Data-Ed Webinar: Data Governance Strategies

and welcome my name is Shannon Kemp and I am the chief digital manager for de diversity we'd like to thank you for joining today's de diversity webinar data governance strategy spawns to date today by Mpho jaques it is the latest installment in a monthly series called data ed online with dr. Peter akin brought to you in partnership with data blueprint just 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 if you'd like to chat with us or with each other we certainly encourage you to do so just click the chat icon in the bottom middle of your screen for that feature and 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 by Twitter using hashtag data Edie and to answer the most commonly asked questions as always we will send a follow-up email to all registrants within two business days continuing links to the slides and yes we are recording and we will likewise send a link to the recording of the session as well as any additional information requested throughout the webinar now let me turn it over to Jonathan for a brief word from our sponsor today info Chicks Jonathan hello and welcome thank you very much and welcome to all our attendees so I'm going to give a little little bit of an introduction to Peters presentation here and really just some observations on the strategy challenges as we see it I'm going to start off with a bit of an infomercial just keep it to one slide but I work for a company called in projects we've been in the data business since 1982 so hard to believe at times the world has changed remarkably since then but over that time within the data governance and data quality spaces and always in the data analytics space although that means something totally different than what it did in in 82 back then there was more of an accounting definition reconciliation and so on nowadays we're really looking at the analytics functions as they support data quality and data governance and so on so not the broader scope of that of that term large and mid-sized customers for the most most part and in general our folks have been with us for on average of 18 years so we're very proud of that across a number of different market segments and so on so that's just a very brief overview of in projects we go to the next slide what I thought I'd do is just really folk so on you go to the next slide Peter just some observations as they relate to strategy and this might help ground the discussion I might help you think about [Music] kind of where we're going here and maybe resonate within your context but as I was thinking about this I just thought you know okay governance strategy you know for a lot of our customers strategy happens the second time around the first time around governance is all about solving some kind of pain at some point people step back and say how do i scale this and then they tend to run into into challenges and that's when you see a lot of these failed programs so strategy is something that needs to be addressed quite often that's on the second time around secondly you know little grounding in the reality of business-related business proposition strategy is going to help you out in this in this perspective it is so easy in the trenches if your data steward if you're in a curation if you're labeling data you know in that in the trenches to really remember and keep in mind why we're doing this right and a lot of the folks what are the teams that are practically engaged you know that's what I see and of course people lose interest they lose energy and so on lose focus so the strategy helps there thirdly you know as I'm looking at the strategy a lot of folks don't question their operational model right you know who's meant to be doing what for whom why what tools that kind of thing and as you're doing that strategy you're really forced to think these things through right you know reality is not always the same as strategy but the strategy forces you to pull back and really focus on those and lastly I would say in terms of general observations there's a long trail of words here right there's always that person on the team that being in the company forever they you know they're they know every reason why governance hasn't worked for the last 20 years and then we'll happy to communicate to you around that right so those people can be asset they can be liabilities you know you've got to find a way to communicate with them and the strategy is a good way of saying look I get it but here's what we're doing is why we're doing it and here's your role detector detector so you know for me a strategy is critical it ties all the pieces together I'm looking forward to hearing Peter swords so I've got one more thought you know Peters got to talk about damage in Bach we go to the next slide and you know there's a lot of frameworks out there right and what does good look like I find that the frameworks are really good they help me look smarter I may be more knowledgeable than I actually am which is always helpful but there's a lot of frameworks out there and I include gear gdpr broadly speaking but some of the ISO things becbs 2:39 risk data aggregation if you're in the banking world APQC and score if you're in supply chain management and so on you got tie those into your governance framework right how do they fit into the governance framework some level quite easy but those really go down to the data level and so I tend to rely on Dom Adam Bach and C remise data management maturity models as as frameworks as sort of mighty references because at the end of the day you've got to drive that down at the data level because that's what we're managing that sort of underpins a lot of these best practices and I think Peter did a CMMI webinar a month or so ago so between this one and that one probably have quite a good reference nap so with that Peter let me hand it off to you looking forward to your thoughts and we'll have some Q&A afterwards documented joining us for the Q&A afterwards too right yeah yep I'll be in to get everybody in there you bet sorry Shannon didn't mean to cut you off oh no perfect day yeah that's exactly what I was going to say it awesome yeah so yeah thank you Jonathan so much and thanks to projects for sponsoring and as mentioned if you have if you have questions for Jonathan he'll be joining Peter and the Q&A at the end and now LEM introduce to our speaker our is Peter akin Peter is an internationally recognized data management thought leader many of you already know him or have seen him at conferences worldwide he has more than 30 years of experience and has received many awards for his outstanding contributions to the profession Peter is also the founding director of data blueprint he has written dozens of articles in 11 books the most recent is your data strategy Peter is experienced with more than 500 data management practices and many countries and consistently named as a top data management expert some of the most important and largest organizations in the world have sought out his and data blueprints expertise theater has spent multi-year immersions with groups as diverse as the US Department of Defense Deutsche Bank Nokia Wells Fargo the Commonwealth of Virginia and Walmart and with that let me turn everything over to Peter to get today's webinar started hello and welcome hi Shannon and Jonathan thanks for a great introduction and we are looking forward to engaging you as we get to the end of this because one of the things that's real important to remember for all of us is that unlike the accounting profession which has literally eight thousand years of tradition in it we do not have that so when Jonathan says he's been doing this since 1982 my own company data blueprint I only form that in 1999 and that was before Google so Jonathan was before before Google if you want to go that way on all of that and we'll look forward to circling back around and catching some of that but what we're going to cover the next 55 minutes here are four major topics first one is that data has some very confounding characteristics it's got some really good and some really strange aspects of it it is uniquely valuable in a way that other types of assets are not it's largely composed of rot which if you hold your breath we'll get to that and I'm going to make a case for making better decisions about data which is really an important part of governance we'll talk about three specific strategies the first one as Jonathan said strategy usually does come the second time around Wow we can't really boil the ocean once they realize how big the ocean is because I said the discipline is immature and by-the-book is not really a good starting place so we need a more targeted approach to data governance second strategy is that data governance has to exist at the programmatic level and I want you to tie that in your organization's minds by the way I say you you the delegates on this call are responsible for going to your management and communicating this stuff if you want some help call Jonathan and I will be glad to help you out on it but data governance is central to data management as a practice and it must be decoupled from IT strategy be directly supportive of the organizational strategy and our third strategy then is gradually add ingredients so things like frameworks stewards checklists scorecards we'll go briefly