 Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining the latest installment of the DataVercity Webinar series, Data Insights and Analytics, brought to you in partnership with First San Francisco Partners. Today, Kelly O'Neill and John Lathley will discuss trends in data analytics from database to analyst. Just a couple of points to get us started. Due to the large number of people that attend these sessions, he will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen or if you like to tweet. We encourage you to share highlights or questions via Twitter using hashtag DIAnalytics. As always, we will send a 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 to you our speakers for today. 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 intricacies of implementing EIM solutions, she founded First San Francisco Partners in early 2007. John 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. John frequently writes and speaks on a variety of technology and EIM topics. His information management experience is balanced between strategic technology planning, project management, and practical application of technology to business problems. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. Hello. Good morning. And hello, everybody. I am here. I am here. Sorry. I was wrestling with my mute button. All right. Let's get, yeah, those mute buttons, they're tricky. Well, welcome back everyone to our December data insight analytics webinar. Oh my gosh, Shannon and John, I cannot believe that we've gone through 11, almost 12 of these now for the year. So super exciting. And we're excited to continue the webinar series in partnership with Dataversity next year. So although this is the last one for 2017, we will launch back quickly in January and we welcome your continued active participation throughout 2018. So it's been a wonderful year, but today we're talking about trends. And of course, this is a common area to explore at this time of year. I've seen lots of articles going around in terms of what's hot, what's not, what's trending up, what's trending down. And as this year closes out and the new one starts, we've thought about a way that we can bring together what we've presented in some previous webinars and also just talk about what people should be considering as they start to move into 2018 as well. And we're pulling together ideas that we have seen both through our client work, what we're seeing and learning through industry exposure at key conferences. There was another conference this week down in Florida and then ideas from our peers across the industry as well. So lots of different sources of the information that we've pulled together today. The way we're going to explore this is we've focused on trends like people, technology, data and process. And so what we're going to do is we're going to walk through each of these areas and we welcome your comments throughout the webinar. And then of course, we always take questions at the end and we will do our best to have time available at the end as well. And like most of our webinars, we do want to start with a polling question. So Shannon, why don't you go ahead and open the poll? What we're trying to learn is how do you stay current on trends and industry news? So do you search the internet? Do you subscribe to different channels and or publications? Clearly some of you dial into webinars and attend conferences. Do you learn from your colleagues? Do your companies have interdepartmental training? Maybe they have data and analytics training as part of core curriculums at your organization. Maybe it's some combination of the above. So all right, we've got just a few more seconds left. Hopefully everyone can participate. This is an easy question and helps us to understand how we can continue to serve you and be an educational source for you as well. Okay, I think we're about closed out here. All right. Do we have the answers, Shannon? The poll has ended. The suspense is terrific. I know. Did you not see it? I just pushed the poll results out. Did you? I cannot see them. Am I looking at the wrong spot? Now I see it. Thank you. Okay, so 13% of you chose search the internet. You know, it's funny because a couple of years ago we did a research paper also in partnership with Dataversity. And at the time it was the topics were business intelligence and data science. And at that time a majority of people were learning via just searching the internet. So it's interesting to see how this has changed. Subscribing to channels or publications, attending webinars and conferences. Well, this is a pretty big category, second only to all of the above. So I do think that the webinars and conferences seem to be supporting this topic area a bit more. It seems like we can tell by the poll. All right. Well, let's just jump into our next trend or our first trend, excuse me. We decided to start with people because really without the people then all of the other trends around technology, data and process aren't fulfilled and aren't sustainable. So, John, why don't you kick us off then with your thoughts around what's trending and how our audience can ride the trend. So especially as they think about their resourcing plans for the new year, what are some of the trends and obstacles that they might be seeing and how would they want to plan accordingly? Well, I'd be delighted and thank you. So, you know, trend discussions are always fun because, you know, as Kelly said around this time of the year, really, really nice to speculate on what's happening, what's going to happen. And we're doing budgets and everything is like that. But we're going to make sure we're a little prescriptive today too. So we'll talk about the what and then we're going to talk about some of the reactions you might want to give to those. We put people up first deliberately. It's not something you typically see in a trend. The trends tend to be technology, business movements and things. But our number one topic still for our work and our number one issues even around the big data and analytics with our work is boiling down to people. So, of course, culture. Everyone talks about, oh, well, yeah, culture. Okay, and that has been with that label, the number one obstacle, and we'll continue to be so as organizations start to figure out where their analytics functions sit, what big data really means to them, where where discoverants live, continue to debate some things that are new to them. The next part of that is training and with big data and analytics, what we hear in conjunction with those words is being data driven. Well, you know, what does that mean? Organizations