive back in for the next 15 minutes or so. She's going to go over the process and then we'll have the messages and then we're going to dive into your questions as to how we might be able to help you here. So Kelly, take it away. Great. Okay. So we're going to go back to this thought process of changing beliefs and behavior starts with changing culture. And the way that we want to think about this is creating a vision of how data will move your company forward. So I know that we've all done vision statements in the past. We've all done mission statements in the past. So this is just a way to consider the importance of doing a vision process and creating a vision statement to really abide by it and help drive your belief and your culture. And the idea is you want to have a vision statement that is crisp and it is resounding. It has a business purpose and it is already aligned with some of your other cultural norms and that vision should start to appeal to the belief system and to the limbic system and help people to respond. So this is just an example of a vision statement here in which the organization plans to be leveraging data for competitive advantage. And this is a vision statement that fit nicely within their company culture because they had a competitive culture. They were very externally market focused. They wanted to be the best of the best. And so bringing in this concept of competition and competitive advantage pulled in the limbic system and helped to drive this belief that data actually does help with their competitive advantage along with other things. But data has a role in the way that they compete just like some of their other behaviors and beliefs. So starting with a vision that is compelling and plays to the belief system is a great way to start. The vision then starts to be more further articulated in, as John introduced, kind of a five step process. The vision is then turned into a clear picture of what the future looks like and a picture of what it means once that vision is starting to be accomplished. That picture helps to engage people and help them to understand where they're headed. That picture, of course, also pulls in things like principles and other sorts of behavior aspects that we've all done before creating those guiding principles. Through the vision and the picture, the picture also informs the plan. That plan might be your roadmap. It might be your strategy. But the plan then helps people understand what are the steps that we're going to take in order to get to the picture and to accomplish our vision. And then the participation articulates what the individual roles are within the delivery of the plan. Now, one of the things that, on this kind of infographic, we had down at the bottom is the purpose. The purpose and the vision do, I guess, align tightly together because the vision is the statement of that aspirational goal and the purpose is the why. Why are we doing this? So together the vision and the purpose have that sort of limbic response that helps people to believe that this is a very compelling program within the organization. And then the picture, plan, and participation spells it out in a bit more detail. So this is a thought process of how we start to address diffusion, adoption. However, we want to think about it, but we're addressing it from its very base nature or the belief system, not just addressing it down at the end result, but really the plan and the participation. We're taking a step back and looking at it from the vision and the purpose and the belief system. So if we go to the next slide, this kind of links this process of division, purpose, picture, plan, et cetera into exactly where it hits the beliefs and the behaviors. So your vision and your purpose, what is your big, you know, I don't know if anybody uses the term be hags or your big, hairy, audacious goal or another organization. I've heard the wig, the wild, inspirational goal or any sort of high-level strategic statement of a goal that helps to inspire people is your vision statement. And then the purpose is why that big, audacious goal is going to be compelling for the organization. The two together help to address the limbic components in terms of those core beliefs of why we're doing this. Behavior is then addressed in a bit more of the why. So the picture of the future state, the principles that articulate how people are expected to behave, the plan in terms of exactly what are those steps within the roadmap, et cetera, and then who participates in the plan? How does it involve the rest of the organization? By addressing from this perspective, then we can start to look at the results. Those results are, of course, how we impact the business as a result of the EIM program. But again, when we're trying to think about adoption and diffusion, rather than starting with the results, which we have all done, by the way, including John and myself, we do want to take a step back and consider how that vision purpose system can better help us drive to the results, not just looking at that end state or the results. So this is kind of a five-step process to get to that core limbic decision-making process. Any thoughts on that, John? Are you on mute? Yes. First one must unmute to articulate one's beliefs and behaviors. That's right. There's... So just to tie this kind of our topic of messaging here today and again, something for the sharp pencil and the paper. There's a couple of really key things here that have been bandied about as optional, but the more and more we get into this and talk about it in research and, again, this group of six people that we've been working with, we're going to be on mute here. We've got some more things to come up with and all of that. So we're kind of mid-course on this, but there is definitely things we're sharing right now. A couple of things that are definitely we're sharing, things like principles. If you're going to get people to think differently in an organization, whether it's on the one project, go back to our little thought bubble slide, slide two or three. And I'm sorry, that was slide four. And either think holistically or not holistically, but still do things intelligently or on data. You've got to shift their thinking from reacting to a requirements perception and coming up with a data behavior or a data practice, sometimes the word that is used. What we've seen, when you have something about beliefs, then what goes along with any belief system, even outside the realm of data, any other type of belief system, you have principles and you have vision. So the emphasis here is to weigh in on this slide with Kelly. I cannot stress the importance of principles enough here. I cannot stress the importance of vision. Even if you are doing a data-centric initiative and you don't have the luxury of an enterprise oversight, you still want to use this as a laboratory to better manage your data, do principles. Without principles, you have no philosophical anchor for a change in belief. And you're changing beliefs that data is something out there that we deal with to something that's really, really central to how we do business. You've got to have those principles. I can't emphasize that enough. Who does what? The vision. It's all extremely important. We have lots of methodologies, right? We have maturity models and all of that, but if you really want to boil it down to the strong message, which is what our presentation is for here today, it's this simple shift from an incorporation of beliefs and behavior into being data and being data-driven on that. Ready to go on to the next one there, Kelly? Yes, and I just want to, I guess I said this already, but I just want to have everybody on the phone