through a bunch of these things finish up with some worse practices if we've got some time we'll talk a little about storytelling at the end so we'll see how fast I babble and work our way through this let's start off with data is confounding characteristics first of all IT thinks data is a business problem if they can connect to the server my job is done and I have heard that phrase from a large number of IT people this is not a bad thing but it's the way they have been socialized and taught and more importantly rewarded for doing their jobs well if IT thinks data is a business problem they're not going to do anything beyond say I've given you access to the data now my job is done of course business thinks IT is managing data perfectly adequately because after all who else would be taking care of it other than somebody with the title chief information officer this means that data has fallen into a large gap between business and IT and collectively our mission as data professionals is to rebuild re-establish the trust that needs to be there now as a topic data has some very confounding characteristics as complex and detailed and besides the three of us on this call and the thousand of you roughly otherwise that are out there in addition outsiders don't want to hear or discuss any aspects of this it's just not terribly interesting to them and quite frankly most of them are unqualified because they don't have the requisite architecture and engineering backgrounds in addition in most college and university courses as well as private courses it is taught inconsistently the focus is largely on technology because technology is easier to teach than what we're talking about in this webinar and in virtually all instances the business impact is not addressed and finally it's not really well understood which means every work group in your organization has had to learn about this because there's a lack of standards and poor literacy rate and they're very unknown dependencies and I showed this little clip here and I'm going to put this up and just give you guys a little bit of good work but maybe not the right kind of work [Applause] [Music] [Applause] [Music] [Applause] now that guy's great right but if this challenge was to go off and learn how to play the piano there are some easier ways to do it and unfortunately this is what's happened in your organization's that everybody in your organization has learned how to play the piano in a different way because of the lack of standard practices around it and it's probably not a newsflash to most of you but half of all companies report making bad decisions and the reason they make bad decisions is because the business decision makers are not data knowledgeable and the technical decision makers are not data knowledgeable which leads to bad decisions bad data decisions lead to poor treatment of organizational assets and poor data quality which leads to poor organizational outcomes and of course we have to get ourselves out of this particular spiral now in terms of confounding characteristics one of the really interesting parts about data I mentioned it before is that I hope you agree that if data is better organized it is increased in value for example if I were to give you these slides or Shannon were to send you out the slides at the end of this webinar and they were in random order you would probably get a little confused and perhaps a little frustrated and say Shannon what's the deal and she said well the data is all there you can figure out how to do this after all the slides are numbered all you have to do is take them and put them in order right well that's not value-added so these poor data management practices are costing organizations a lot of time and money and 80% of the data in your organization authoritative Lee is rot rot as an acronym standing for data that is redundant obsolete or trivial and that's a problem because it means your knowledge workers have to wade through a large amount of it by the way my wife corrects me on that she says is redundant incomplete obsolete or trivial but rots a better acronym than riot so the question comes up which data needs to be eliminated that means governance is largely about reducing the wrought recycling the stuff that is good adding good practices to it and rename excuse me reusing the remainder so that we have fewer vocabulary items and we can engineer the rest of it for beta better leverage now data as an asset is fascinating asset because it's the only asset you have in your organization that is non delete excuse me I said unbelievable it's absolutely believable it's not depletable that's the word I was looking for it is non degrading and it is durable in nature durable is an accounting term back to our 8,000 years of accounting history on this end of the strategic level data assets win when they compete against other assets because other assets don't have these characteristics people wear out buildings where and most of the time our transactional data is not nearly as durable as we'd like it to be so everybody wants to say things like data is the new oil Seikaly I can't stand that phrase it's a terrible way to think about oil because you never think about what happens to the gasoline after you put it in your car and yet data needs to be reused so the best way to think about it is when somebody says data is the new oil say can I correct you on that and just put the words just give me the letter s in front of it there are two things about s soil that make a little better metaphor for this first of all you don't just randomly run around your yard and sprinkle seeds all over the place and expect that things to happen that's not the way it works and secondly you don't plant tomatoes on Monday and expect to harvest them on Friday so timing and care and preparation are two things that go well thinking about data as the new soil if you want to call it bacon that's ok too as long as we can get people to talk about it but the bottom line is that data deserves its own strategy it deserves attention that is on par with similar organizational assets and it deserves professional administration to make up for past neglect now bad data governance costs organizations millions each year productivity redundant and siloed efforts poorly thought-out hardware and software purchases delayed decision-making because they're using inadequate information spending too much time reacting instead of being proactive and finally eliminating 20 to 40% of all IT spending through data governance so those are pretty interesting characteristics let's move directly into the first strategy and question pops up what is the world's oldest for I've already told you it's accounting again it's been around for 8,000 years in the most important aspect of that as we now understand that there is something called generally accepted accounting principles and those principles are absolutely key to understanding how organizations do this again we go to maybe a hundred and fifty years if we do it I look at Lady ADA Augusta King she is the daughter of Lord Byron and the world's first programmer she invented programming before computers were invented so let's take our 8,000 years of generally accepted accounting principles and that by the way they haven't been around for 8,000 years and let's look at seven definitions of data governance and I'm going to put these all up here on the board we look at them and it's like oh my goodness whoa what does all this stuff mean even our own and democ the exercise of authority and control over the management of data assets good definitions all good definitions no problem with any of them but I prefer a better way to introduce it the way to explain data governance to people is to think about how you're actually going to do the work and I'll do a quick little Dilbert here just to illustrate it the committee says Ted decided that the file naming convention will start with the date in the order of months day and year of course he's got that wrong already then a space then the temperature at the airport and the high-hat size of the nearest squirrel to be perfectly honest it was a long data governance meeting and we probably didn't do our best work you don't want people to think about data governance that way so let's give them something simple that they can think about managing data with guidance now interestingly enough the first question people ask then is would you want your soul non depletable non degrading a durable strategic asset managed without guidance and the answers probably no so we go back to managing with guidance and frankly we really need to add one more word and they're managing data decisions with guidance so let's take a look at how that works out first of all hopefully this is not the first time that you've seen our Democritus the governance is central to that wheel we published this in 95 we've made a revision to it this is the first version there a second version of it out there these are good efforts but you'll notice that governance is central to each and every aspect of data management around here and this is really key now I mentioned earlier by the book was probably not the best way to take a look at this and by the book says that your governance should take a look around metadata and data quality in bi and master data and data acquisition and database strategy and