aren't being very clear with their expectations because they probably don't know right off the bat what that actually means. So we're going to find this to be, this is going to be a real tough one for 2018 for leadership who have been told from some either by the market or by their board or by whatever leadership to be data driven. That course boils down and into the organizational impact in terms of people's roles and people's titles. There's a lot going on in that area. There's many, many forms of analysts. There's that that people are going to have to figure out and we're even starting to hear the terms citizen data analysts, which is just any old person can be an analyst now. All of those, all of us that are listening have been probably through at least one or two iterations of technology change. And we, this is very, very common. Who does what? And what's the title? Real key there though is the CDO, CDO, Chief Digital, Chief Data, both roles needing to be clarified. We've seen some problems and we see other organizations that are going to continue to struggle with, with what are the roles of these top level positions. And lastly, it's going to be super, super critical to have that racy or rascally mentally in your head. And, and already for our pipeline for next year and from the conference I was just at in Florida last week, we're seeing a lot of questions about, well, who's accountable? Who has to carry this forward? And you're going to have to have a people plan. So along the line of prescriptive, let's just go. Kelly, if you don't have anything to add to that, we can move on to the next one. I just want to talk about how to react to that. Yeah, you know, I thought that you bring up the citizen concept and there's really been a lot of, a lot more conversation around the citizen roles, the citizen data scientists, the citizen data analysts like you indicated. We've even seen roles like a citizen data engineer, a citizen data steward. And the idea is that the, this is a broader number of people within the organization able to perform data and analytics roles without that specialized training. So I think that that kind of rise of the citizen role impacts each of these categories. So how does that impact your culture? How do we maintain and create a data driven culture? And is the citizen role a response to that data driven culture? Or is this sort of distributed and federated world going to impact all of these different categories? So I think, you know, the foundation of this people plan and ensuring that there's clarity of role across a distributed and federated organization is really important. And I did want to just clarify if anyone didn't ask the question around what a racy versus a rasky. We've also seen ACs. So racy is responsible, accountable, consulted and informed and it's a way of identifying who does what within a process or an activity. The rasky is adding the clarity around supporting roles and who supports a function. Some organizations consolidate this rather than adding an S they remove the R and call it an AC so accountable, consulted and informed. Anyway, just to make sure everyone's clear on what those different categories are. Okay, so now let's move on to how do we ride this trend. John sauce. Sure. Well, this is the fun one. We put it first, the organization chart. When, when we do our work, we get to the operating framework because we don't like to call that an organization unless you actually really working on the organization. But once you create how your capabilities will be deployed or what capabilities will be deployed. The org chart always comes up and this is not going to change at all all into 18 and probably in the 19th. And the real key here is if you're going to be data driven and really serious about it, depending on your industry and your culture, you might see more than one need to do some reorganizations. So please just be open minded to that. There is, you know, we've roles are going to continue to evolve in conjunction with that you will have new job descriptions. So, if you are connected with human resources or, you know, get them ready. If you're not start to groom a relationship with human resources, if you're becoming data driven, or analytics and big data are playing a more prominent role in your organization because they're there are when you start to add attached to the racy and the rasky charts. You're adding new account abilities and new account abilities means different performance measures, etc, etc. You can see how that daisy chains into a human capital issue. The collaboration and cooperation between it and business we all know that that's always there. It's, it's, it's becoming more prominent and that means work to improve collaboration and working together. All right, so collaboration is a little different than cooperation cooperation is kind of working together collaboration is working together but having an agreement really that if if one party doesn't agree you still have a way to go forward. And, and that's something to kind of get yourself ready to deal with that's a mindset to have rolling into 2018. And of course, the sustaining plan or the organization change management or whatever. When you go to being data driven. When you start to rely more on data as a monetization either as a revenue source or an expense manager, and you're bringing data into much more prominent. You're making changes and changes. Just to recap really quick we've said this many times before on our series here. Three things happen when change happens and two of those things are not what you want to happen. You don't want me to end up where you started and you don't want to end up taking an entirely different path and giving up on on the cause. You want to move forward to a data driven world. So, if you don't have a sustaining plan for your big data program. Or you have kind of a life training thing and and there is no thought about how do we embed this into our culture to be thinking about data on a day to day basis in addition to processes and stuff. It's time to get that ready to go. You will need that in 2018. These just a quick story and I think Kelly will probably have some more to share here after. We have seen several times in the past year and are actually working with a few now on into next year where the chief data officer is really a chief analytics officer. And, you know, in one case it works okay because the organization had no expectations in another case. The expectation in was the chief data officer was going to manage the entire data asset, but they only concentrated on analytics and everyone was kind of