reconsider how they're using their vision statements, how you're using your principles, because you've probably all done an aspect of this already. So how would you align what you've currently done back to this concept of driving adoption via beliefs? And just consider should we take an opportunity to update these? If the written is correct and action-oriented, we just need to be using them in a different way. So just as you're thinking about it, consider that perspective as well. Okay. Now, let's see. So we have kind of the two ways to drive the messages, two different outlooks here, and Kelly and I talked earlier, we're just going to talk with this one together. There's the top down way, to engage leaders with a passion for data. So let's talk about that. I think the word passions is important, don't you, Kelly? Exactly. Again, going back to our beliefs and our limbic system, if we're passionate about it, it's important, right? It starts to drive our behavior. Absolutely. And James has several wonderful case studies of clients he's worked with. He's had fabulous success through information management. We don't see them a lot because he does work in another hemisphere, but some of his work is fabulous. Now, Dr. Redmond, another one, he introduced this concept at EDW, and it went over very well. More of a bottom-up type thing, the provocateur. I like that.  I am a data provocateur. I don't know if you put that on a resume or not, but it's, you know, the provocateur is someone who instigates change, right? And we do see organizations that do accomplish some bottom-up type of things. Or they take a project and do something really cool data-wise with it and then attract some attention and do that. But now the provocateur has to be a risk taker, too. They have to push things a little bit. That's why they're a provocateur. But that is another way to look at this kind of thing. Now, I will tell you right now that our group hasn't determined if one or the other should dominate or it should be both or it should be 60, 40, or 70, 30. We're still working on that. Or there's some middle-out option here, too, that there might be as well. But here's kind of two ways to look at how these messages can be delivered and how these messages can flow. Anything else on that one, Kelly, or move on? Yeah, and I would just say, think about what would work within your organization. Maybe you have a bottom-up program that's happening right now. And maybe you're trying to determine, is this valuable? Can we get this done as a bottom-up? Well, this is a great perspective. Think about this organization as provocateurs to help drive this. Or maybe you've got a top-down approach where your CEO has hired a chief data officer and you're trying to figure out how to drive a top-down. So based on your existing program, where are you and how can you leverage either one of these outlooks to help get the end result that you're looking for? Okay, all right. So actual delivering the message in just really just two or three more slides here, but these are going to... We're going to be talking about these a little bit here. First, reality. Kelly, this is the reality of what's going on right now, right? Yes. Whether we like it or not, it is. Yes. You want me to care? Okay, no problem. So we've got internal pressures. We've got external pressures. And a lot of times we think about those external pressures as negative, but in fact, catalysts can move the data program forward, such as regulatory compliance and increasing regulatory compliance, right? That is, you know, the greatest provocateur that we have, right? The secret is, of course, moving beyond regulatory compliance, but we do have a great external driver from that experience. We have great external drivers in terms of our demanding customers. I think that on that infamous slide four or whichever one with the thought bubbles, one of the big drivers of doing a data program are experience across multiple channels, right? So our clients are getting more demanding, our internal employees are getting more demanding, and the technology, like it or not, is giving us new opportunities to either push our data external to our firewall, keep it in-house, update some old legacy systems, et cetera. And all of this is being managed while you still have to keep the lights on internally, and you've got other sorts of internal pressures as well, both from a technology perspective and as well as the way that you're looking at how you manage your products and your customers. So it is actually kind of a nice time to start thinking about this because we've got a lot going on that will force the organization, whether they like to or not, to start to address data as one of those key assets that informs a lot of the decisions around how you manage your internal operations and your external go-to-market. So I have a story to kind of show what's happening here. We have several clients that are or organizations we speak and communicate with as well that are addressing the Internet of Things and we all probably know that the Internet of Things is if we think we have a lot of data now, oh my goodness, right? Just look what's coming. And this organization wants to address and has already set up its team for the Internet of Things and are starting to see the overwhelming technology challenges that they're going to have. But what they really haven't talked about and we've got to work with them about is what do you do with it? It's not just one thing to say, well, the Internet of Things is the next really cool tech that's coming down the road and we can do cool things with it. What can we do with it? Big data kind of started out as a third generation data mining analytics and we had an idea what to do with it. The Internet of Things is one of those is a classic laboratory for what we're talking about here today. Whether you're an early doctor or you're going to be a laggard, or a creator of that data, or you're forced to deal with it for competitive pressures, you are going to be in the data business pretty much end of discussion if you're a big company here pretty soon. And it's going to be a real challenge and if you don't accept that reality, wow, you're not going to get anything out of it. You're just going to spend an awful lot of money, I think, but that's it. So let's talk about the message that we want to give. And Kelly and I are going to kind of tag team on this because of that prior slide. First of all, it is all data now, isn't it? That prior slide, Kelly, it's all data. What really is there right now that isn't data? It's hard pressed to think of something. Yeah, and I think you may think that your company produces widgets, well, guess what? To understand your most operationally efficient way to produce the widgets to understand your most effective way to sell your widgets, all of that is data. So regardless of whether you produce like hammers, it's all going to be about data in terms of optimizing that process. And by the way, there is such a thing manufactured that is truly a widget. And I think we should probably check to see if there is some Internet of Think technology associated with widgets. There are a couple of different kinds of a certain popular draft beer to create the bubbles and it's called a widget. That's my next research project is we're going to have to examine a lot of widgets. The key here is your problems are solved now and your opportunities are grabbed with data. There's no such thing. So for the sharp pencil in the paper, there are a lot of indicators. Show me something without data and show me something that we can be successful if data is in everything we're doing and then we contrast how we treat our data now, then tell me