document strategy and no no no it's too much and especially as you're introducing something new to this so as Jonathan was saying earlier pick three things right we'd like to look at about so maybe a first component of data governance might be to look at the connections that exist between data quality and reference and master data management if you don't know what those are we have webinars coming each of them so just stay tuned and they will roll back around on this but the idea is governance is key it's a necessary but insufficient condition for successful implementation of either quality or master data references and that's one example we can take a second example of that as well which is maybe that we've now moved from there into sort of a bi kind of situation and again notice we're exercising different wedges of our DIMM Bach PI but the data govern still continues to be absolutely central to this now to look at data governance from an overall perspective this is the way the dim Bach version 1 described the environment of data governance so you can take a look at this you'll get these slides as Shannon said we're not going to cover everything on here but it's a nice input-output process diagram showing you what it is you need to do to do the same things that we already do at the corporate level corporate governance does exist in all organizations and with respect to IT governance very very useful equivalency constructs if we're going to govern our corporation we're going to govern our IT then we probably ought to be governing our data as well and let start out by being very practical about the process what does data governance mean to my organization right now there may be other times when it means other things but at the moment let's focus on one aspect of it means managing data with guidance getting some individuals whose opinions matter we do need to do some social engineering around this to form a body that needs a formal Charter a purpose and authority who will advocate Evangel a4 try to avoid words like dictate enforce and rule to increase the scope and rigor of what we call data centric development practices or more commonly known as data centric type things and let's take a look at how they work in common first of all we've got a governance organization they are supposed to be managing data with guidance well where do we start and that the answer to that is your data strategy is going to tell you what is the first bite of the Apple or maybe a better metaphor the first bite of the elephant I'm not advocating eating elephants but you know what I'm talking about with the references here what do they do to support strategy and the feedback from that is how well is the strategy working now a data strategy is absolutely context less unless you have an organizational strategy as well the organizational strategy says what is the data doing to support the organizational strategy if it's not supporting organizational strategy one might ask the question why is one doing that now at least in Peters world I say that data governance also prioritizes IT projects and I've got large number of organizations that have achieved success with that by creating that as a clear part of their data charters it doesn't make it easy and sometimes we end up with some unhappy people as a result of it but it is definitely working well we'll add some feedback loops in there just to make a a more complicated diagram here and then going to simplify it now let's just take the real important parts of it out here what we're really talking about is that data strategy and governance has to be about achieving specific business goals and that the language of data governance has to be metadata if it not one might ask the question why did these things go off the rails as Jonathan referred to earlier answer is because people tend to start talking about things that are not directly supportive of organizational strategy a quick definitional difference here data governance tends to exist as a policy level of guidance and data management is the implementation of it so you might have a policy that says all information not marked public should be considered confidential and that's a perfectly reasonable strategy in order to do that in fact in the federal government they've recently been told that by law so as of July 14th 2019 all data and the federal government that is not sensitive data is now going to be considered open data that's going to be a big change for everybody the process of how to get there in the federal government and everywhere else is the data management function and that's the component that we're going to look at as well let me show you though a little bit of how these two work together now dip is an abbreviation for a data improvement project which is something that the data governance in the data management organization are agreeing to put together to get started I may start out with not really a lot knowing to some sort of vague feedback in the you know data leadership position may come along and they they may say we need to do some data governance and that's policy and that'll start to improve data over time it's a slower approach to that but we also need from add a leadership perspective to implement some specific improvements and that's a faster approach as we go through this that gives us better feedback and when we have that better feedback we can then start to implement more aspects of data governance for example putting in place a class of people whose job it is to be trusted with the data that's what the definition of Stuart is and those stewards may have some contacts in the data community that they can influence and help to make their jobs easier and those data community participants interact with other people so we've pretty much got the entire organization there and eventually we'd like to have a two stage process where data improves gradually over time through better guidance but the data improves more rapidly in response to a burning bridge incident that may actually be faster where we've had trouble in the past is that that sort of waving set of lines there between data things happen and organizational things happen that line is not always as clear as it could or should be and that's the place we've got to get better as data professionals in order to do this both Jonathan and I have had many many years of helping organizations understand that a change at the data input level for example forcing hospital employees who are taking in patients they shouldn't actually optimize for speed because we did run into a hospital at one point that thought they were doing lots of more nice ER juice than they were because the default hospital admission code was knee surgery and the hospital director thought that the hospital was being managed according to data so a little bit of a context here we can't rely on simply the top line to improve data quickly and we don't want to do just the bottom line we need a balance between the two of those pieces so that's strategy number one keeping data governance practically focused let's move on to strategy number two now data governance has to exist at the programmatic level and what I tell people is to equivalent it - that's a terrible word let me say that again make it equivalent - and everybody's mind your HR program nobody in your organization is sitting around and saying hey you know I think we've done enough HR we're never going to hire anybody else we're not going to have any internal problems we'll never have any regulations that we have to respond to or new laws that we have to implement so all of you HR professionals can pack your bags and go home it just doesn't work that way HR is recognized as a valued part of the organization that needs to exist as long as the organization needs to exist and you know what your data governance program is exactly same if you don't understand or your organization doesn't understand the difference between a program and a project your organization is likely to be one of those ones that Jonathan said is going to need to come around and do it at least a second time I've been some organizations three different times to get them up and running in order to do this so let's take a look at how this manifests itself it's really sort of problematic because we need to understand that data governance being central to data management means that the way most organizations have been doing things is simply raw now the way it starts out it seems very reasonable in support of strategy oh by the way I should say happy birthday to Doug Bagley who's our originator of this diagram the friend I met a number of using about decade ago actually and just noticed on LinkedIn yet his birthday the other day anyway back to our thing the organization starts out with strategy and they do some IT projects and data and information is kind of like the last thing that they think about well if your IT project involves a piece of software as soon as you start talking about software the entire project becomes about the software and we tend to spend all of our money on software and then we temp and forklift the data into the new system now this is a problem Dave McComb has a wonderful book out this called software wasteland and some of the points that are made here are very similar across everybody's domain in this area it ensures