confused there for a while. So, I'm spending some time on the people stuff is going to be really, really important Kelly back to you for another story or for your additional comments. Yeah, so I think that the this concept of the chief data officer which now is across probably a majority of organizations have the concept of a chief data officer or a top data job. Now with the advent of the chief analytics officer and then as was mentioned in the previous slide, a chief digital officer. There is a potential for confusion and for some battling for seniority amongst those three different roles and which one is subsumed under the other role. So I think that that's important to understand in this revisiting of the org charts around what works and what is going to be most sustainable. I think the other thing that when we're thinking about org charts and we're also thinking about IT and business collaboration, it seems that it's collaboration cooperation, but also some of these roles that are traditionally business or traditionally it seem to be starting to merging or starting to merge or blending. John are you seeing that too. Yeah, I'm seeing a muddying of the waters between what business traditionally has done and it is traditionally done. From a prescriptive standpoint, it's it's kind of connected with this collaboration thing in some organizations business has taken over what would have been traditionally data things because they're tired of waiting or impatient and there there is some friction. Okay. Between the two and others it has more or less of stepped back and said we are going to be the wires the fires and the plumbers and the movers of things like that. And you do what you need to with the data. There are pros and cons to both of them. And both of those approaches and again, spending some formal time on how you're going to connect it to is really, really important and I think from an encouraging note here. I was listening to a speaker this week who who has had a great style and everything was presented in a positive note. And I kind of got inspired by some of that so in a positive note. We should understand that those lines between it and business are blurring and by the way they've never been separate businesses within your business. They're just different departments. And everyone's paycheck says the same thing up in the corner of the document. So, so it pays to get everyone on the same team and take conscious efforts to do that. We've been saying that for years, but in 2018 it's going to become really visibly important to do that. Yeah, I would agree. And I think it is more so now than ever and people being comfortable with technical role within the business, a business role within technology, etc. And it's also I feel interesting that the educational institutions are starting to support this view as well in the sense that the analytics programs are not necessarily within the computer science department. So we've got some affiliations with some schools and one of the schools were affiliated with their analytics program is underneath their marketing line of study. Another school has their analytics program under their, you know, continuing liberal arts and professional studies, not so not necessarily business, but not technology or computer science either. So I find that that whole blending of capability is really interesting, although our organizational structures still tend to be hierarchical and departmentally driven. So anyway, food for thought on that one or charging for sure they sure will be. Yes. Okay. Onward and upward here onward. So let's talk about technology. So lots of folks on this line are really interested in the new technical trends and that sort of thing. This is an area that keeps us all on our toes because there seem to be emerging technologies and vendors. And consistently throughout the year and talking about what's coming up and what's going down is always very time dependent because give us another month and it might change. Anyway, John, what are you seeing out there. Well, this one is so cool. I think we actually have two pages of trends. Yes. So database technology that's this one I put, you know, I put this one first week. We take this around. We said, yeah, let's do that first. Things like graph databases are going to require you to really open your mind. You know, Hadoop was a bit of a mind bent for folks, but everyone kind of got their head around it now here comes graph. And now you're starting to see a real strong likelihood that that if you haven't gotten your head around it, you're going to have more than one type of database technology, not just different vendors with the same type but different architectures. You're going to have those. So you're going to have that. And you know, the cool thing about graph is that graph is going to be probably the go to ways of controlling your vocabularies, your semantics, your navigation around data lakes and things like that. And we have some prescriptive stuff coming up here and how to deal with that blockchain. What did I see this morning, Bitcoin 19 no 12,000 something like that is just ridiculous. The technology I'm not talking about Bitcoin today, the technology behind that is going to continue to expand in its application. So you need to be exploring that this blockchain will become a part of your architecture in the future. Now, there's not a lot of examples now. So this is one of those things. This is a fun trend. We can go study something and maybe not be held accountable for quite yet. Okay. But, but I had talks with at various times with some of our peers like Len Silverstone over this week, a Malcolm kind of dropped in several other folks. And we all ended up coming to just blockchain do this does blockchain do that how does it do that. Why are they mining for blockchain. All these questions are running around, and everyone has a quasi accurate answer, but nobody has it now done but this technology is powerful. It has a lot of different things that can go not much not unlike relational math, when you have cod and date started to to come out with the relational aspects of things at the beginning. Nobody knew what some of you folks aren't at all. To beginning, nobody knew what to do with a relational database. It was a truly foreign concept. Now, you know, now it's, it's, it's pretty standard. Moving on the predictive analytics as an enterprise capability will grow predictive analytics have been there for a long, long time organizations have always been doing those this type of work. But as an embedded relying on, you know, data telling us what to do is becoming culturally