how we're going to be successful here. And that's compelling. The intensity and the sincerity of this idea of data. Is it thought out? What's the business value? Do you have the stamina? We changed the word on that when we were doing this presentation. I used another word. I started to use willpower but it's more than just will. It's like training for the marathon. You might have the willpower to do it but you still have to build yourself up to be able to execute. Absolutely. And I think that part of what we want to address with this word because I do agree it's an important word is in the same way that we think about that adoption curve and where you have the trough of disillusionment. Well, that trough of disillusionment occurs within your own EIM program or data governance program or even like master data management or data quality program. You're doing you have a high likelihood of hitting a trough of disillusionment and so rather than abandoning the program do you have the stamina to continue to keep going based on the belief that this is going to be a competitive differentiator for your company, for example. So it is truly the stamina to get beyond some of these obstacles and not throw up your hands and say it's all too hard. We have better ways to spend our money and spend faster and all of that. I wonder how many of our listeners have been in an organization or participated in an effort where data management or data governance hasn't been quite successful. There's disillusionment around it and then something else happens and they have to do another initiative that requires similar activity but you get the word and I am pretty sure if we had a poll facility set up we could take a poll right now and that is how many of you are not allowed to use a word like data governance anymore or data management or data warehouse because you had a project that failed and instead of stamina to get through it you just don't use the word anymore, right? We're not going to do it anymore. Think of another way to use it, use a different word which of course confuses people and things like that. That's what we're talking about here. And again, those diffusion curves the Gartner Group, HypeCycle and the Jeff, the Rogers diffusion curve they're all valuable. You will have these things happening in markets and happening in technology and what is your will to get through those particular phases here? So here's the message and I'm just going to deliver it and allow Kelly to expand on it and then we'll kick it back to me for the questions and then I can show the survey while people are asking the questions. I have a couple of questions here already. I'll start with the first one and then we'll take it from there. Doing nothing about data management or data governance is not an option. Simple as that. Just sitting there is an option. Kelly, I think you have to either make a conscious decision and accept the risk that you're not going to mess with it or you're going to mess with it at a project level or you're going to embrace it. Thoughts on that? Absolutely. And I think that when the audience, when you're coming up with this, oh, we need budget to do this, we need budget to do that, consider the cost of doing nothing because there will be a cost to your organization of not addressing your data, whether it's addressing data quality, whether you're addressing your governance and protection, whether you're addressing your data management processes. Doing nothing ultimately has a cost to it and that cost actually grows exponentially over time. Whereas the cost of actually addressing a problem early on, that actually reduces the cost over time of managing that data. And so it truly is whether you accept the risk to do nothing or you accept the cost or the process to establish a discipline. And in most industries, doing nothing is truly not an option and will cause the organization to become non-competitive or to have some sort of regulatory compliance issue that then obviously creates other problems within the organization. And the other thing is this last bullet point I think is really, really important and it was one that John came up with, this concept of waffling sometimes is just as dangerous as doing nothing because waffling also creates some cost and going back and forth of, well, maybe we should, maybe we shouldn't, that sort of thing, that also creates a cost.  And if you're trying to calculate why we should do something, calculating a cost associated with that lack of action is just as relevant. And waffling has a finer point on it. When an organization says, well, you know, we're considering our options about this data management thing, but in the meantime, continue doing things. The organization put in a very precarious position of making assumptions. And those assumptions are invariably going to cost you more money. So, you know, the message here is pretty simple. Make a call, Mr. Executive, Mrs. Executive, make a call, all right? Either make a conscious decision and accept what we are now documenting as profile risk. You know, no matter what kind of organization you are, then make a call to try to embrace this idea and get people to function and engage at a higher level with data-centric type work, governance management, BI analytics, whatever it is. Make the call to make it a more holistic cultural thing or keep it at a local level and take a look at it again and accept the interim risks and document those interim risks. And we're right at our time for our questions. So, we'll just do it. Our next CDO vision series is June 9th. It's a CDO interview. Kelly, we have the person yet. Have we confirmed? I think we have. Absolutely. And so, you know, one of the reasons that I'm excited, and I didn't want to interrupt in the beginning, but when we were talking about some of the opportunities and challenges and the fact that there's been a lot of turnover from a CDO perspective, we have the concessions of a previous CDO on our next webinar. And John Batega, who was one of the very first chief data officers ever identified, promoted, given the ultimate opportunity, is going to be with us, talking about how to make the role of the chief data officer successful and what he's learned over the past in terms of successes and failures. So, I'm really excited to have him participate and to address this issue of, you know, I think we called it the top data job turnover. Why is there a lot of turnover, and what can you do to prevent it? Cool. So, John Batega, next one. And it's also June 2nd is the first Thursday of the month. That is a typo on my part. It's really June 2nd at 9 p.m. No, it's actually June 2nd at 2 p.m. Eastern Time on this Webified Broadcast Channel. Off to the questions now. Kelly and I will both take this one. It might take us a few minutes. By the way, if you have a question, please, shoot it in. We've only got a few here. I know there's more than that out there. And send them in. But I have a question right now here from Regu. And it is, we are doing the standalone type governance and data management with many projects and many initiatives. What is some advice to start to bring those together? Kelly, I'll kick that to you, and then I can follow up. So, it's always hard to answer questions if I can't ask a question about the question. But anyway, so what I'm assuming this means is that the standalone governance is project-based governance, where you're looking at data issues on a project-by-project basis. And so to me, this sounds like one of these bottom-up sort of grassroots sort of processes, which is okay, by the way. But maybe you can start to see some synergies across those different standalone initiatives, and you can identify economies