that the data will be formed around the applications and not around organization-wide information requirement so it's the process architecture that's created when you do this is narrowly formed around application support which means very little data reuse is possible now that's the wrong way to do it here's the right way to do it and you'll notice there's not much changed all I've done is flip the data and the IT boxes on there but it's a very important to flip because if I put data next that means that the data assets can be developed from an organization-wide perspective that systems supporting the organizational data needs can complement organizational process flows and most importantly we can maximize use of information and data along with again good bad very easy to see the difference strategy data as opposed to strategy IT but you can see that going to upset some Apple carts and make some people a little bit unhappy it gets worse again from a governance perspective many organizations say we've got an organizational strategy and an IP strategy and then we've got our data governance strategy down below this well again this is just simply wrong and if you allow organizations to do this you will achieve not as good results as we'd like to the correct way to look at this is that IT strategy and data strategy or co-equal in this case although I'd like to pull an animal house here and say that the data strategy is a little more equal than the IT strategy the key to this is understanding that data is not a project data being a durable asset is something that we need to have we need to understand we're managing these assets with a useful life of much more than one year in fact one of the key measures to all of this is called customer lifetime value well how long is that data going to last the lifetime of the customer reasonable project deliverable times for a project are 90 days or maybe two weeks if you're in an agile mode of somewhere but data evolution is measured in years and in many cases decades this is a hard sell to management we're going to buy into a process that's going to take us five years to make up for past problems in this and to start working in future because data evolves it is generally not created we grab it from other places and as an organizational asset it is significantly more stable than other types of organizational assets for example I've number of organizations where literally I have gone back to them after 20 years and we've taken our conceptual data models and our logical data models that we built and looked and said yep we are still managing exactly the same types of data that we were managing 20 years ago which means it is more than worth the investment that needs to be made in this the key is if you start building IT components and you have ready-made data architecture components to put into those these are a necessary but insufficient prerequisite to agile development and the only alternative is to create more data silos which means you'll be either Johnathan's customer or mine or maybe both at some point in the future data programs must precede IT projects because IT as they build a project oriented type of discipline where as data evolving over time means that from a structural perspective we need to separate make it external to and precede systems development activities and we don't teach it this way in school so we've got three generations of students who have been taught incorrectly in their college and universities and you wonder why we have so many projects that literally go off the rails our third strategy then is to take a look and gradually add ingredients and this one is more important because so many organizations like to figure out exactly what they're going to have and they think they can forecast the future on this now if you guys can actually forecast the future give both of us a call because we're all in the wrong business and we should do something very very different than we are actually doing right now if you get on on Google Maps you can see this is my house and this is the barn that I build now I'm what's called a course husband which means that part of the deal was that my wife gets a barn and so we went to the bank and asked them for money and they gave us exactly this much money he may say well that is not a barn is it the answer's no that is not a barn but that's all the bank would give us in terms of value because they wanted to make sure that we had a good foundation and data governance is partly about making sure that your organization does in fact have a good foundation upon which to build let me take the barn analogy just a step further if I built this barn with a poor foundation then I would spend money in vet bills because the barn would not stay up and it would fall down on the horses and cause them harm and my wife would take them to the vet and I would pay the vet bills right well if I'm paying vet bills I don't have money to give the bank to pay back the bank loan for the barn you see banks are pretty smart before further construction can proceed they required the county to come out and give me a certificate that showed that I had a good quality foundation and I could build a good barn on top of a good foundation but there is no IP equivalent so this gets us to a discussion of frameworks and frameworks can be extremely useful if they are utilized correctly the idea is that a framework is a system of ideas a guide analysis if you have not already encountered it John Zachman framework is one of the more successful and popular frameworks out there we haven't done a topic on that but if that's one that you want to see just send Shannon a note and she'll accumulate them and get them together on that we can certainly do a topic around Zachman framework it's a means of organizing project data it gives you the ability to make it priority based decision making as and what should go on the framework next like if I'm building a barn I'm probably going to put the sides up and then I'm going to put the roof on it why well because if I put the electricity in before I put the walls up I'm gonna have wires all over the place and the wires will be exposed to the elements if I put a roof on I can put walls up and then the wires can go inside of the walls it gives you a means of assessing your progress around that again I already told about the roof and make sure that all funding is dependent on that now this diagram that I showed you a few minutes ago from the dim box is a framework you can look at this and see what things are we doing which places should we start what activities are important for us to do and what project deliverables will most benefit the organization but I'm going to show you a number of other frameworks your task is to look at these frameworks and decide whether they are valuable for your organization not now not in real time when you get the slides since look at this after the fact but here's a great framework that the data governance Institute put together our good friend Glenn Thomas who's been at this business for many many years Rob Steiner has a framework that he uses here again his is a very good framework each of these frameworks couple from IBM here in order to put things together all good frameworks don't look at these right now and pick one but spend some time with your leadership and see what happens gosh here's the American College personnel Association and their framework looks like a boat cool my point of these frameworks is that each of these frameworks offers certain things to the organization and is well worth your time to sit down and study them and think about how your organization works and decide what elements from what frameworks are going to be useful to your data governance initiative in order to do this and most importantly in what order should you implement them because if you try to implement it all at once it becomes somewhat problematic so again a couple of different frameworks that are out here I don't expect you to look at them now and make a decision but take them with you and look at them and then see how that works within there this is a SAS Institute has a very good one that's out there each of these things are very very useful within that context and I want to tell one more story here before I get to the sandwich story so let me just go back to the the data governance framework I'm working for a good organization that's a government organization right now one of our data blueprint clients and they're doing a great job on this but they decided that they wanted to have a data leadership component in their framework and so when I showed them the framework that they were going to be adopting they looked at it and half their leaders dropped out said well we don't want to do that that's like real work and we've got real jobs so everybody kind of wanted to be involved in it but there's a difference between you know the chicken and the egg kind of situation and in this case skip my said chicken and egg chicken and pig in this case the pig obviously is committed to the process and the chicken is participating in the process and these are good ways of helping the organization understand when they look at these frameworks and see how it's going to work for your organization you may decide that you have different ways of doing this in different phases in way in