 Hello and welcome. My name is Tendley Proudfoot and I'm one of the Digital Production Assistants with DataVercity stepping in for our Chief Digital Manager, Shannon Kemp. Thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will be joined by a special guest, Nigel Turner, to present data governance combining data management with organizational change. A couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. And we very much encourage you to chat with us and each other throughout the webinar. To do so, click the chat icon on the very bottom middle of your screen to activate that feature. For questions, we will be collecting them via the Q&A section. Or if you would like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DA Strategies. As always, we will send a follow-up email within two business days containing links to the recording of this session and additional information requested throughout the webinar. Now let me introduce our speakers for today, Nigel Turner and Donna Burbank. Nigel has worked in information management and related areas for over 20 years. This experience has embraced data governance, information strategy, data quality, data governance, master data management and business intelligence. He is a great advocate for keeping information management as simple and business focused as possible and feels that a key role of information management professionals is to help business people relate information management to real business benefits. And now let me introduce the speaker of the series, Donna Burbank, a recognized industry expert in information management with over 20 years of experience having organizations enrich their business opportunities through data and information. She is currently the managing director of Global Data Strategy Limited, where she assists organizations around the globe in driving value from their data. She has worked with dozens of Fortune 500 companies worldwide in America, Europe, Asia and Africa and speaks regularly at industry conferences. And with that, let me give the floor to Donna to get today's webinar started. Hello and welcome. Thank you. And thanks for all who have joined. I've seen some familiar names and I appreciate a lot of you folks do join every month and that's that's well appreciated. So if you're not familiar with our series, the good news is that all of the past past webinars are on demand. So you'll see that we've had some earlier in the year on data architecture and data strategy. We had a case study last month, which is one that both Nigel and I worked together on about data modeling at the Environment Agency of England. And for those of you who don't know Nigel, he is my partner in climate global data strategy and he runs the practice over in our European side of the pond. And we are joining forces today to talk about what is near and dear, particularly the Nigel's heart is data governance, not that I'm not a fan. That's particularly Nigel's claim to fame and combining that I think uniquely with the focus on organizational change and how our data architecture can support that. So you've seen the abstract track when you registered, but just to kind of talk again on today's topic, we're talking about data governance, obviously, but really the key and I think I'll attend me mentioned it and when he introduced both Nigel and myself is that one of our differentiators that we are passionate about is how you really make change in an organization. And more importantly, how you drive business value in an organization. And we found particularly in modern times where so many companies want to be data driven, it's governance that is really that vehicle that drives change and business value and really true strategy. And so, you know, I think a lot of people do have the old school at times opinion that data governance is just, you know, management and telling people what to do and making sure your data is right, which is true. But really when that is done right, that is what helps drive organizational change and business success. So that's really what we're going to start to talk about today. And we'll align two things, org change and organizational structure and data governance and data architecture and how an architecture can really support that. So without further ado, I'm going to pass it over for Nigel to talk a little bit more about how we kind of see this data driven business and data governance supporting it. Nigel. Okay, yeah, thanks, Donna. And good morning, good afternoon, good evening to everybody, depending on where you're bringing in from. I suppose it's one of, you know, when you pick up any sort of article these days on the business world, it's become a bit of a cliche to say that we're in an age of sort of the data driven digital business, but because it's a cliche doesn't mean it's not true. And many organizations are trying to sort of move in this direction. So clearly, you know, in this type of new world that we're in, data is becoming increasingly more important. And I quite like a little Gartner summary on the left hand side of this slide where, you know, traditionally, when people talked about business, they talked about the PPT triangle, people processing technology. And Gartner I think I've quite rightly recognized that in the digital world where data now becomes a key currency of the business and the key data asset, a key asset of the business, the PPT in itself is not enough that you need actively to manage data as well, if you're going to use data to drive your business forward. And on the right hand side, I won't read through all of those because I think they're fairly obvious, but sometimes people need to be reminded that companies quite happily invest in and run human resources HR departments to manage people and to support line managers. Everybody pretty much every organization has some sort of an IT function. Many organizations have process functions as well, because they recognize if you're going to improve the efficiency of processes, you need people to drive that forward. And I think data is becoming widely recognized now as being exactly the same as all of those, the data doesn't improve by itself. That if you are a company that relies heavily on data, and after all, what company doesn't these days, then you need to actively manage your data in order to improve it. And that's basically what I think the essence of data governance is all about. It's about the business and IT working together in order to develop that information asset. And if you talk about, you know, where does Dharma, for example, the data management association, regard the governance these days? Well, it's one of the 11 data management disciplines. But if you notice there's something a little bit different about data governance, which is that it's at the core, the hub, if you like, of the Dharma wheel of the 11 data management disciplines. And I think that's because what Dharma says, and again, something I strongly endorse, and I know Donna would, is that all the other data management disciplines to some extent depend on governance. And just to give you a couple of examples, if you take something like data quality, for example, then which data you need to address, which data you need to improve, what sort of rules do you put around that data can only really be decided by people in the business who are accountable and responsible for that data and manage that data on behalf of the business. And that's pretty much what governance is all about. So governance, I think, for any data-driven digital business is absolutely key. And I think with the way we look at data management capabilities as well, and I'm sure if you've been on these webinar series with Donna before, you've seen this diagram a few times, then we also see, I think, data governance as being very much a bridge between what the business is trying to achieve, what its data needs are, and then, if you like, the more technical disciplines of data management that sit from level three to level five down, all of which must be part of the solution for most organisations in terms of delivering the data required by a data