accepted. So that's going to just continue to embed itself even in companies you wouldn't have thought about it. The Internet of things, lots and lots of data from lots and lots of places means handling and stuff really, really fast. And so of course your technology is going to have to be able to drink from a fire hose, such as it is. Kelly anything on this page or what can just flip on. You know, I think one of the other things about the Internet of things is, yes, the need for speed and companies were putting everything on the Internet, you know, whether it's the nest, you know, technology, whether it's your car, what have you. But one of the things that this has also created is an awareness and acknowledgement of the need for greater security, because in the rest to put so many devices on the Internet, many security gaps were not properly closed. And so it's the Internet of things I think is, you know, it might change in terms of where it's placed on our trend chart for next year. In fact, I've read some articles that in which people are saying that the Internet of things is falling off the rising trend. And is now in the not hot category based on some of these security risks associated with it. So, anyway, I think that's very interesting. Okay, a next page of technology. Yeah, yeah. AI and machine learning. If you want to sound smarter to talk to the party now, say, right, or machine learning. It's coming mainstream, but everyone, you know, you can get to in a handful of people, someone thinks, no, we can't do this. It's kind of, and we don't want to do that. And for those of you that are not science fiction inclined, Skynet is the evil machines taking over the world from the Terminator movies. Yeah, anyway, it shows what I do in my spare time. Anyway, but, you know, there's really good stuff on the other end are people saying now it's impossible, can't, you know, can't happen. This is just sophisticated algorithms. It's not a futuristic map. Things like that. The point here is that it's not going to go away. And, and you've got to start to understand it's for example the difference between AI machine learning machine learning is a subset of AI are not two different things. There's lots of AI you can do a lot of AI type things without doing machine learning. All right. I mean that is, and you can, or you can focus on machine learning. There's all kinds of different flavors going on out there. Two of our two trends down as cognitive that's probably could be in there's a great area between those. You can even consider that there. But anyway, more consideration is the data lake. Data lake is now part of your architectural toolkit. All right, so the trend next year isn't oh people will use data lakes that's gone and data lakes are here. All right, but they're going to evolve much the way the data warehouse evolved. When you had, you know, data warehouses, then you had data marks. We have data lakes. Now we have these regions or zones within the data lakes. All right, you're already seeing that we had that is data warehouses and we had data warehouse to auto and corporate information factories, you know, all those kinds of things. All this is going to happen. The tools to manage this are going to happen. And you probably in 18, even if you've already done data lake and have something up and running. Things are going to be happening so fast that I would say maybe by second or by third or fourth quarter. If you've got a day like it's been up and running for a couple of years, you're going to start to consider a generation to of your data lake. Things are happening that fast out there. Cognitive learning will enter the vocabulary of enterprise architects. So in other words, are we willing to have algorithms. Look at things and recommend new things to us and things like pattern recognition and facial recognition and things like that. Are we willing to take that dive into pushing some control off to the algorithm. And you're going to see, yeah, yeah, we're going to do that, you know, we're going to do that. And of course, you know, as a data governance person or analytics person, whatever, what's that mean, what can we do with it. You've got to understand that stuff. Overall technologies in general, we've experienced Kelly and I have joked about this. And amongst our peers, every time you come out, turn around, there's a new name that sounds kind of silly, you know, like a splunk or a scoop and it's spelled funny and as missing vowels and stuff like that. But, and you feel like you need a scorecard to do this, but these are all various shades of things that are were added on to the old core Hadobe through the Adobe stuff to do different things. Well, you're going to start to see things get more general and things become more less purpose built and more general, more mainstream, much like our modern DBMS. Prior to the do technologies were, and you're starting going to start to see more yet and inevitably with all the vendors entering here you're going to see consolidation and things going and things merging and all those kinds of things to so there's a lot. That's like, well, two pages for technology. That's the only thing today we've had two pages on, right? Absolutely. Yeah, I mean, Hadoop used to be the answer to every big data question, right? And, you know, there were so many add-ons required, it became really confusing costly to manage. You know, I think this concept of, you know, both the combination of the database management tools evolving and technology swinging back to being more general is those providers of solutions, you know, technology solutions or combined technologies that are coming into a solution will really start to gain traction as companies are wanting to simplify, you know, their cost for managing their infrastructure. I think some of these are very related for sure. Yeah, so then, you know, what are we seeing around writing some of these trends? John, how do folks take advantage of it? Well, the first one, and I'm doing this now, working with a good friend here in St. Louis, a good friend of our firm who's into this and he is showing us stuff with graphs that's pretty, pretty incredible. I think the key knowledge map for your organization is going to be vital. So, if you don't know about graph technology, you've got to get yourself educated on it. I would say of all the things we've talked about, this was a mandate, if I were a CIO or a CDO, I would be spinning up capability in this area as an R&D basis before anything else, honestly. It's just that, you know, it does really cool things and it's going to be a vital element of metadata tools. You've