which you take your organization into this particular Road now we talked about chickens and pigs let's talk about sandwiches now for a minute and the idea is from a governance perspective currently your organization without knowing anything about you is got varying data literacy levels in it your organization also has varying amounts and qualities of data supply and varying use of data standards what we're trying to do with governance is to sort of smooth these things out because one thing we do know about data is that there's so much of it that we largely have to automate processes around data so data governance has to be thinking not in terms of manual decision-making but in terms of automatic decision making only when you have automatic decision-making can you put these things together in a way that works better for organizations and these things of course cannot happen at the automation level with the kinds of data volume that we see in most organizations without engineering and architecture concepts mentioned before most organizations most people don't have that kind of requisite background but interestingly enough I was on holiday in India last year and literally on this tea farm that I took a picture of here there was a sign up at the cash register that said quality engineering architecture work products do not happen accidentally that's a very profound piece of wisdom from Peter Deming if I recall correctly we stop Peter dime you say how does the Deming quote sorry I said Peter demagogy I said Peter Drucker's Deming on this and of course we need to go back and add in the words data here data engineering data architecture data quality engineering all of these things cannot happen without that so how do we get started in the process pretty much you're going to have some startup activities right you're going to assess your context and come up with a roadmap of some sort make sure you've got executive mandate and assign some stewards that on the left-hand side occurs one time and on the right-hand side you're going to go in the cyclical process execute evaluate revise apply change management atypical Deming planned to act check the cycle why are you going to do that well in many ways is the same way that one would learn how to play a musical instrument if one starts out and understands that the first piece of music are going to play as chopsticks tan tan-tan tan tan-tan tan tan-tan tan tan-tan tan tan-tan tan tan-tan tan tan-tan tata you guys ago what are we listening to alright was me imitating a piano very badly and more importantly when you hear somebody playing that you usually say stop playing that piano right because it's annoying but it's the only way you get better and that's what's got to happen on the right hand side of this diagram is that you're going to find out what works for your organization and start to include in that process things that go into it so let's let's go a little further with this again just to tease the analogy out here right if you look up in our done body of knowledge it'll say that you need to have data policy standards resolved issues projects and services quality and information recognized that's a lot and by the way this deliverables let's go a little further roles and responsibilities oh my god we've got suppliers consumers and participants and each one of them has multiple roles that are in there what are the practices and techniques that we've got to learn how to do oh my goodness right that's just four slides and that's a bunch of work there's no way you can anticipate how any of this is going to work which is why so many data governance initiatives sputter falter lose support the primary decision-maker disappears from the scene they're no longer able to put in place things that they'd like to put in place so let's take a look at it from a little bit of an easier perspective again I'm not saying any of these things are bad but they are all aspirational and you should try to plan in advance how they're going to be what you should do is pick some of them and start to implement them on that cycle so let's take a look at the components that go into this first of all we've got ite and we need to have ite because IT provides a supporting infrastructure for things that we do that's a four quadrant diagram so on the left hand side of the diagram we have domain expertise that is less than domain expertise that is greater on the right-hand side and the roles are less formally defined on the right-hand side and more formally defined on the left-hand side and on our y-axis website the wrong key let's go backwards there we go on the right hand side of this diagram then we also have two dimensions here the people in the lower half of the diagram that I'm going to show are going to be they're going to encounter governed data more directly whereas the the people at the top half are going to encounter it less directly and they're going to have more time to dedicate to it on the bottom half and less time on the top half so our first quadrant is the leadership quadrant we're going to have some data leadership and we need to make sure that data leadership is in fact correctly trained and one of the most important aspects of data leadership is ensuring that your data governance program has the required resources if it doesn't have the required resources then it's not going to be successful in order to do that many organizations just think that as long as I've got a data leader that's good but if we look out there one half of the chief data officer z-- worldwide have no staff and no budget that does not represent conditions for success the leadership is going to get guidance from people the guidance is going to come from people whose title is data steward ideally in Peters role data stewards are a full-time position and if you have the opportunity of getting ten people to give 10% of their time to a task I would ask to trade that ten percent of ten people's time for one full-time equivalent because you will get better productivity and results out of them but stewards are informing leadership about data decisions that they and when leadership makes data decisions that information goes back to the stewards who then need to interact with the data community participants these are your subject matter experts the people who use data heavily in your organization and this is where ideas come from about how to improve things because the data community participants are the ones who are running your analytics or fixing problems with your legacy systems about half and half by the way in order to do this we've also got this fourth category in the upper right hand quadrant the generators the users and they have some feedback that they will be giving directly to leadership and through the data community which then comes back to the stewards in terms of ideas and once leadership has made the decisions their leadership then expects that decision to be transformed into action and that action goes back to from the stewards to the data community participants and the generators of these ideas if we don't have these types of conditions together is going to be very very problematic finally of course we would make sure that people who are not directly involved in this do experience and change in order to come up with this now let's take the context of a data steward in particular and our friend David Plotkin has written a very fine book on data stewards the only problem with it is that I want you to imagine yourself trying to go into the organization saying well I've only got 10% of 10 people's time so 5 percent of that time is going to be managing subs as a business data steward and somebody else is a technical data steward and oh by the way if you've got data stewards and you need to have data steward auditors in there and a data steward manager in order to do all of these things sorry David this is too much way too much as a starting point it's a great place to mature into and in 30 years of doing this I probably count a dozen organizations that have matured to the point where they can do this which means that hundreds and hundreds of others that I've had direct contact with are not mature enough to break these things out so instead of trying to describe what these roles are and who's going to occupy the roles and all of this stuff up front let's just start with a concept of a steward what is a steward while Stuart is someone who is trusted it's one who actively directs things and if we move it to data Stuart then they actively direct the use of organisational data assets in support of mission objectives this gives them the ability then to go in and talk about specific types of things in a very narrow let's roll it out gradually type of process again we can move on to scorecards on this yes do we have decision-making authority standard procedures all of this stuff that we go up to but let's not do it all at once and the same thing there's true for scorecards let's not go through and add all of these things at once let's start small grow our way up into these programs because if you try to do it pre-specified all at once you'll run into the personalities and things that they're these soft aspects of this thing the technology can't help us with and instead we will have really bad cat fights in order to do this one more component for this strategy piece again