strategy to support the business strategy. And governance is very much about linking those two things together. So the people who are operating, excuse me, in the data governance space, need to understand the way the business is going, but at the same time have at least some understanding of those technical disciplines in order to recognise which of those things need to be developed to support data management. So governance for us is very much about change and it's very much about improvement. And I've looked at lots of data governance definitions you may have as well. If you Google data governance, you'll see dozens and dozens of definitions. My criticism of many of them is that they're all complicated, and as Teni said earlier, I don't like over-complicating things. And I think a lot of them as well focus far too much on this idea of data governance being somehow about control, about stopping people doing things with data that they shouldn't be doing. Now, I'm not saying that's not important, but I think that if you focus your data governance efforts predominantly on stopping things happening, that's the wrong emphasis for governance. And the definition that we use is the one you see here in the big blue box. And I think it's got sort of three key elements to it. The first is that because data in a digital business is increasingly an important business asset, it only makes sense for that asset to be owned by the business. There are still many organizations out there that see data as IT's problem, and they're there becoming rarer, I think, but unfortunately that culture, if you like, when data is wrong, it's all IT's fault, is still prevalent. But it's the business that ultimately creates the data, and it's the business that consumes the data, and therefore the business must be at the vanguard, if you like, of any governance process. And I think the other thing about governance is that it's a BAU activity. It's not a project with an end date, although to start a data governance program, if you're in an organization that doesn't have one, then you need to treat that initiation phase, if you like, as a project with some deliverables and some timescale. Once that project comes to an end, and at that point data governance is usual activity, just HR, BAU function is working as the... The other thing I think I've stressed is that governance is all about demonstrating how the business can be made better through better data. And so the focus on any effective governance program has to be on improvement. And I think I've seen, again, another failing I've seen with some governance initiatives, is that it's all about monitoring data. So basically, you know, the governance team compiles a report of data quality, for example, once a month, and tells the business, you know, our date is as bad this month as it was last month, and I'll send you another report next month which says it's as bad again. There's no point in doing that, there's no point in measuring something unless that's the basis of an improvement that you want to make. And so the implication of that as well, the other key word in that definition is benefit. So the benefits of data governance can't be intangible. Some of them might be, but if governance is really to work, it must be real and it must be measurable. And all the stakeholders of data, including the data creators, the data consumers, the people who amend and adapt the data, should recognize that it brings benefits to all of them, as well as benefits to the business as a whole. And that's what the way the ideal world should work, but in our experience, and also talking to many other data professionals, I think the norm in some organizations at least, is that data governance is still a bit of a mystery to them. They don't really see it as a core business capability. And therefore, when they come across and I'm using bad data again as an example, but that's not the price, the only focus of data governance, but it very often is the key driver, is our data is not good enough to be a digital business. What do we need to do to improve it? And if you don't have formal governance and formal processes and formal structures for actually improving data, then we all know what tends to happen is that when data is bad, the reactions to that always tend to be reactive. Let's wait until we fit a problem, then we'll put a team together and we'll do some panicking and we'll fix it. It's often done in an ad hoc way so that everybody that comes across a data problem develops their own methods and their own ways of doing things, which can never be shared or reused anywhere else. Very often, as well in those organizations, data cleanse and data improvement is a manual process. So they download a load of data to a CSV file, you can get it on a spreadsheet and eyeball it line by line. And I think the other problem is as well then that there's no real ownership of the problem and I really like that quote that Donna and I got from a senior client of ours earlier this year and it said, you know, we are told we're all responsible for data but if everybody's responsible then in reality no one's responsible and nothing ever changes. So making key people take prime responsibility for data is vital and that's a key characteristic of any data governance program. So how do you make sure then that if you try and introduce data governance into an organization then you can actually make it successful and these are perhaps eight of the key lessons that we've learned during our time with clients and engaging with many other people as well in the data industry. I think the first thing is that, you know, you need a clear vision of what good looks like in your organization and if you're going to do a governance program then basically, you know, it's again, I'm sorry it used a bit of a cliche but it's true, governance is a journey. You know, if you've got outstanding data problems that you've had for many, many years and people are fixing those problems in the ways I described, you have to take people on a new journey to a new destination and you can only really do that if you have a clear view in your own mind as to what that destination would be like when you get there. That's really important I think to paint that picture to excite people to want to get involved in any governance program that you're running and the second thing as well of course is that but when you set out on that journey you need to be realistic. So if you can only walk at the moment then to say that we're going to get from here to Rio de Janeiro, I mean Wales at the moment by the way, we're going to get from Wales to Rio de Janeiro in the next six months is probably not likely particularly if we're an organization which isn't very mature when it comes to data management and where sort of the best practices of data management aren't yet in place. So when you set your vision also be clear about what your expectations are then you need to align what you do with the benefits to the business as in the definition I've just given. You could find bad data everywhere in your organization, you could spend time improving it but that's unrealistic and it's also unproductive. So what you need to do is identify which which data you need to do something about and improve or enhance to benefit the business most and focus your efforts on that and that implies as well therefore you need to be you need to prioritize the data that really matters to your organization and again that's why governance is essential because it's only the business who can really do that you can't leave that to IT because very often IT don't really know which data is the most important that should be driven by the business and not by IT. The other thing of course as well that I see go wrong sometimes is equip people to succeed. I'm a great fan of Game of Thrones and those of you who are fellow fans will know that the imminent battle with the white walkers the zombie soldiers of Westeros is about to happen and if you send a load of peasants out into the field and arms they're going to get massacred and it's the same if you if you if you put a governance