got vendors out there that are starting to literally gut their tool set and take out the relational engine and the relational metadata models and insert graph engines and graph aware metadata models. That's how important that this is going to be. So you've got to get onto it. The next thing in terms of all the speed and everything, I mean, look, machines are going to run faster and faster and faster, but it's going to have all this volume. And I use the phrase drinking out of a fire hose and I use that with great deliberation. Everything's going to want to have to run faster and machines are going to be able to do it, but you still got to squeeze all these, all this data down into something that's meaningful to use. Edge computing is going to be significant. And if you're not sure what that is, it's going out to where, on the Internet of Things, going out to where you're getting the data collected and doing some data management all the way up in what you would call the data hinterland before it even gets to the core or lake or anything like that. So you're distributing your big data processing. And then, of course, the last thing I think we like to get, well, we have, what's not the last thing because we have two pages of technology. That's right. But the last thing on this page is the blockchain. That is one of those rare things, Kelly. I think that we have a solution looking for a problem, you know. And there's somebody in your organization who's really smart is going to look at that, understand it, say we can use it here, whether it's for compliance, whether it's for security, whether it's for privacy, whether it's for managing Internet of Things data. There are a lot of folks kicking around a lot of uses of this. So please get smarter on that. Absolutely. You know, we had a client that geared up for real time analytics. They got all everything they were supposed to get spark and all the tools to go with this. And then they discovered that, you know, be careful what you ask for my grandmother used to tell me that be careful what you ask for you might get it. And they got all set up and then here it comes, you know, 20 terabyte a day, boo, just coming in. No idea what to do with all of that. It couldn't even process it then. Then the other thing on the other side, a positive thing we had a client that was just started to dabble with things and had a sandbox and use some fairly primitive tools, but did use a Hadoop and a data lake type construct and started to run some AI type algorithms and found some profound high return reshaping of its business processes. And again, they, what they did was kind of what we're encouraging you to do here is spend some time thinking about what kind of solutions you can bring to bear on on your problems, but you're going to have to get smarter on them. Yeah, you know, just to piggyback on top of the first bullet point, I heard a presentation recently from a fellow named Tim Baker at Thompson Reuters about how they were leveraging graph databases and knowledge graphs to better understand their content repositories. So exactly what you're talking about here in terms of graph database technology to give us a much better view of metadata and give it to us in a way that's much more consumable and quickly consumable so that we truly understand what are the data elements, what are the relationships to other data elements, where are they in our organization, how are they consumed and a lot of information that's best represented via a graph. So anyway, just piggybacking off on that. So we do have a second page of trends. So let's jump to that second page of trends and we can talk a bit more about the AI and machine learning on this one as well. Oh, and I did forget to mention our friend here at St. Louis John Singer is his becoming a he has many. By the way, John has some stuff on on data diversity. Kind hosts, there you go. Tracking and monitoring about the data lake. Again, I'm not much of a sports fan but when I do sit and watch sports actually like to watch American baseball and which a lot of people things like watching paint dry. But I find it a good, a good afternoon to relax. And the reason I'm using a baseball analogy is in the 1980s, the coach of the St. Louis baseball Cardinals was Whitey Herzog. And he played a very active dynamic game and move people out and move the pitcher to right field and the right field to the pitcher and just and and it became a part of baseball lore that you didn't you had to run a scorecard or a tally sheet to know who was playing where when you watch this man coach a baseball team. And it reminds me a lot of data lake and big data technology. You have got to stay awake. If you go out for hot dog and come back, everybody's moved around the field. All right. You know, so you've got to be really on that. The other thing is kind of tied to the story I just told there is be serious about your late late latency requirements, you know, latency being the time from when you want to do something. Data is available to when you really want to use it and do something with it. There's everyone saying, yes, we're going to be real time. No idea really what that entails. All right. You know, do your business processes require to be that real time. Do you have the controls in place that will allow you to be real time. The last thing you want to do is make a really horrible decision faster than you've made a horrible decision ever before. Right. You don't you know that. So you might want to say, let's hold off on being aggressive on low latency stuff. Let's hold off on some other things as a way to do this. And then the last one was what does AI and machine learning really mean to you and define what you can do with it. And again, if you don't have a strategy or not so much a strategy, Kelly, I guess almost a position paper, right? Or a white paper on how to deal with these technologies, AI, big data, cognitive learning, machine learning, edge computing, you need to sit and think about this. This is one of these that we can very, very often. Two things happen. And if there's a third Kelly, just chime in. But right now two things happen. One, you go ahead and get stuff because you have a vague notion that you're going to do it. And then when you buy all the stuff and start to do it, you realize that that notion was too vague, and you are not at all ready to exploit that technology. The second thing is you don't consciously think about it. You end up kicking the can down the road unintentionally. And so when you do need the technology, you are now a laggard. Everyone else in the neighborhood has it. And you're scrambling