of scale and how those standalone initiatives can start working together more coherently. And through taking a look at those consistent activities, consistent practices, and how they are addressing consistent data issues, you can start identifying where you want to create this concept of a vision and a purpose, and that sort of thing. And it might be very fundamental that your vision for data governance is to optimize efficiencies in your organization. And your purpose is to reduce costs by 30% of all new project initiation because of data. So you can still have a vision that is compelling, and it doesn't have to be grandiose if you are starting from the bottom-up. So that would be my thought. John, I don't know if you had some other thoughts as well. I think that's fine. I could expound, but I'd probably just be rephrasing some of what you said, and we have two more questions here, which I hope we can get to them. Here's the question. These are bold messages. What kind of opportunities should we look for to deliver these messages? And that's a good question because we've said, go say some pretty strong things to leadership. So maybe we should wax on the practical. I'll start with it, and Kelly, you can chime in. From the practical standpoint, you're going to have update meetings on large projects. You're going to have trip reports from conferences. You're going to have diagnostics after projects are finished that went well or didn't go well. You're also going to have situations to address, as Kelly remarked, external factors that are driving your need to manage data compliance, et cetera. So when you have those opportunities, is when that message gets delivered, and it is. Now we have to make a conscious decision at this point. Do we just do a one-off focused application of data governance, data management best practices, and document the risks? Or do we start to build out, whether it's bottom-up, be provocateurs, or be evangelical from the top down? Do we continue to build something of an enterprise perspective? But these opportunities will present themselves, and I think you need to deliver repeated small messages. Kelly, any other thoughts on that? Yeah, no, I would absolutely add to that. And some of this is also your internal stamina to do that. So identify those existing communication channels and communicate your message in that way. Identify your provocateurs that you can use to be your evangelist to communicate the same message. And so sometimes having this short put sweep vision statement means that you're all saying the same thing, and you can tie back to and reference that vision statement over and over and over. And I'm going to use another example. So I have three kids, two of which. One is six, and one is 12. And I swear they are the same person, but in a male and female body six years apart. And they, when they want something, they repeat it over and over and over and over. And I swear by the end, I ultimately agree, right? And I'm sure that you either have kids, brothers, sisters, anybody. The idea is, you know, repetition and helping to create some awareness by saying a consistent thing over and over and over, regardless of the communication channel, right? What we're about, we're leveraging data for competitive advantage. And this is how we did that today. This is how we did it in this meeting. This is how we did it with this project, et cetera. So that consistency and repetition is very valuable as well. I like that. Are we there yet? Are we there yet? Are we there yet? Absolutely, right? Can I have it? Can I have it? Can I have it? We'll call that the Bart Simpson technique. That's right. Okay. All right. One other here I've got, and then if nothing else flows in here, we'll be able to do a wrap-up here. Our organization has a training and organization change department. Is this something they should be involved in? And I think if the organization has that, you've already got a lot of stamina or potential there to do that. We've interacted with a few of those over time. Kelly, any examples come to mind? One recent example here in my town here in the Midwest where they had a lovely group of people that could facilitate anything and they were very, very, very helpful in building out a data governance program. So yeah, there are people whose whole specialty is to get people to think differently. And I would certainly leverage those. Any thoughts on those? Yeah, and I think it goes back to what you just said, John. So some of this needs to be practical. And if you've got existing mechanisms within your company, do everything you can to leverage them, whether it's one individual who has a change management title, whether it is someone in marketing that does your internal communication. So whatever you have internally that is tasked with a similar sort of activity, leverage them. Get them on side. Use them also as your provocateurs, right? So I think that that's a great opportunity that you have. And getting a wider net of people that you can both go to for help as well as for dispersion and diffusion of the message, it's absolutely a great opportunity to take advantage of it. Cool. All right. I don't see anything that we haven't kind of maybe touched on a little bit already and we are getting near the end of our time. Once again, June 2nd, 2 p.m. Easter time, true confessions from a chief data officer. Ooh, I like that. Anyway, I'm going to kick it back to Shannon for the wrap up. And I thank everybody for giving us an hour of their very busy time to listen to us. We are always grateful for that and we hope you learned something. Shannon? Absolutely. Thank you. John and Kelly, thank you so much for this great presentation. As always, really appreciate it. And thanks to our attendees for being so engaged in everything we do and submitting such great questions throughout. Just a reminder, I will send a follow-up email within two business days. So for this particular webinar by end of day Monday with links to the slides, the recording of this session, and anything else requested, that was requested throughout. And, yes, as John mentioned, June 2nd is the next webinar and hope to see you all there. I hope everyone has a great day and thank you very much. Thanks, John and Kelly.


Let's start another talk after coffee break and the stage is yours. Okay. Can you hear me? Okay. Thank you for coming to this session because in a world full of mobile phones and cloud services, actually, few people are interested in this type of environments. So really, thank you for coming and my name is Hongran Yu and people used to call me because that's my nickname. And now we are going to talk about old fashioned desktop environments that are still useful. Yeah. Okay. And self introduction. Where am I? I'm the creator of the LSD desktop environment. And also I'm one of the main developers of the new LSD, LSD cute desktop environment project. And I'm a Linux user, especially Debian Linux since 2004. And now I'm working for an AI startup company named Epicure in Taiwan. But previously I was a physician working in the hospital for six years, but I love software. So then I changed my data switch for my career and now I'm here. So yeah. Okay. So what's LSD? Some of you might have heard of this desktop environment, but some of you may not so familiar with it. LSD stands for Lightweight X11 Desktop Environment. And it used to be the default desktop environment with Raspberry Pi, but now they use their own desktop. But their own desktop is actually a derivative of LSD, so we can say they still use our desktop. And also it's the default desktop environment of the Lubuntu distribution. And the project was born in 2006, and actually the project is born