program in place I'll ask business people to step up to take the lead you've got to equip them to succeed and that means you've got to train them they've got to be educated in the best practices and they have to have the right tools and equipment to enable them to succeed. Also you can learn from best practice I mean don't reinvent wheels when it comes to data governance because thousands of organizations globally and I were embracing data governance and there's an awful lot of best practice out there so learn from it rather than start from scratch. Something Don Rhino will touch on later also the other thing is that you can't get a governance solution out of a box impose it on your organization and say that's it job done because every organization is unique every organization has a different journey as different expectations has a different priority when it comes to data and therefore one size does not fit on in data governance but you can use a framework in order to help you do that and what I was going to do later is come back and talk a little bit more about the sort of framework that we would recommend for governance. So what I'll do now is just come back to you Donald to talk a little bit about how governance fits into architecture more generally. Sure yeah so when Nigel mentioned the framework there's sort of several meanings to that word and the framework of that house really is the architecture of the house. So there's sort of the governance framework that we'll talk about and a key part of that for any framework is the core architecture which you know is near and dear to my heart. So when I think of data architecture I see that as this part especially in the context of governance is part of a wider enterprise architecture because an enterprise architecture really starts to get to the people the process and how data is used in an organization and so we when we ever do data governance we often start with I'll show some of the artifacts that you've seen in some of our other webinars things like a motivation model as Nigel just mentioned getting to the root cause of the purpose and the what the desired outcome is for governance is probably the most important thing you could do even sort of look at data quality but what's the so what as we say at the end what are those business drivers and then when you're we'll talk a lot more about this as we go trying to integrate governance into existing business process and your existing data governance your existing business organization because you want to make this a business as usual activity as Nigel mentioned that this shouldn't be necessarily a separate thing you have a finance department that manages money it's not actually it's part of everyone's day jobs to make sure that your budget is aligned right so as is with data it should just be part of everyone's daily you know operations and then of course the data and how you map the data to those processes so if you've joined some of our previous webinars you'll see that we often show this it's just a simplistic example of what we call a motivation model and really this is a way to get to the sort of touchy feely part of things or the people side of things but and if you're an architect on the call instead of an architected way and so that's one reason I like it it sort of helps make people make sense a bit which can also be complicated and I would find having done both of us on the call have done too many of these don't want to count often what makes a governance problem project go wrong is it's not necessarily technology but the people and the different motivations and the different politics or the different missed alignment um again generally not out of malice but everyone has their own motivations so getting that on the table soon um and making that clear really helps align things we've also had some very positive results a it's a simple one pager you know I'm a fan of that keep it simple you know building this could can take a lot of complex things and then sum it up what are we trying to do with governance we're trying to get better accountability have better quality and have a culture of data um and it just makes that very clear also and if you're doing data governance right there will be some heated arguments because you're getting to the crux and is that is that one sort of healthy place where people can argue about data governance issues right or data governance I was joking with a friend the number of times I've had someone literally raised their voice to be in the past four weeks about their definition of customer with the right one I had to almost laugh because that happens at every organization until everybody gets in a room and battles it out a bit um that's where you see everyone else's perspective so it's sort of like in fighting within a family when there's a good culture that can work very very well but often when it does get heated it's very helpful to come back to this one pager and say why step back I'm not arguing with you because I'm a bad person I'm arguing with you because I'm trying to support our mission one of the ones that really hit home with me I'm working with a local hospital and they're trying to get a single view of provider in a master data management a governed master data management effort um and there was a debate of do we keep one of these elements and a lot of people didn't know what that element was and we're trying to keep it simple and a lot of majority of the people got up and voted let's just get rid of it and one gentleman stood up and said no this is the flag when we're trying to medevac a child who's on the brink of death to another hospital this flag lets us admit them very quickly and there's absolutely no way I'm going to take that off because someone's going to die that was maybe an extreme example but we all got quiet and that was such a very concrete way to realize yet this sounds like it's a master data management it'd be easier to drop a field but they're literally where it lives at stake in this case so in that case it was very easy to point back to the mission and say I'm arguing you'll do about this element because it has a business impact and that's where I've seen in a positive way a lot of aha moments have come out of why that person in the other you know department seems so annoying because they have a different destiny of customer well they're in marketing and you're in sales by definition those are different customers or they're in HR and their customers you so it's a great time to listen and kind of get to those motivations another thing that is helpful especially when you're trying to A organize data and then B create a governance framework and a governance organization structure so there's two sides to capabilities one is your governance and capabilities and also the capabilities of the organization itself so this is a nice way to kind of again a one pager business capability model for the enterprise architects in the room of what are the core things we need to do we're doing product development and marketing and sales and human resources and then where and this is just an overlay we often use where is customer data used across or where's product data used across this and then one of the ultimate debates and I will talk more about this in a later is you know how do you define stewardship and one of the other I've seen a few folks jump in in the comments yes the the age old was the customer definition which you know if you wonder why people think I joked about this with the client just the other day you think we wonder why they think the data that team is nerdy well we just spent three hours arguing over what a product was you know what a customer was but you know this is critical but often it seems very easy when we're creating stewardship to say well create a single owner of customer be done with it how hard is that there's one entity on the model well that might be true from your modeling perspective but not from a business but nobody owns customer right that or several people own customer which is the more correctly stated if you look at the customer journey that