to get it in. And of course the price has gone up because the demand's gone up and you can't find enough people because everyone's hired all the people you could have hired. So with all of these, you've got to get smarter and get yourself prepared. And considering these things proactively, even if the answer is to wait, or the answer is to accelerate, you've got to talk about this stuff now. Absolutely. Yes, absolutely. I think that there's, we're seeing some practical applications of artificial intelligence via robotic process automation or RPA in which companies are automating some really time consuming processes. That need a level of smarts that can be done via artificial intelligence, but at the same time are repetitive enough that it is easily tested and implementable without a massive infrastructure. And then I think the tools vendors legitimately are trying to make this easier for people. And if we're looking at some of the applications of things like AI, machine learning, cognitive learning, Microsoft's coming out with these cognitive toolkits. You know, TensorFlow, which is an open source neural networks and deep neural networks capability, you know, et cetera. So the vendors are also helping people make it easier to test and see, is this something that we want to do, or is this something that we're going to table for later. So anyway, I know that we're talking quite a bit. We've got two more topics to cover. So let's go ahead and jump into really the implications of not just the people and the technology, but the actual data. What are we seeing on the actual data? This one is fun because everyone wants to monetize our data in some way. So many things are point to a trend that more and more attempts are going to be made to actually make money or save considerable money off your data. Our friend Doug Laney at Gartner just wrote a book on infonomics and he talks about monetizing data and that's what the book's about. And it is sold more than any other book in our area of thought than you can possibly imagine. It's, you know, you're going to have more data sources. Everyone is going to try to make a few dollars or save a few dollars off their data. And we'll talk about how to deal with that. Then we have the regulations. Those are not going to go away. We're not going to see a decrease in regulations. You're going to see an increase regardless of what political wins are blowing whichever way because of the pervasiveness of personal data flying around. You're going to see more and more and more and more. And it's going to require more rigorous understanding of your data landscape. We're starting to use the word smart data instead of big data. And that means make your big data actionable. We'll be smarter. Don't just do a data lake architecture because everyone else is doing architecture that's appropriate for you. This goes back to some of the things we said in some of our first events this year. Make what you have actionable. You're going to see more and more demand on the sandboxes and the data scientists. Thank you. That was really cool. That was a nice report. We got a couple of good nuggets now. What can I have that I can use day in and day out? And then the internet thing, of course, more and more data sources, more and more data volumes. Some of it's probably silly. I don't want my refrigerator to talk to the internet, but it's going to. And you're going to have to figure a way to deal with that. Absolutely. I would agree. And I think if we, from a data sources perspective, there are so many more specialized data sources now. You can buy a highly specific data source to solve any sort or many types of questions that you have within your organization. And I think based on the volume of new entrants into the data services market, the big players are starting to feel some pressure. I had a client say to me yesterday, look, these guys are really putting pressure on us and they're becoming much more demanding and they're expecting us to manage their data and essentially put together processes that enable them to audit us more easily and charge us more money. They're becoming aggressive. So I do think that as we see more companies monetizing their data, selling their data, that this is going to continue to happen until we hit saturation point. And then, of course, it'll go back the other direction. All right. So what do we want to do about it? Well, if you're going to monetize data and yes, either selling it or using another because another depth is monetizing data is directly using it to manage and lower costs. Okay. So whatever when you're going to do you are in essence making data a product. So product management practices really, really important. That means data quality. You're never going to avoid that. Quit trying to just going to be there. That means having a product manager for your new data source. We've got several clients that have found data they can monetize and are selling it into their market and into their customer base. And they're finding, okay, yeah, they're interested in it, but you know what, you got to have a support line. You have to have a release strategy. You have to have a good pricing card. You know, you've got to have salespeople that understand what they're doing. I mean, you know, it basically productionize this. You need to have really good governance and really clear policies over your buying of the data as Kelly was just inferring to the data vendors are starting to get a bit belligerent about some things. I don't think that will last a long time, like Kelly does, but I, there's going to be a pair of times as a we've got this data and it doesn't matter what you think, you know, take it or leave it. So, if you've got cloud storage agreements, moving on in the next year or they're coming up for renewal, time to take a hard look at them. If you're going to have regulation, you need to know your data landscape. Now this ties back to our tech trend of graph databases. You have got to have metadata. You have got to have a data landscape, which is where is it, where is it sitting, how much is it, who put it there and who looked at it. You have got to have that. Without that, you are, you risk, this is a bit bold, Kelly, and you know, dampen me down if I have to, but there's some organizations without this risk being coming non-competitive in their markets. And then be smart data. And, you know, they said, oh, we're going to do big data, we're going to do big data. Use the words if that's what the boss wants to hear, but really be smart. All right. What is your data strategy? What is your data portfolio to look like? And what is