here in Taiwan. And but few people know that. Yeah. And the design philosophy of this project, why we start such a project in 2006? Because there are already many desktop environments, but why yet another desktop environment? Because we have some different design philosophy. For example, we want the system to have low memory footprint, and it has minimal LSD design, and also it's GTK plus based. But the most different thing is the whole desktop environment is loosely coupled. And they are loose coupling between the different components and its modular. That means most of the component in the desktop environment can be used outside the desktop environment. So that's a big difference with others. But some people may argue that you just put a bunch of independent program together and call your desktop environment if they are not so integrated while it's called the desktop environment. But we want to challenge this kind of view. But actually, it turns out that the project went pretty well previously. And this also shows that Conway's law is somewhat correct. And the software, just the organization of the software system will be reflected by the structure of the organization that developed the software. Yeah.  So it looks like this streamlined old-fashioned. But I prefer the term classic. It's not old, it's just classic. Yeah. So simple and fast. And yep, just like Unix. OK, but during the years, it has been there for 12 years. And we currently face many challenges. For example, in a world full of mobile devices, actually, desktop and laptops now have less than half market share. And also, as you know, most of them are actually running Windows. And in the small fraction that runs Linux, only a very, very small fraction are using our desktop environment. And also, the impact is not as huge as before. And also, we suffer from a fragmentation of the technical stack. For example, Katie and Genoa are two very different camps. And also, some are more mobile-friendly. And some are more desktop-centric. And also, we have X11, Wayland, GTK2, GTK3. And GT4 is going to be released in the near future. And we have Qt45, QWidget, and QML, a lot of different toolkits to support. And also, we have a lot of different Linux distribution and this BSD flavor. So you have to make sure you work for all these kind of combination. It's getting more and more difficult. And also, given the technical difficulty and the technical fragmentation and the small market share, it's actually more and more difficult to get new developers to build up a new community. And also, more slow become a problem for us because being lightweight is not as important as before because more slow solve the problem. It's not us to solve the problem. It's more slow. So actually, you can claim that my desktop memory only need 100 megabyte of RAM. But that doesn't matter because your browser needs 2 gigabytes. So this used to be the problem we were solving in the past. But it cannot be the only focus for our project right now. Yeah. It is still very important for a business like MIPS or Raspberry Pi for those kind of stuff. So it is still values. Thank you for mentioning this. That's why we are still alive. Yes. But another unfortunate trend is if you type this into the Google search trend, you will find that GTK Plus VSQt. Actually, less people are interested in GTK Plus nowadays. And the overall trend is just declining. So it's really not so easy to find new developer for this kind of desktop project nowadays. So we really need some changes. Then we tried to address this issue in 2013. We tried to pour some core component to Qt, tried to see how it works. As you might know, LSD was GTK Plus based desktop environment. But we tried to pour some of the core components like the desktop file manager to Qt with the source code we written in C++. And of course, some of the KDE developed helpers during the process, especially wheels difference. We want to thank KDE developer for their help. So the initial experiment showed that actually porting the application to Qt from their GTK source code is possible. It's not easy, but it's possible. And later we found that there was another Qt-based desktop environment that showed the same design principle with us. It's named ReserQt. So we started talk to see if we can collaborate more. And later we found that the fact is simple. We have a desktop. We want to change the Qt, but we don't have enough of engineering resource. And they have a Qt code base, but they lost the momentum to continue the development. So then a historic event happened. And after some more collaboration, we decided to just merge two projects into one. So we formed a new project and then Qt. And this is why I say it's historic because this is rarely seen in the free software community. Every day you see new folks, but you can hardly see successful merges. There were merges, but most of them failed. Yeah, but our merge went out pretty well. So actually, we proved that it's possible for two projects with totally different tech stack merged together. So now in the jungle of desktop environment, we have a unique position. Because we come from the GTK Plus world and they came from the Qt camp. And there are also a lot of different desktop environment. But now we are sitting in the center of this desktop environment jungle and try to make everyone happy. But of course, that's very difficult. But we will try our best to make sure that we can still stay with our original goals but become more flexible. OK, so of course, we don't have LSU-Qt conf like this. But when I visit Paris, I try to find our team member there. So we had a dinner together. You can call that LSD conf. OK, so what about the new LSU-Qt desktop environment? And it still has the class UI. When I say class, it means more Windows-like. The UI that normal people will want to use. But it becomes Qt-based and we still stay with the same design printable as the original LSD and still being lightweight and quick response of the UI. That's very important. That's the core value of this project. It should be very fast, even on slower machine without such powerful hardware. But of course, it's not only for the ancient hardware. I keep mentioning classic desktop environment. It's not for all hardware that will be retired soon. So that's the whole point of this project. And still, we want to make every component modular so they can be used outside LSU-Qt. So even if you don't like the desktop environment, you can still use some of the component freely without problems, such as the file manager and archiver, image viewer, something like that. And we implement a Qt platform plugin that made the Qt program work better with Linux desktop. And that plugin can actually be used outside LSU-Qt. So other Qt program can also use that. And that's one of our core value. And also, we don't force you to use any window manager. You can choose whatever you want. And we actually encourage users to do this. But if you ask me, my personal recommendation will be Openbox. But you can use K-Wing, whatever you want. Most of them will work. So now it looks like this. Pretty similar to LSD. Or you can say pretty similar to Windows. Yep. But there's one thing that makes our project pretty unique because you can really see a project that uses so many different technologies from totally different cameras. Because as many people know, that the test back in Linux desktop is pretty much in a mess. And if you are supporting KDE and you are supporting GNOME, you might end up using totally different development tools. Library, the whole stack will be different. But we try to blend them together well and work without problems. So the main graphical user interface is implemented