we'll talk about it that customer touches a lot of people or in the medical example that I gave you know does one person touch patient you know one person own patient in the organization I hope not I hope the person that checked me and in the admin office is just checking my insurance and not my you know heart rate I hope that's a nurse that does that and those are two different very different departments the admin and the in the nursing department so yes two people own patient entity and there's there's different stewardship levels so I think breaking down in terms of business capabilities and not dated capabilities and not data domains business what is the business doing and how is data supporting that business can be super helpful so I'll say it a lot Nigel will as well as just keeping it simple one of my customers had a great quote that I will unabashedly steal now when in doubt zoom out and it's when things get complex that's where these high level capability models data models process models you know motivation models come in handy it's just zooming out before you can then zoom in before we do a detailed physical data model or a detailed master data implementation on customer let's understand where data customers used and that's often where we see things go wrong we forgot a department or we didn't understand how someone else saw this data and that's where governance kicks in and your governance organization should probably have or people from each parts of these organizations that you've outlined so that's sort of where the touch points lie there is lots of touch points with technical data architecture when we start thinking of data and I found this sort of a helpful way this was something we had put together for an insurance company just trying to say that what are these tools these data tools they can be super helpful for governance the data model is a very simple one what are the even the core entities we need to govern is that the brokers is the claims the policies customers all of the above and how do they fit together which then links with process where is data touched in this process and again that's another and we'll talk about this later when you think of stewardship well there's the claims department that touches the policy there's also the underwriting department you know there's a lot of different departments that touch that same piece of information and you need to govern along all of those processes a high level data architecture diagram is another key aha moment I often do is a data flow diagram as well might be in that that whether it's the high level architecture and or often more helpful adding kind of the data flow of often data is incorrect because of the integration or things are missing I think I've said this in a webinar before but I just loved it so much we did a very short it was an agile sprint for governance the first implementation for a big retail company and we had the head of marketing say you know I never thought I'd use the word data flow diagrams in a sentence but I loved them that never had someone explain why my campaigns didn't work because the email campaign system the campaign system wasn't linked to the master data hub and so when people were updating their emails it wasn't getting to the campaign very obvious thing that no one had caught in that organization because no one had the time to zoom out so again that one and doubt zoom out can really have some high level impacts and these are great things to share did this that retail company share this in their first data they had data governance sprints for the one we talked about organization you can do that I've done that a lot if the company is agile make governance agile and we did sprints we picked different business pain points and did sprints with all of these architect all of these architecture diagrams we had a business data model process model architecture business rules quality all in a single one month sprint and four weeks because these things do take a little bit longer but they don't have to take a year so again that was a nice way to really get to the crux of issues in a single way business rules and policies something as simple as a glossary that's your classic what do we mean by company customer what are the policies around customer are there insurance rules around how you can issue insurance policies are there HIPAA regulations for healthcare etc and then a data quality dashboard and this I know is near and dear to Nigel's heart is this is such an easy way and he mentioned this earlier of it's great to have a dashboard to say if we've prioritized that email is critical to our business for customers how well is it even being populated how accurate is it etc etc but it's a great thing to monitor in each day the governance meeting so you can really keep track of how we're getting better it's really easier dashboard for the organization so then you can start to kind of get to the crux of using the data for business advantage so I mentioned process and I think there's a lot of alignment with business process and governance because as Nigel mentioned earlier you really want to make governance a business as usual activity or BAU and I always use the analysis the analogy of finance right and so the finance department is very much like the governance department or governance team and we often create words that can sound very academic to people like data stewards and data owners and data custodians well when you think of it you have the same roles within finance when I take a business trip and I have to put in my expense report and send it back I'm a data custodian or a data steward right I'm stewarding the finance information that I manage just like a data steward is doing that's just part of my day job is to take a business trip and fill in my expenses I don't think oh I'm doing finance governance I just am that's just part of my job and so many people across the organization you know often and I know my colleague Bob Siner that often speaks at the University says this a lot as well and I agree with him of you know data stewards aren't made they're found of this probably people that are doing this every day in the organization that need to sort of be highlighted and promoted and given a voice to make change and make sure that what they're doing aligns with the bigger picture so probably everyone on this call is a data stewarder just getting our data owner in some sense right so that is was where these business process models come in of in the different processes you know I'm doing supply chain accounting am I a data stewarder you certainly are because you're you're touching price you're touching you know different pieces of the organization so that often helps and I've used these when we train data stewards of saying this is your day job and this is where data this is the data you need to be responsible for as some of this some of your day job so one of the tools I have been using more and more and more to great success is in a way it's the modern version or the hipster version of the in my mind of the process model is this idea of a customer journey map so anyone who is doing marketing or when we talk about becoming a digital organization often starts with a customer journey map and this really is basically a process model in a way I know it's slightly different before anyone corrects me on the call but from the customer's perspective and this is another way like that motivation model that I mentioned it's really to take people people's own personalities out of the equation and bring it back to the task at hand so if anyone was at the EDW enterprise data world conference in Boston you might have heard our customer Arizona State speak and they built a student journey map very similar but from the student's perspective and they like any organization had a lot of the competing priorities and they had a lot of different teams looking at this they did a data model on the customer journey map and it was the app dev team doing mobile applications it was the data warehousing team it was the the finance team the consumer services all of these