here's our guidance from you know previous webinars we've done and things like that and these are things that we should include in data governance program right no let's not do that in fact let's instead look at this and say first for our organization again hypothetically completely in nature but let's say that risk management is most important and then security and privacy in other words prioritize these things so that we can put them in an order that will make sense and I want to go over here just so that you guys hear the Hotel California running in the background and if you read through the words it goes in and talks a little bit about how problematic these things are but if we don't have buy-in from the business community or we've got business and IT fighting each other that's going to be a problem if we start to go ready fire aim which many organizations are prone to do that's a worse practice we solve world hunger or boil the ocean we're going to have a hundred percent of our data completely clean by Friday right the Goldilocks syndrome if the big one is too big and the little one is too little then the middle one must be right generally not a good way to do it if you've got people sitting at data governance meetings they're trying to figure out what they're doing oh my goodness that's a deaf sign for your project not taking projects through to fruition not dealing with change management which is a formal discipline assuming the technology alone can answer the question or not building sustainable processes and ignoring the shadow data systems that are there if you'd like a copy of that Hotel California a little video there just send me a note I'll be happy to send it out I really can't remember whether I put that together somebody else put it together but it's definitely a good little piece so let's spend the remaining ten minutes here talking about how these strategies work in action and the most important thing that you'll do as a data governance person is to learn to tell stories because if you can't tell stories about how data is working people will have an extraordinarily hard time relating to what you're talking about first story was a wonderful customer that we worked with and interestingly enough in their own words they would say things like getting access to data around here is like that kathleen zeta-jones scene where she's having to get through all those lasers I don't know if you remember that movie but it was a fun one with Kathleen date zeta-jones and Ogun de Souza British actors is so wonderful on these things but anyway she's trying to get through and steal something she's being a robber here but this was literally the language that they used and when we started to show management that this was how their knowledge workers felt they had to act in order to get data that's not what this organization wanted to have so it's a great example of taking in this case something that was really really near and dear to the organization they really wanted to do this very very well but they couldn't and making it practically focused so their data governance initiative was all around making sure they would eliminate those laser beams and they didn't have to be as live in his agile as Kathleen zeta-jones in order to do this another story in Detroit we were sent at one point to look at how Detroit did things I was part of the government of those days I was a federal worker work for something called the Defense Information Systems agency a say go off and learn from the private sector and one of the things we learned was that in Detroit they considered its success if they put something on an engine on an assembly line and it didn't slow the assembly line down well what that led to were three different bolts which means when we put parts on the engine we might put it on with this type of bolt in this part and a different bolt is another partner the third bolt in another part which meant that if you were going to maintain the engine you needed to have three different wrenches which means you need to have three different bolt inventories and rent inventories around and Toyota would do one step beyond Detroit's they would sit down and look back and say what can be harmonized and by the way the number wasn't three it was usually closer to dozens of different bolts in this case and of course to it and never simplified it on one type of bolt but you can see here that the process of thinking about this and adding this extra step in made their cars easier to build and easier to maintain and generally that most people think that's a good idea so trying to simplify things around that gives you again the ability to gradually increase the scope of your data governance efforts and put them into place programmatic level one of the easiest data governance projects I've ever done before was working with the US Army because the US Army looked around and they govern everything and they said oh my gosh something is not governed in the army we have to fix that right away so we use their data governance to implement an army policy which just said that we need to have in this case many many things that actually do work so again from a strategy perspective it was keeping it practically focused if we've got something that's not managed and ought to be managed yeah we can fix that pretty easily we were there as you can see from the date on this around ten years ago when we started the military suicide prevention project which is still going on and unfortunately we still have 20 service members daily that commit suicide but we were putting together the very first set of data that allowed us to do the analysis work around this and in order to do this we started working with what I called a council of Colonels and in this case a very very large 30 by 30 matrix so I want you to imagine this room here full of a bunch of Colonels and they're all very well-meaning people and they would say yes Peter you could use my data in column 12 for purposes number 10 number 14 and number 17 in those rows and if you've ever tried to manage anything off of a 30 by 30 matrix it becomes very unwieldy very quickly so one of the things I was able to do was request that the Secretary of the army come to one of those meetings I went Secretary of the army heard the third person stand up and say yes sir you can use my data for these particular reasons he said hey let's let's let's stop and rethink this ìletís right I'm going to put you guys out there a little solution we're going to call it my data and anybody wants to tell me why my data can't be used to save my soldiers lives my office door is open are we clear and everybody in the room said yes absolutely we're clear and we can take that and empower the team moved us along the way and we do have better ways of going in and now understanding more specifically what's happening around these we still need some more help so I'm not telling you that the military suicide problem is gone but it is absolutely a data governance problem and most importantly the gentleman who did this that I probably wasn't empowered to do that but did tell me that I was okay to include that story in a book that I was putting together at the time called monetizing data management and those monetizing capabilities were useful to hopefully be inspirational to other people now I've told that story directly in person to more than 100 ce8 gos of private corporations and not a single one of those CEOs has taken the courageous step of saying it all belongs to my organization it's my data and that has been an enormous enormous problem and an enormous disappointment and Norma's barrier to actually making progress in these areas another quick example here we had one organization that was doing lots and lots of work with tanks and from a governance perspective they were going to have to go back and modify their accounting package and instead we said let's not modify the accounting package let's talk in terms of specific types of tanks so we can talk about tanks that fly and thanks to swim and thanks that you put things on big guns and we'll talk about tanks too but that's another component of it each of these tanks then breaks out into hundreds and millions in some cases of data values but these data values three million of them when you buy a tank only one of them controlled obsolescence excuse me in the problem with that is that we found one of the branches of the service was actually maintaining tanks that were obsolete not a very good practical way of approaching this whole process again another governance perspective Barclays he may have called this from the oopsy that we had in oh seven oh eight when the economy tanked Barclays bought Lehman Brothers Bank and there were contracts that they didn't want to buy they said we're going to buy the good stuff we don't want to buy the bad stuff so we're going to put all the good stuff and the bad stuff on a spreadsheet and when we don't like something we're going to hide the rows of that spreadsheet we'll take the spreadsheet to the judge the judge will say everything on the spreadsheet good both lawyers say yes and say that's the contract right well they handed the spreadsheet to an associate the night before it went to the judge and after 11 o'clock they went through and I'm hid so unfortunately they actually ended up with the contracts after the