using Qt 5. And for some core system services, we are using KDE Taiwan libraries, like K-Windows system, to interact with the window manager. And we use KScreen to better handle your screen resolution adjustment, something like that. Because it's really hard to do with row XCB. Or if you want to support Wayland, you will become very difficult. So we try to reuse other people's work. And in some places, we use KDE Solid from work for the hardware discovery. And about the file system abstraction, the virtual file system, how to enable application from using accessing remote file system, we try to use GNOME file system. So we use GIO, GNOME GIO, Glib GIO, and GVFS. It's kind of unfortunate that this project has the prefix K or G. But actually, they are desktop independent libraries. But when people say, gee, they will say, no, you are using GNOME libraries. And no, you are pulling the KDE stack in your desktop. But no, they are just normal libraries, simple and independent. So we mix them together. And for the window manager part, instead of developing our own window manager, we actually encourage our user to just use their own favorite because there are already too many window managers. And but the default we will recommend is OpenBox or K-Win. They work the best, but you can choose whatever you want. And we have a config option for you to change the window manager easily. And we have a core library named Qtxtg. It's a free desktop spec implementation done by our project. And this library is actually used by some of other similar projects. So we use others work. And our work is used by others. And I think this is what free software should be like. And it's not like you are GNOME, I am KDE. You do yours, I do mine. We do ours, but things should not be like that. So we try to mix the good parts from different camps. And actually, we reuse some code from different projects. Of course, the file manager is ported from the GTK version of PC-MEM-FM-Qt. It's a file manager. Of course, FM means file manager, and PC-MEM is here. So it's actually pretty good to start your own project because you can name the software after your name, just like Debian, Linus. Yeah, so it's PC-MEM-FM. And it's ported from the GTK version. And also, recently, we have our own file archiver to handle a compressed file. And you may be surprised that it's implemented using Qt. But actually, it's ported from GNOME file roller, a totally different GTK program. But we show that it's possible. You can actually reuse the source code, the code, the library, everything from different projects. They are not so different. Only the UI and user experience should be different. But the underlying technology should be shared. That's our core value. And also, we provide the configuration tool for an open box and even a post-audio mixer. It's also ported from its original GTK version. And also, we reuse some code from KDE as well. The terminal emulator actually used some code from KDE console. And our config tool used some UI classes developed by KDE. And we mixed them together. And they worked pretty well without problems. Actually, there were some problems. There are still some caveats that need to be handled with care. For example, you can integrate with glib main loop inside Qt programs. And that is officially supported by Qt. But it's buggy. But most of the time, it worked pretty well. But if you are trying to emitting Qt signals within the g-object signal handler, that will not work. And it's a known bug for quite a long time without any fixes. And if you are doing multi-thread programming and do asynchronous IO with both libraries, and they have slightly different threading models. So if you are doing multi-thread asynchronous IO, which is the case of file manager, you will be in trouble. So this need to be handled with care. Otherwise, you will have some weird bugs. And also, in Qt, there is an environment variable. And then Qt, no glib. You know what it means. You can turn off glib integration. And if your program depends on glib and the user specifies this environment variable, your application will be broken in some ways. And we actually have some bug report related to this. Yeah, so there are still some caveats. And also, this one is very interesting. When you are mixing the C libraries and C plus library, you will find that in some C libraries, they will name their variable or function delete. But this is a keyword in C plus plus. So this code cannot be compiled together. And this really happened. It happened in the source code of FileRoller. So when I'm trying to pull that piece of code, it failed to compile until I see that, oh, the function name is delete. And I renamed that and it worked beautifully. And also, in the source code of glib and GIL, there are some functions named like variable named signal. But signal is a Qt keyword. So if you are trying to include this header file in your Qt program, you will have problems. So you need to disable the Qt macro with some compiler definition. Then this will fix the problem. And the other problem is, although Qt enabled faster development for us because the library is pretty easy to use, troubleshooting become more difficult. Because it's the form, many different platform and source code of Qt is pretty complicated. And when migrating from Qt 5, Qt 5.9, Qt 5.9, 5.11, we encounter some regression bugs frequently. And in most of the time, we will receive the bug report from our users, things like DND, drag and drop doesn't work, or screen resolution went wrong, something like that. And we will take a look and find out it's not our bug. Then we try to read Qt source code and help the upstream developer to fix them. We do this and cooperate with KDE developers and the Qt developers to get this issue fixed. Okay, and another pretty big issue is, it's really very, very difficult to have a consistent look and feel for your desktop environment because there are too many different UI tools, too many different configurations. You will have GTK2, GTK3, Q4, Q5, and some pure X11 programs and they will use maybe some use XFT settings and you have to also support S-Cursor, a lot of things, otherwise they just look different. And even if you try to support all of these, GNOME programs do look different. They are using client-side decoration and you have no option to turn it off. So it's pretty hard. And we try our best to support most of the things that are common, like font setting is actually not desktop independent. Not desktop dependent, it should be shared among different applications. So in our configuration tool, we try to accommodate this incompatibility and try to update a setting for different toolkit properties so they can look more or less unified. It's not really possible to make them has a similar look and feel, but we can try our best. At least don't make them look like aliens. Yeah, but that's not an easy task. And also in Qt, things become more difficult because now Qt file rely on some Qt platform plugin and each desktop need to develop their own plugins to support some better desktop integration. And some of the features are not provided by Qt by default. So we try to cultivate our own plugin and to support a better file dialogue or better theming, things like that. Yeah, but in GNOME GTK handles most of this, but in Qt you have to handle this yourselves. Okay, so what about the final result? The UI looks mostly similar with the original LSD, but people will say, no, no, don't use Qt, Qt is bloated. Why you use Qt? But actually it's not that bloated. Okay, it's somewhat bloated, but it's not that bloated. You can see that the end result, we did this experiment on Debian. And Debian Stretch, the current stable version and after