different groups as you can imagine with different priorities and different goals to try out their development and what it was ahead of the mobile app development said you know I'd never seen data like this or my applications from the student's perspective I never really thought of the impact of when we're sending out a campaign how many times that student is touched each day with a web campaign or something like that and so doing this from the customer journey is another great way a to understand how data can be governed across the journey how it can be better leveraged and used for business advantage and it also helps break down any barriers across teams again I'm not arguing with you fellow data owner about what a definition of customer is I'm just talking about the definition of customer when they're at the discovery phase and all we have is an IP address so my primary key for customer right there is IP address I don't even know their name so you know it's a whole different type of customer right so these are very helpful way to basically A see where data is used kind of understand how governance can kind of flow in the organization kind of break down some of the barriers so these are just a few of the many tools we use for architecture there's many more and sort of different tool kits but I hopefully that kind of gives you a sense of some of these maybe traditional or maybe outside your toolkit you're using you know it can really help prioritize use and make real to your organization what's going to make make sense to you instead of implementing data governance so that's one part of the framework I'm going to pass it over to Nigel to talk about how some of these tools can tie in to the other part of the framework which is organization and people in process Nigel okay thanks Donna basically I think earlier I mentioned that we think that adopting a framework is very important if you're going to embark on a data governance program this is the very high level version of the one that we use it's sort of derived from industry best practice but we've adopted to our own particular needs based on the sort of feedback and our experience of our clients and what they actually need and again in the attempt to keep it simple we sort of say that in order to get data governance up and running you need to think about six key core capabilities if you like that governance needs to embrace so I think the point about this is that governance needs to be holistic so if you focus too much on just getting some people in place to do something you focus too much on the tools you focus too much simply on the processes and workflows you actually need to do all of this to a greater or lesser extent in order to make governance work and it's you know we call it the governance house and I mean the business goals and objectives Donna's already talked about that really comes from the activities that are involved in producing the motivation model and the capability model that Donna talked about the data issues and challenges they also come from these things but of course predominantly we would get those issues from the stakeholders that we talk to and actually talking identifying and then talking to the key stakeholders in the governance program and those stakeholders by the way should range all the way from pretty senior people in the company ideally the CEO down but you should also talk to some people on the ground because I think we all know in many organizations what actually happens at ground level isn't really always known to the people at the top and we've had some quite interesting revelations from people who work with the day to day to day who say actually no it doesn't work like that this is what we really do so understanding if you like across the layers of the organization is really important once you've identified those two things you can feed those into the roof of the house this is a strange house because you sort of build it from the roof down rather than the foundations up and then you can develop a sort of vision and strategy and vision refers back to what I talked about earlier is you know what would good look like what do we want this governance program to deliver what sort of tangible things will people see when we've done it that they could they don't see at the moment and the strategy is really about and how are we going to get there and I suppose the big decision that is often made when you embark on a governance program is are we going to big bang this so are we going to sort of do this everywhere so we appoint leaders for whether the data owners or data stewards across the business and we get them to look at starting to improve the data based on the challenges and the issues that they've got and that's certainly one way of doing it another way of doing it is you say we've really got a big problem in one part of the business let's throw all our energy and all our resources into that pilot this our governance program there prove that it works learn the lessons and then roll it out more widely to the rest of the organization and again both of those are very valid ways of implementing governance it really depends on your organization when I did this in a company I used to work for we piloted it simply because we came across a stakeholder that was unbelievably enthusiastic gave us his full support I really wanted something to happen quickly in his area so it was looking a gift horse in the mouth if we turned him down so by doing that we were able to build a very effective use case of the successes that we'd achieved and then use those to sell governance to the rest of the organization so when you come across barriers that is one good way of overcoming it find a friendly stakeholder work with them develop a use case and then sell that use case to the rest of the business so vision and strategy is all about that then of course you talk about you know what sort of organization do we need to put in place what sort of roles do we need to create and what sort of skills will the people need who fill those roles I talked earlier about not sending people naked into the battlefield then you've got to think about what processes and workflows do you need so for example how do we know when data goes wrong do we have a workflow to inform the relevant person that there's an issue and that they need to investigate and look at that issue then of course you need to think about the data itself as Don has already mentioned you know what data is it that we want to manage through governance what are the KPI's that we want to set for that data and how do we measure whether we're improving or not and then culture and comms I mean Don has already said this anyway a governance program crucially has to embrace everybody across the organization but also sometimes people outside the organization you know a couple of companies we've worked in we've encouraged them to develop data SLAs with some of their suppliers because a lot of the data problems they were experiencing were because the data from their suppliers simply wasn't good enough and was involving a lot of work to fix it and to try and clean it up to make it fit for purpose and then when you've thought about those things and what sort of capabilities you need you can then think about the tools and technology that you need to actually underpin those things so what tools do you need for to manage issues for example do you need to create an issues log do you need to create a data glossary do you need some data quality profiling and data quality re-engineering tools to help you to help you drive up the quality of data etc etc so all those things are key things that you need to think about but I think the key message is that if you just buy some tools it's going to fail you've got to have those other components in place first and the reason we adopt this is we think there are lots of benefits of applying a framework like this Don has already I think hammered home the point that you know you can use this end to align high priority business needs and the key data that you're looking at I mentioned earlier that it needs to be