sale closing having the wrong kind of exercise in this case I don't have to talk when I go to Barclays or Lehman Brothers at all about spreadsheet governance they understand the rules of this because they learn through an almost disastrous process one more quick example here very famous ones group called Mizzou ho Securities and Japan they had a real interesting thing where they had a Securities analyst who wanted to sell one share of a stock called J com and he wanted to sell it for six hundred thousand yen but he hadn't had his coffee and he sold six hundred thousand shares for one yet naive if you don't know the conversion rate you know that's a bad bad result there was a 350 million dollar loss on this but the governance examples were amazing the in-house system did not have limit checking one of the things you should do if you don't sell things for penny stocks is that you should look for the price of a stock and see if it's a penny and report it and put a break on it so maybe we don't want to let that one go to the market because after all the market may not and in fact this case did not at the time have limit checking and it did not allow order cancellations these are horrible horrible events now what we've done is taking a little bit of story telling in this we're going to take up just a step further here as we finish up give you a couple quick takeaways on this again data governance is need for its increasing because the volume is increasing and the practice improvement is not there it is a new discipline so we must conform to constraints that are in your organization and there is no one best way to do it must be driven by data strategy keeping it practically focused implementing it as a program and not a project and gradually adding ingredients and the reason we need to do this of course because most people think data is this sort of bat sign that sits there between IT in the business and reality wise it's more like this although quite frankly it's actually much more like this so I've given you a couple of references to take away on some of this and we now get to the part that's the really fun part where we get to answer your questions about all of those Shannon back over to you aren't getting myself off me up there here thank you sometimes it's a challenge this great presentation is so fantastic as always and if you have questions for Peter or for Jonathan feel free to submit them in the bottom right hand corner of your screen there to answer the most commonly asked questions we receive just to reminder for everybody I will be sending a follow-up email by end of day Thursday to all registrants with links Emma flies to the links to the slides and recording of the session so I diving in here and Peter way back when on slide 36 I know you always go so many slides is amazing I think you are the champ at gotta be good at something right Shannon yes as you are making assumption that there is a data strategy on slide 36 so what happens if the data strategy doesn't exist what do you do excellent question and think about it let me turn the question slightly it's a very good question the way it is posed most right now but here what we were talking about is that data governance has a lot of things that need to be done to make amends for past lack of attention if nothing else your organization has five times the amount of data that it needs to have and that's a huge barrier to productivity so the question is what if you don't have a data strategy well then you will probably try to implement data governance by looking at some of the slides that I've shown here and let me just go to my favorite one which is the one that has all four of those pieces on it all at once and try to do all of them without actually having any practice at it and it's kind of like me telling you that I want you to play electric guitar lead next week and you've never picked up an electric guitar in your life you know where where do you start so all strategy does is tell you what's more important than what else and that's what data leadership is about qualified data leadership should say yeah we need to clean up all of our data but we're not going to be able to do it by Friday but we might be able to clean up this sliver for this use by Friday and that might actually deliver value in which case I can then go back and ask for more resources now let me add another piece on to this the more successful data governance practitioners are people who are recognized by their organization that if you give them $100 worth of resources they will return a hundred plus dollars even if it's one hundred and one dollar right they're still getting value back out of it in fact I have one that is so successful that literally she doubles whatever she gets if she's given a hundred dollars she gives back two hundred dollars and I keep saying can I give you cash because we were both being a completely different business in that case Jonathan let me turn it over to you what do you think about slide 36 mmm so I I think the business of not having a strategy is quite common and quite often folks you know we need data governance let's set up this group and off they go you know for us we caught up and say look manage the data that's important to you so if you cannot buy that data to a key performance indicator to a programmatic initiative that's funded to senior management's objective you know goals and objectives kind of thing then it's just less important and generally speaking the data governance group has more work than these meets or can do given the resources so you know becomes part of your Rackham and stack and kind of selection criteria when when you're looking at the tasks that have been given to you but in general you know that's how you sort of separate the wheat from the chaff but in general quite often we find the data governance guys asking the hard questions you know what is that policy level leadership what direction that you want to give me to allocate my resources and focus and in doing that you kind of work backwards into you know forcing in some leadership it's not a good position to be in it's a matter of education because at the end of the day you need that you need executive leadership but that's how that's a common scenario that we focus on so let me ask you another question on that though Jonathan when you're talking to people about how to get started and actually do the racking and stacking piece we also see that a very wide variety of approaches in there and some of them are automated some of them are not automated many organizations think that data governance technologies can help what's your opinion in that area what's the proper role for a data governance technology how can automation help us I mean in this particular challenge here I mean obviously there's tools to help pass and so on but you know if you haven't nailed down what it is you want to do having a tool to automate it really you know that can just make a bigger mess than what you started with and it forces you into a level of comfort and you think the tools go to solve the problems but in fact you're just digging a deeper hole hospital so we talked about the framework the governance framework how do you want to think about all these components and how they work together and understand that a conceptual level thinks on conceptual data model is a conceptual framework model how do you all these pieces to talk to one another and then go about automating the tools so I popped up the Alice and the Cheshire cats true you know little analogy which is which road do I take and the cat says where are you going and she says I don't know and he says correctly then it doesn't matter if you don't know where you're going any road will take you there um let me let me ask you just to elaborate on before we go to our next question - cuz I know you had some thoughts on frameworks in there and I I don't I hope people don't I go through them quickly not because they're not valuable but just because there's really no much point in me taking up you know 30 minutes of discussion on them but but that you know or what I want organizations to do is to look at these frameworks and say what aspects of these frameworks are useful in my organization and how can I apply them how do you approach that process as well so I am a as you know sort of a big fan of the maturity kind of process and R is involved with the CMMI stuff so I kind of got a inclination in that direction but you know if you were one of your first steps as a set you know understand where you are and understand that if you're at a lower level of maturity you you can't focus on the high level right you've got to focus on those next steps you've got to build them out so the maturity models and those frameworks whether the maturity or not going to help you identify those tasks that have to be done and break them down to a level that sort of digestible right everyone wants to be best in class but not everyone talks about how you get there so I use the maturity framework anyway to sort of understand what those next steps are what might be expected to help me break it down I use the dim bought to you know that's a that's more of a reference model than a maturity model but you know to give you some insights into am i looking at everything have I missed something if I have missed something and I'd