code boot, okay, the experiment is done in virtual box because nowadays it's really hard to find such a limited hardware. But I set it to two CPU cores and give it 500 megabytes RAM and the screen resolution is set to this. And then I do code boot and then open a terminal emulator and the default file manager provided by the desktop. Because if you don't do this, you're only testing how much RAM will be needed by rendering the wallpaper. So you need to open some application and I just opened the default file manager for every desktop environment. But because open box window manager does not bring its own file manager. So in the experiment, I did not open any app in open box. So you can see that after code boot, if you're only looking with a bare bone window manager open box, your system will, the whole system will take 71 megabytes of RAM. And if you add on LSD, you will take 100. And for LSD, okay, I admit that it takes more memory, but it's not that much compared to the current standard. You can see other, actually the memory usage of the current else Qt is very similar to that of SFCE. So you can know that Qt is not really that bloated. And the whole desktop environment still used half of the memory usage of KDE Plasma. But of course KDE is much more feature rich. So it's just trade-off. It's not, any one of this is superior or inferior. It's just trade-off, different design philosophy. Yeah. So after all of the migration to Qt and adding new features, porting all of the code to C++, actually the resource usage is very similar, at least on the same scale as the original. But after all this, we got the same UI or no, similar UI with more memory usage and what was the benefit of doing this? Yeah, I will talk about what's the benefit later. But also finally, Lubuntu is going to switch from the GTK plus LCE to the new LSTQt in the upcoming release. Yeah. So, and sorry, I skipped this previously. Oops. Yeah, actually by doing this, now we have a much more active community because as I mentioned previously, it's now really hard to find someone who want to write C code with GTK plus and especially it's already hard to find someone interested in desktop project. It's even harder if you want to find someone who is interested in desktop, but the guy is not interested in KDE or GNOME or others. And the guy is interested in our desktop environment and then the guy's willing to writing C code with GTK plus, the probability is pretty low. So it's really hard to find new developer, but with all the new changes, new toolkit, C++, and all of the reusing technology from using project, now we have a quite active community and the core development team has about more than 15 developers and doing the development actively. And also our community become more active because of this. So I think that's the, and we only increase the memory usage by 50 megabytes. I think that's pretty cheap. So after all of this hard work, I think it's quite worthy because now we have a much more healthy community and development can still continue. It's much more active nowadays. And also our work can be reused by other Qt projects. Yep, so what about the future? The top one problem is the way to support because, but when can we have good way to support is still an open question because it's like chasing a moving target. Wayland is changing rapidly and some of the original free desktop specs are not yet designed for Wayland. So the old migration will take some time, but because we are reusing the K-Windows system library from KDE, I think it will make things much easier because we don't have to do all this low level stuff ourselves and we can reuse what has been done by other projects. So that's one of the reason we introduce library from other project and not do it on our own. It's pretty important to not waste the deep development resource to rebuild the same pool every time. Yeah, but, and also we are trying to improve the user experience because if you have very low memory footprint but the user interface is pretty bad, it doesn't make sense. Otherwise you can use a terminal emulator. It has the lowest memory footprint. Yeah, but to make a desktop environment it should have better user experience and this is what we did not do pretty well at the moment and we are trying to improve that. So any feedback on this one is really appreciated because we are not driven by any company and we don't have a foundation, something like that. So we don't have our own designers.  So it's more difficult to get the same level of design like some commercial solutions. But actually the code base itself is good but the UI just not look as polished. So we should improve this part. But we want to keep this experience classic. That means we are not going to hide every menu or we are not going to remove button you used to click. Yeah, we will keep the classic experience. That's the whole point of the project. And not make everything look like mobile phone. Yeah, and then we will still make sure the project is still, the desktop is still lightweight and fast and when you click anything it should respond quickly and that's pretty important. And but being lightweight is still important but it's not the only focus nowadays thanks to more slow, yeah. But I agree that it's still important for embedded system and also I think it's a very good desktop for devices like Raspberry Pi. But I rarely see people using that on Pi but actually it works pretty well. So if you have a Raspberry Pi, give it a try please. Yep, and we are going to do more application and make it more polished. So that's the current plan. But there's one thing I need to clarify here is the original LSD is not dead. It's still being developed and actively maintained thanks to our team member. Yeah, because the original plan was a full migration to a Qt and let LSD replace the original LSD desktop. But we realized that many users still prefer the original one and some of the developers are still willing to work on the old GTK code base. So now we keep two different product lines. So while the new feature are adding to LSD, LSD is still being actively developed. Just the development is not as fast as before but it's still being developed and maintained and now it's GTK3 ready. So if you are still using LSD, don't worry about that, you can continue using that. It will not be dead pretty soon. It will be, I think unless GTK itself is dead, LSD will be there. Yeah, so thank you for listening but I think we still have some time. So let's take a look at some, some, maybe show you some of the applications. Yep, this is the file manager and of course his name, yeah. Oh, okay, okay, maybe we can have some Q&A right now. Do we have some questions? Yeah, please. Okay, I have two comments and the first is just for your reference. The first one is that another successful example of project merging in free software is that it's SchoolLinux and the Debian EDU, okay, they merge together because they are doing the same thing. So because I have studied that for some time, okay. And the second is that actually, lightweight desktop environment is, they are still, the requirement is always there. And what we are doing now, maybe you know that is that to make old computers alive. And for example, just today, there will be a group of students that will be known to be information volunteers and the computers there are very old, 32-bit and actually currently only LSD works on such machines. So we are still, I made a version of the customized, customer error bound for them. But currently I have a problem is about the, you talk about the XDG implementation. And what confused me is about the XDG manual, XDG manual. Actually for KDE, for GNOME, the XDG manual implementations