your data architecture? And make sure it matches what your data strategy is, which reflects, of course, what your business strategy. Too many CIOs, my story for this one is pretty broad. Too many CIOs are saying we're going to go in this direction with data because they have this fuzzy image in their head of doing really cool things with the data. It's got to be tighter than that. It's got to be a lot tighter than that. I'll kick that back to you, Kelly, for some additional thoughts. Yeah, absolutely. And, you know, when we're thinking about reviewing things like cloud storage agreement, you know, that also comes into play with some of the regulatory environments in the sense that the general data protection regulation does apply to, you know, where you're storing your data and you need to understand what those storage providers are promising you around the data. And so I think thinking about data not just as a repository, but we need to be looking at contracts. We need to be looking at agreements. We need to be looking at what we have, what we are authorized to use that data for, what our vendors are doing for us within that agreement. And so that's a new process I think that's happening more regularly now within organizations. Speaking of process, let's go to our last and final section where we talk about that kind of supporting practice or structure for all things that we do to make it sustainable. So, John, what are some of the trends that you're seeing from a process perspective? Well, digital transformation, that's almost buzzwordy, but it's going to keep happening in in 18 being data driven, monetizing data, moving more into mobile expressions of your organization, pushing interactions deeper and deeper into mobile type things. Understanding what's going on at there via social media, all of these types of things becoming less of an, it's not that they're going to grow, we know that it's going to become less of an option because just everyone else is doing that. Of course that points to other types of innovation. Now, a lot of innovation has been the one off data scientist kind of yahoo moment, right? But you're going to start to see because this is what really keeps organizations moving forward. It's not just the innovative one bright point of life, but it's that ongoing efficiency and sustainability of new product or a new program or a new service. That's really, really cool. So start to look for innovation being a little bit more on that sustainable efficiency thing. Data monetization needs new processes because you're going to have new data products and data-centric programs, which are going to have different goals and objectives. So all of that's going to drive new processes. Just accept it, folks. You can't do business as usual with data the way you've done business as usual. You've got to change some things. And lastly, good old data governance. Day without data governance is like a day without sunshine. You, it is a requirement. Please just, if there's any executives listening to this, just accept it. Quit asking people to do ROIs. All right, really, I'm sorry. I'm getting a little bit on a soapbox here, but it is a requirement. It's no different than, you know, you're doing all this. You're investing all this money. You're going to try to get all this out of that data. You want to be data-driven. You want all this stuff, but you want no controls. You want no accountability to find. You want no rules. You know, try doing that with building a new factory that uses a few chemicals and a complex supply chain. See what you can get away with that same mindset. Day to day governance is required, folks. Whatever you call it, call it whatever you want to. But having the rules of engagement with your data is really, really important. Not going away. All right. Anything else, Kelly, then we'll just start to wrap up. No, I think that this ties – this really ties it all together. And all of these different processes are linked together to make sure that you are becoming data-driven. And I love this concept of being data-driven. It's becoming so much more pervasive in our culture and in our vocabulary. In fact, yesterday I was listening to a presentation of a kind of a business colleague of mine that runs less than $200 million alarm business here in the Bay Area. And he had a slide talking about data-driven. And I was like, oh, my gosh, I love it. And so this doesn't have anything to do with the size of your company or anything like that. The idea is that using what you have available to you from a data perspective will make you more competitive and more efficient. It will help you innovate. It will help you monetize. And then, of course, governance is really making sure that you're understanding it. So I do feel like all these process trends are really tied together. So how do we take advantage of them then? Let's go on to the wrapping up some of these slides. Yeah, because it's the holidays. I have to go shopping. No. Put data-driven into your annual planning. If those words are being kicked around, the way to get serious is say, you know, and everyone's doing it at this time of year anyway, pretty much, right, is what's it mean? How does that instantiate itself in our annual financial objectives or market objectives or strategies or initiatives we're planning, what we're budgeting for, what we're resourcing for? Put someone who really knows data into the planning process. Too many of us still react to budgets and annual plans. So the data governance people sit there, the door opens, out comes the big stack. Here's our annual plan and I'll tell us how governance is going to deal with it. That's actually kind of backwards. The governance people should be behind those doors telling you that, wow, that's not a really good idea to do. It's to fund two separate projects that deal with customer data. Why don't you make it one separate project, right? Be flexible with product procurement. Everyone out there probably pretty much talked to me, has a procurement department that can procure a certain way and you're going to get challenged on that. If it takes you 12 months to get a product bought and in place, it'll be obsolete by the time you're done. Kind of pass that off the line. You're going to have some flexibility with these products. Lastly, for a data person to say this, Kelly, I hope you understand it. For me to say this is almost painful, but start thinking about process design. I know when you're monetizing data, data governance, standing up all these new capabilities means new workflows and new workflows equals new process. And that means think about processes. Those