holistic and of course as well it sort of helps you if you use this to assess where the an organisation currently is there may be parts of the organisation and some of these components are already in place and therefore you simply say you've got that already let's use that in perhaps a slightly different way or let's use it as it is and then in other parts you might get some components are partly in place so maybe as I mentioned earlier they do a data quality dashboard the problem is they don't have people then to take the dashboard output and look at how data can be improved with to improve the score of some of the key data items within the dashboard and then there'll be other areas where basically nothing is in place and so you need a whole new work stream of activity in order to make things happen a good example of that we came across for example could be training all the data inputters in better data practices so that they don't make basic elementary mistakes when they input the data so if you do this you've got your baseline so you know where your journey's starting from and it also then helps you to define your realistic target so I talked about earlier and to say this is where we are today how realistically how far can we get in three months in six months or in the year's time and I think the advantage as well of the framework is that it's structured yes but it does therefore allow that difference that every organization is unique and therefore the emphasis and the focus you put on particular activities crucially depends upon the business drivers and the needs of that of particular organization so how do you sort of come up with a framework well we've got a whole series of questions that we ask and these are just a very small subset of questions and I certainly you'd be pleased to know don't intend to identify to read through all of those you can read them for yourselves and you know just a few key questions if you for example you know things like you know vision and strategy you know how does your organization rely on data today and what is likely to change in the future so if you've got a business for instance that's currently B to C but they want to start selling B to B do they have the data of the businesses that they need to try and sell to so that it helps to generate the right discussion really and also the key question there is what impact the data problems currently having on your organization then you talk about organization and people you know identifying who creates the data who consumes it and then asking the question is anyone accountable or responsible for this data or is that scenario I mentioned earlier what everybody is and therefore nobody is and then with processes and workflows you know are there ways of reporting data errors how do the business and IT work together to try and affect data improvements and to try and improve manage data in that way then data management the measures do you know what your key data is have you got data models as Donna said of the key data that can help you to identify the critical data culturally then you know does the scene do the senior managers of your company really understand what good data is and why it's important to them and I mentioned earlier are people being trained in it and then tools and technology is that a data architecture in place so is stuff just being created willy-nilly where is the data held physically so you need to go into some detail in this before you can actually put the governance in place but what I want to do now is because this that the main theme of this is about organization and organizational change I'll focus a little bit more about the organization and people bit and then what I've got here is simply an example organizational structure that could be applicable to a particular organization and we use the word example there very very carefully because this isn't not what we're saying needs is needed in every organization and we'll come back to that in a minute but typically in many organizations you see a governance structure that looks a bit like this so you've got the involvement I think the key point is that the executive leadership team or the board whatever you call them does need to be involved in this for it to work it has to be driven from the top and many of the successful governance programs I've seen is that there is an executive sponsor for your data governance program within the executive leadership team or within the board and there's somebody therefore their job is to sell the benefits of governance to the rest of the ELT and make sure that the the drive for improvement comes from the top down and then you need in many organizations depending on how big they are some sort of steering group and normally speaking and again in my experience for any governance program to work you need at least one person who is the lead of this whole activity that makes sure that everybody else is doing their job and therefore we'd always suggest you know you need a data governance lead in a small organization that lead could be part of time in anything bigger than that you need at least one individual and sometimes depending on the scale and complexity of your program more than one person who's full time to this and then you've got your data owners and I'll come back to how they are identified in a minute and you also want to be involved subject matter experts as well so for example you know when you're talking about customer data the view of the data protection officer is clearly very important the view of legal is very important as to what you can and cannot do with customer data I can think of a few organizations that would benefit greatly from having those roles a little more carefully defined but then also is important as I said earlier that those people may steer the strategy and own the roadmap for data governance but you actually need people at the ground level making a difference and making real change happen and therefore we're always an advocate if you create these working groups and the working groups often led by someone called a data steward whose day job it is to improve the data working with other business data stakeholders with IT people and with the relevant SMEs in order to improve or address a particular problem in the data area as I said before this is just an example there are many other ways of organizing that as well and here's a few sort of variances of that here just to illustrate what we're saying that one size does not fit all and the one on the left is actually from an organization I used to work in this was an appropriate structure for governance simply because it was a very large organization with over 100,000 employees and it was a global organization so there were sort of people responsible for data pretty much all over the world so you needed basically to split it down into the three main business areas of the time which are retail wholesaling global and that you had an overall steering group which consisted actually of any other executive leadership team member chaired that then a program board led by a full-time data governance lead and then you had business area boards as well that focused on subsets of the data problems within their own particular areas but then the program board made sure that they were brought together and actually collaborated and cooperated where they needed to where data as Donna said earlier was used across more than one function or part of the business then the second example you've got a small organization where if you notice they were just basically needed a steering group and one working group and that one working group was probably sufficient in order to drive data improvement forwarder and this was a company of around 400 employees pretty much all based in one particular location in London and therefore your organization was much simpler and much flatter than the one for the large global telco if you take the third example the consumer energy company they had their whole driver for data governance was data quality they had big issues with data quality and therefore they had the steering