are actually, they, I have to say they don't follow the spec. And the XDE follows the specs, but I have a problem is that the underline in the manual is gone. You know, our physical project, we use underline to separate the tag and the application. But in the, I tried the newest error bound to 18.4, the problem is still there, it's just the underline is gone. Okay, I'm not sure if you have re-implemented. You mean the underline in the manual? In the manual, yeah. Manual items? Yeah, yeah, yeah, yeah. If the name contains the underline, it's, it will be gone. That's probably related to the implementation of like GTX. Yes, because only XDE has this problem. Oh, okay. Okay, KDE, GNOME, and XFCE, they all, the underline are okay, only XDE has this problem. Okay, I think it's not related to the XDG spec because in XDE we are actually using the project provided by GNOME. Yeah, but I have to say that GNOME does not follow the spec, very, very, very easy. But I know that you must reuse some of the component of that, but at least this is so far what I have tried. But I haven't tried LXQt. Okay, we can check that, but I would encourage you to try LXQt. Yeah, yeah, because so far, I'm also waiting for the L1 to 18.10. Yeah, because I need to make the live ISO, you know. So if I need to remove the XDE and push the LXQt again, it's not so easy. So just the two comments for you. Okay, thank you. Hi, I have a comment and also open question for everybody. So thank you, Pistons years effort to make this lightweight environment for us. But I want to point out one thing, the most role is not the limitation, it's challenging also the niche. For example, when I start to use the PC, I work on with Stackware using, let's say, 44, 45 floor piece. People, all guys probably remember that time. And still in, it's how many, 20 years later, we still have this problem because you have a new in-base system, let's say, Raspberry Pi or something like, say, Apple Watch. Still, we still have a low-end hardware. We won't have some kind of GUI on top of this. The question is now, we want to reproduce the experience on desktop into the new things. Is how can we create a new ecosystem which I think is very important for open source. How can we bring this kind of computational environment to some people, especially young people which cannot afford to have a PC for their own in the past to a new generation? In my case, I'm kind of an apology. So I'm the few people who play with the PC and the BitNet, but not that everybody can discuss it, but not for the young people and for the rural people. How can we bring this kind of thing to them? It's a new challenge. I don't have an answer, but I'm thinking this, I want to indicate this way to show this kind of thing. I still have an Apple PowerPC G4 which I cannot run anything but the Rubang 2. And so I'm thinking, I mean, if I can donate this computer to some young kid who don't afford to buy a new computer, I'd like to. And I think this is something we as the community of open source can think about. How can we give this kind of abandoned things from the old technology so that everybody, not just for some kind of engineer or some kind of rich guys in the city, we can also, we can all have this computing power to use these kind of work things. Thank you. Thank you. Yeah, we will keep trying to try our best to make sure it's still work for all the hardware. I just mentioned that it's not the only focus to stay lightweight. You still have to care the user experience. Hello, my name is Andreas Tiller. I just want to make a little remark that you joined two free software project. I really applaud this because it's so rare and you cannot mention it frequently enough that that's a good move because we have so many desktop environment but in principle we only need one good one. Thank you for this. Thank you. Hi, my name is Catherine Sutter. I want to thank you very much for your work. I've been using LXD for many years. Oh really, thank you. Yeah, thank you, nice to meet you. Right now I've been working on... I have a DD friend who is very angry at me for not making bug reports for LXD and I promise I will try to do better as a user. In terms of features, there are some practical things that I think might make it easier to use on a very tiny screen. I have used it in a Cherute on Nokia N900 which is a small Nokia phone that I think had Mego or something and they used Matchbox derivative. And I thought I made LXDE work on that but then I would have to do very frustrating things like find an alt key and click, something equivalent to an alt key and right click to move the window so I could find the button to click. So obviously those things would be tricky but I think they would make LXDE portable to many small arm boards that are coming out. Another one that I made it work on was the pocket chip. Their effort, they did a modified Debian install on that as well and I got on the user forums and called myself Debian user on the pocket chip forums. And again, Matchbox looked like it worked a little better because they did full screen but I don't know if I can help with these things in the future, but it'd be nice. Thank you very much. Actually it's a big headache for us because we actually encourage people to try different window manager but although they all claim to support NetWM protocol, most of them work totally different. When you plug in another window manager, you will get many problems here and there but you don't know the root cause. It should work but most of the time you just get some problems here and there but you don't know why. So I think that's one of the reasons why the project keep doing their own window manager. But I think it's better to let the user choose whatever they want. Yeah, but not all of the combination work. Yeah, so yeah, agree, agree. When you go to a new desktop and right now I have installed Cinnamon because I felt that it's a good effort, I should try it and I like it overall but it's slow and I have to click five, seven things just to do something I used to be able to do with one, you know. Yeah, if you have some bug report that cannot be handled, you can. Yeah, it's actually better to use the bug tracker but if you, yeah, if you want, yes. Do we have more questions? We still have five minutes of time. Or we can just take a look at. And then let's see demo or something. Actually from the, sorry. Actually, can you tell if this is a Qt program or GQt program, that doesn't really matter. Yeah, this is the final manager and it just looked like the original GQt version and quick responsive, I closed it and yep. Oh, whoops, move to another screen. Yeah, but okay. It's kind of frustrating with my window. Oops, doesn't work, never do that demo. Yeah, but there's one thing I want to show. This is our new file archiver and it's for a graphical user interface for you to handle compressed files and does anybody find that the UI look very familiar? It's ported from GNOME file router to Qt, yeah. And it just used the same way as the GTK version. Of course, we don't have client-side decoration but it's pretty similar and this is the file dialog. You can see that it's actually not a default file dialog provided by Qt, it's much more feature-rich and it looked pretty similar to the GTK file chooser but it's implemented in Qt and backed by our file manager. So we try to do better integration and make sure everything is still fast and responsive. Yep, and yeah, the overall user experience did not change that much by just changing your UI toolkit. Yeah, so if you feel frustrated with the older LSD, maybe you can try the new one. Any more questions? If not, then let's thank speaker and we have coffee break. Okay, thank you.