of you that have thought, well, I never have to go back to Six Sigma or ISO 9000 or Lean, guess what? You've got to start to think about that stuff. And then lastly, governance has to be adapted to be a bit more agile. So one criticism we've noticed here that's valid and which is going to have to change next year and you're going to hear more and more of this out of the market is, you know, a quicker time to solution in the data governance area as well, just simply because things are changing so fast. So if you are thinking of taking three years to stand up a data governance program and nine months before anything visible is from a data governance is going to happen, you better rethink those plans. You're going to have to get a bit more iterative and start to carve it up into smaller pieces there and I'll turn that back over to Kelly on that one. Yeah, absolutely. And I think that, you know, best the way to take advantage of this is to really integrate the data requirements with the process requirements so that you're leveraging your technology and that you have this people plan that supports all of it. And all of this needs to be supporting a dynamic environment. Your businesses are dynamic. The industry is dynamic. And so thinking about taking a long time to start governing data or to start ensuring there's data understanding means that you're not going to be supporting the needs of your enterprise because even some of those traditionally stayed types of industries are becoming are seeing the need to be competitive around data, manufacturing steel companies, etc. So with that, let's just summarize our key takeaways. And if we've got any questions will probably still have a couple of minutes left. Yeah, we've got we've got one or two that have come in and I've already started to form some things on those. So take away that you do. Are you just going to do the TTA. I'll do that. Yeah, yeah. Absolutely. And you can monitor questions. So people issues are really still the main challenge in the sense that if we are going to create a data driven culture, then we need to make sure that we are helping our people understand what it means to be data driven. We're providing the correct training, we're understanding how their role fits into how they create consume data. So the people issues around this are still really the biggest challenge and the biggest opportunity. The diagram on the right hand side is a great way to think about this in the sense that the severity of the challenges versus our ability to rise to the challenge. It's hard to rise to people challenges sometimes. There's personalities, there's relationships, there's commitment. So it's both the biggest challenge and the biggest opportunity. Technology, this, you know, as usual, the technology industry is extraordinarily dynamic and changing, which means that you will then need to be able to respond accordingly and adapt your infrastructure, your architecture, etc. In a way that isn't chaotic, but is able to be flexible and where you can respond quickly to those new demands in the dynamic environment. And it's, you know, we talked a lot about business and IT collaboration, but it's kind of internal versus external as well in the sense that you're seeing collaboration also amongst industry and that sort of thing. And so internal processes, external processes, products that we're using internally that we're sharing amongst industry peers that will continue to evolve. And then of course the data that comes out of those shared processes and shared systems are now becoming monetized. So that's going to continue to evolve. And this drives towards recognizing your true requirements for speed, latency, security and governance around data is really going to drive the business outcomes that you're looking for questions. Yeah. Okay. I've got it. I've got them lined up here. We've got a handful. We're going to be able to get to just some of them. I'll take the first one here. Could you define graph database technology in a couple of sentences? Well, the official definition of the graph database is a database using graph structures for semantic queries. Now, first of all, that's a cheeseburger definition. And second of all, it doesn't really help anybody because graph databases are kind of a whole new way of thinking about data if you're embedded in the relational world. So real short, graph databases are a particular type of structure and database mathematics that will allow n number of dimensions on a data element. Relational is a row and a column. Graph database is almost an infinite number of dimensions that are called tuples. Okay. Graph databases came out of the semantic web world. The aforementioned guy, John Singer, has a lot of good articles on graph on data versity. I would encourage you to go there. If you want to get a product sniff, there's an open source product called Neo4j. You can download kind of a play version of that and have some fun with it. And that'll be our quick answer to that. Next one, Kelly, I'll let you take a shot at it. But you and I both could just have this stuff. This is a great one. We get this one all the time. The governance initiatives are often considered exercises in fatigue management. I like that. If data is supposed to always be owned by the business but after time they stop attending committee meetings, jeez, does that happen? Great. And we are right at the top of the hour. So if we can just wrap it up really quick with that very quick answer. All right, Kelly, 30 seconds for Kelly. Yes, I would say focus on what the business needs and requires from their data to increase their revenue, reduce their costs and reduce their risk because they will not get fatigued if they're seeing values from it. And that helps to answer the next question, which is recommendations related to data governance, find the business purpose and execute according to what's going to improve your business outcomes. Awesome. Sorry, guys, we are right at the top of the hour. So we've been on a roll. We were on a roll. We get to talk about analytics driven culture next month. I'll get the questions over to you so we can get that in the follow-up email, which will go out by end of day Monday for this webinar. And I hope you can join us next year. As Kelly and John just mentioned, the next one gets keys to creating analytics driven culture. It's a great presentation and thanks to our attendees for being so engaged in asking such great questions. Again, I'll get those over to John and Kelly to get in the follow-up email. And I hope everyone has a great day. Thank you so much. Thank you all. Happy holidays.