group but then the board that really did the work consisted of around 15 data stewards each of whom had responsibility for key data entities and key data attributes and their whole focus was on improving those those 15 or those actually 120 key data entities and attributes that were needed to improve core data quality within the organization they knew wanting to become digital but knew they had to fix these problems before they could even think about embarking on a digital journey and then the last one again reflects a larger company where you needed basically two layers of data stewards one who was a domain late data stewards so that might be customer for example the lead data steward and then sitting under that you had subsidiary data stewards one of whom is responsible for consumer one for business etc so as you see all those four models very different some of the same principles were the same but the actual execution of those principles varies depending on the organization that you're looking at so Don I know you wanted to talk a bit about federated governance as well Yeah and we actually had a healthy discussion between ourselves and just developing this of you know is it a level of maturity or is it just a different culture and I think you know one comment was you know is federated the first step before you get to be more of a you know having a true steering committee and I think it's just a difference in organization some organizations are very federated some that I mentioned before are using agile and this whole idea of just mentioning the word steering committee is going to kill your project that just will not fly right so this is a company we worked with and they were particularly sensitive and I found this very eye-opening for me and I'll be using this going forward being careful of this they said everything you show is this very you say it's federated but everything is a top down everyone's someone's reporting to everything else and they were right and they're like our company's more concentric circle I mean so it's sort of a it was not a hippie-dippy cut it was a manufacturing company so that one sort of surprised me because I would have thought they would have been very top down and so for them it was much more about collaboration and federation so they were also undergoing a big digital transformation and Nigel mentioned this before but it's worth highlighting we're also a fan of don't reinvent the wheel I know this is a wheel but figurative wheel they already had a digital transformation team and one could argue that digital is slightly different from data etc etc but this was at the executive level and this is where sometimes we data folks can argue ourselves to death in terms of you know I think I chimed in in the chat I you know I love the discussion of what is a train and things like that we just need to not do that in front of people so we have an internal discussion of well I know that this is you know all the key executive it was almost funny hearing us say all the key executives of the organization and making all the strategic decisions of the go forward plan but I don't know data isn't in the title and one of us at the dope spot each other is a guy all the key executive would just listen to yourself so the key organizations making tech decisions isn't data tech where we're supporting a digital transformation so we have changed tech and and just showed how data supports digital which wasn't you know that's very much true so the other part is that all of these teams were already doing data innovation whether it was marketing or supply chain R&D we wanted to respect that and it wasn't that any reported to each other it was more that if we had this council that got together it was not a steering committee it was a council of folks just sharing ideas that was just going to align and then the project teams that were developed and I've used this at even non pro the agile organizations you got to move fast and show that quick win so that people understand what governance is otherwise it's going to seem epidemic academic so all of the teams got together we realized we needed a customer MDM solution why don't that wasn't necessarily a quick win but they are doing it in small chunks so they're picking a particular region their picture like their subset of MDM it didn't have to be a one year effort they're literally doing a couple months sprint on enterprise wide customer MDM but they're all doing it in an aligned way so this took us a long time to get up with but it sort of respected the autonomy and it's respected the culture so I wanted to show this and the agile development life cycles I wanted to show this it's just sort of a because I do it myself we tend to always use almost a classic DM box kind of steering committee working group it doesn't have to be how it is tie into the existing organization and culture the other question I've seen some of the chats about this too and in arguments back and forth which is healthy and great which is how you define stewards how you define that definition of not just the councils but the stewardship so I'm going to pass it back to Nigel and kind of some ways to look at this okay thanks so this is really all about how do you decide which people set on whatever bodies that you create and whatever organization that you create so much of those roles be and I think this is a slight simplification but I think the way we look at the world that there are five ways basically that you can apply different methods of appointing people who are accountable for data to sort of governance type roles and basically they fall into these five year and you can read them for yourselves so I'm conscious of time as well so I'll work through this fairly quickly is that you can have a process centric model where the process owners become the data owners basically for the data created and deleted by the business process so for example if you own the procurement process in your organization and that process creates deletes and amends data then it makes perfect sense for you to become the data owner and the people within that that process to become the data stewards of the data that's one model and then systems centric model can also be applied this was very applicable in my old organization when I worked for the telco I mentioned earlier which is that there was a lot of business power and funding were held by business people who were the owners of the key systems of the company so there was a key there was a key owner for things like CRM excuse me that was my phone going on bad practice yeah and it was also Game of Thrones again yeah so for example if you had a business owner of the CRM system then it made perfect sense for that business owner to become the data owner of the data held in the system then you're going to have a data domain approach which is basically that you know you can have way person Donna mentioned this earlier that is responsible for product for example right across the organization cuts across the various processes in the various organizational boundaries then you can have an organization centric model which is basically you say right I'm in finance we own finance data the data owner of finance is a finance person and the data stewards become finance people and that can work as well across geographical locations so if you've got outposts in Singapore, Rio de Janeiro and Montevideo then you might appoint an individual in each of those locations to lead on governance efforts in those areas and then finally which is one we're seeing more and more is what we call blended so basically blended means a combination of all the above depending on the particular part of the business that you're working with so for example in finance it might make sense for it to be organization centric if you're in the production area then a process centric model or a system centric model might be more appropriate so how do you decide what's appropriate and what's not and very quickly what I've done here is just reduce this summary of basically the pros and cons of each of the models this is a worthy slide and I apologize for that but it may be useful reference for you so are you trying to decide in your organization which of these models to adopt here if you're like a few advantages and drawbacks and I think the key thing to stress here as well is that none of those models is perfect or the perfect solution but one of those models is probably best for your organization depending on your particular needs so for example just a couple of very quick examples process centric that works really well where you've got a strong culture of process ownership in your company if your processes are properly defined you have clear owners for them then putting data ownership there makes a lot of sense you know system centric works in the organization I just talked about where business people had a lot of finance financial clout and a lot of influence because they were the owners of systems and therefore it made sense to align it with that data domain usually works better in smaller organizations they also used a lot of insurance industry for some reason but data domain centric is you know we're working a small company where maybe you know customer data might be used in three departments of that company but it's not really that difficult for one person to take ownership right across that end-to-end data chain and I mentioned already organization centric and blended and basically they all have pros and cons none of them is perfect and therefore you picked the one that's best for you and Don I know you wanted to talk there a little bit more as well about aligning things with corporate culture Yeah I think these are just some things to think of I think you know when I think of things that have gone well and gone wrong and when we've done of course never gone wrong with us but yes we've all had we've all had not as ideal as we wanted in the past so much of it often comes down to culture when I come into an organization I try to be aware of that I just watch how meetings are run I kind of you know to the top down is that federated I ask people who are working there and I think what can go wrong is just getting it not the right fit again agile waterfall one is it better than the other just don't mix them in the wrong place so you know think of your organization would it work better that it's more of a formal top down that we get the executive steering committee that drives things it could be do your industry it could be you know I would hope a pharmaceutical company has a fairly rigid you know drug testing process right which is probably very different from a startup I'm doing some web marketing right so aligned to your culture you know do people like meetings is that is there a meeting you got to always join on to people who'd rather get on a wiki or a slack page or you know again think of that do people want to meet in person or not fit what already existing your customer don't read the DM block and say oh they talked about a steering committee so that's what I'm going to have that may be the way but align that to your culture we didn't talk as much as I would have liked as a whole other webinar on the idea of when you're thinking of governance is it offense or is it defense are you thinking more of aligning to fix risk was there a audit breach are you a highly regulated industry and that's what management cares about or is it more about opportunity and I'm going to kind of start to pre-answer one of the questions was how do you get the buy-in of your executives I would say number one think of that first don't go to an executive board that's all about their grand new product launch and an opportunity and growth and say you know it's not going to work your data's bad that's going to be the wrong thing similarly don't go to very heavily you know regulated industry and talk all about just the only data opportunity I think us on the call is data people tend to go more towards the risk and maybe want to be a little more positive an opportunity of how good data could help right also pace and timing I always look for a quick win in an organization and I always ask what's quick for you all right so some quick wins are two weeks some are two months some are two years I would disagree with two years right but what's fast in your organization Nigel I mentioned on the third bullet complement what already exists if there's steering committees that you could align with as long as you don't lose the data focus all power to it are there data owners and stewards that could be promoted and look at it as a promotion to data owner work with what works right that's kind of a whatever funny term but that's going to be your easier past success align with existing processes the last one and we already talked about semantics language matters if people get hung up on meeting minutes because it sounds too formal call it an action log if it's not a steering committee it could be a council I had one customer that called it a tribe right a collaborative whatever it is but get that right or you could get one company instead of a steering committee it was a data strategy council and again had people signing up to join that so give give that some thought I know we're close on time so these are some use cases you can read in your leisure sort of what has worked and what their drivers and priorities are and then we probably only have a time for a question or two but while Tenley is kind of coordinating those I'll just kind of point to next month if you're interested in master data please join us again Tenley over to you great we actually do have some questions the first is regarding finding data stewards how do you engage a department where you know who the stewards should be but the powers that be will not allow the time for them to govern or for them for to governance I'm sorry I'm struggling with that one I'll say two quick things then I'll pass it over to Nigel I think one I've had this question a couple of times in the past few weeks just documenting how much time is and I think we often forget that this person should be spending 10% of their time and they will be doing this sometimes we're a little too vague or we expect people to know and the other thing is tying it into something that's already helping hey they're helping fix the data for that marketing campaign could they just spend an extra 10 about whatever and then often those two things the why and they're very clear on how much can sometimes help but what do you think Nigel yeah I agree with that I think goes back to something I said earlier as well above the importance of getting support for your governance initiative from the very top of the organization I came across this when I did this in the telco I worked in and the way we broke we broke the barriers in two ways the first way of doing it was simply we got more senior managers to say tough get on with it this is an important business priority and therefore it's in your objectives to improve your data and therefore you need to appoint David Stewart's to make sure that happens for you I think that's really important the other thing I would say as well is something I touched on earlier when you get that resistance don't then then walk away from that department go to another department where there is more enthusiasm and people are prepared to allocate time prove the benefits and then go back and see those people again and tell them what they're missing out on because they haven't found you know they have got the will or the time to do that in other words provide them with real hard evidence empirical evidence that data governance does improve the business and kind of help them improve their own bit of the business okay well thank you Nigel and Donna for this great presentation and Q&A but I'm afraid that this is all the time we have for here today just to remind everyone we will be posting the recorded webinar and slides to dataforcity.net within two business days and I will be sending out a follow up email to let you know the links and other requested information thank you again for attending today's webinar and I really hope that you have everyone has a great day thanks thanks all thank you