done that on purpose you know my organization just not ready for it those kinds of things to really frame frames of thinking and helping we start there's so much to do right so how do you narrow it down and focus it is amudha question what do I need to do first and what do I need to do next and you know all the rest of this thing let me take it a little bit further again we will come back to the rest of questions I promise you on this but this is really good stuff because even our dim box wheel here that we put together and Damon and I'm the past president of Damon still highly involved in the organization and I mean this with all due respect this diagram is decision in two ways that are pretty important to data this shows what are the aspects of data but I get calls here at data blueprint all the time that says the dim box says I need to do data warehousing well it's not what the dim box says the dim box says data warehousing is a part of data management but it's easy to see where you would get that impression because this diagram does not show optionality z' and dependencies other than showing data governance as the central activity so i think even this diagram can be improved on in order to come up with framework type discussions and there thanks for adding to that Jonathan and this is why we have a half an hour for questions in this presentation it was great I love the in-depth answers and I believe everybody else do so just you know I kind of going back backtracking a little bit you know and going back to click on data strategy what are the elements of a good data strategy should include priority scope operating model and implementation strategy and where and who owns it is it IT or the business we get that question a lot a lot Jonathan we go first davus karate righthand it if you know if you have an organization that is has set up a CEO and so and then that that's somewhat of a simpler question because they recognize the need there but you know who doesn't own it is IT in general I like to see come under the CIO if it's not if it's not spun out of of anything as its own component and you know but my votes for the CIO in general terms if you have the option but you know the very least that organization has got to recognize need for it and establish some organizational component that has a degree of independence and the ability to bridge across the organizational structure and I've seen it done lots of different places if you put it in IT you got to watch out because you know you need to control that but occasionally you see it in IT and sometimes that's just the way it is within various organizations I would say that one in ten IT organizations does a really good job of implementing data and data strategy correctly I talked to in fact that one of the gentlemen that reviewed the monetizing book that I mentioned earlier came back and said I don't know where else this function would resist you would exist as I'm the CIO I'm responsible for data I understand how this works and I said yep and you also only talk to people who you think are equally smart which means you're dealing with a very rarefied atmosphere and I challenge the individual to go out to the organizations to some data conference has come to a standard price data world one time and ask other people what they did and found that they were not as successful here so one in ten does it correctly but I also agree with Jonathan that's absolutely if you want to make good progress with this do not try to do this under the CIO because the CIO is already slammed there are all kinds of things that your CIO is paying attention to everything from managing vendor contracts to trying to make sure that software is delivered and implemented on time to make sure that we've got the hardware and software matching and you know have gotten through procurement regulations and implementations and lots of dependencies and optionality is in that environment as well and if you ask them to do more with data something else will start to fail in most organizations so fully half the CIS that I talked to about to say yep I got no room on my plate and I'm glad it's somebody else's problem now here you go good luck with it yes that chuckle is exactly what we're hearing on this and the other half I'm go in with my title CIO what if you take that away from me or your kind of you know I mask elating me if you will and that's generally a point that can become contentious for some organization so we still haven't tipped that but I also like Jonathan agree that if we can get it out of IT and report specifically into a CEO who is more financially driven or we've got right now as far as where CEOs are reporting about one third are reporting directly to the CEO but one third are reporting into a CEO or CFO or as chief risk officer kind of constructs and a third of them are reporting into CIOs we will need a couple of years to let that you know sort of percolate and we can go back in a couple of years and see what works maybe there's one answer or maybe for organization type X structure why is the right answer for it we don't know at this point but what we do know is that what has been working hasn't given us the results that we want and what has been done has been done what has been implemented in most organizations is the data has largely been an IT implementation project around tools without the discipline on it so Peter with respect to the components I would just point out that the CMMI data management maturity model defines data strategists specific way and this defines all the things that it influences so different different sources will provide a slightly different perspective there but I would go back to that webinar last month that sort of addresses that to answer that particular question or at least one view of that particular that question yep absolutely and a big adherent in that and Jonathan you'd have to be careful if you say her name three times she will appear so I have not yet said her name but I get it - you think they're listening for us no no we love melody absolutely Melanie Melanie Melanie and just PL no that is a link for past webinars to them on demand if you missed it last month so you can that will be included in the follow-up email so Peter specifically you know what did you mean by data shadow systems shadow data system shadow IT systems they are systems that exist in the organization that are not formally part of IT and many organizations consider them to be a problem I consider them to be a point of really trying to find specifically much more about where IT still has some room to support so I'm trying to figure out what slide that was but well when you walk into an organization you know I tend to be shown a lot of things right and one of the things I'll find is that there's an individual in the corner will say look IT doesn't know but I've got a sequel server implementation running under my desk because I need different kinds of support but the rest of the organization and I say to think soon don't worry IT knows you're there and they're keeping you there because they know you're doing good work with that piece and they can't support you in other ways but shadow IT systems can become a problem because we also have many many instances again Jonathan I'm sure you've seen this as well where an organization will make an error because somebody didn't understand that there was a data refresh problem that should occur they may have been working with data and then they find out oh my gosh that data I've been using is three years old because I didn't understand enough about the data that I was using and I was quite frankly unqualified to do this so shadow IT systems are systems that are not typically part of IT but that are out there in the environment and that can in some instances become problems and a lot of organizations tend to think that's a place we should go stamp them out I say don't stamp them out until you understand exactly what they're trying to do because an awful lot of organizational knowledge can be tied up to it Jonathan go ahead yeah no I mean I think it's all good points in the sense of you know they're not desirable but there might be a necessary evil kind of thing but just to give you a bit of a story I'm working with a group right now who has a number of this happens in the analytical space quite a bit right because you know an analyst is forever slicing and dicing and adding columns and doing all kinds of things so but in this case they're grouping together country codes and region codes and making their own groups so and to support a number of different business endeavors but they're doing it kind of on the side those reports are bubbling up and they're not necessarily matching with your general ledger in your financial activity right so at some point someone looks at the two reports and say these don't foot what's what's going on kind of thing and so that's an example of you know yeah we need to we need to bring those into some sort of governance so we can create a data service in this instance that provides the crosswalk look up look up table it's a reference data problem around country codes really and we can provide that service as an enterprise data management office it's highly value-added it hits some critical reporting that goes out and and we take the offload work from an operating unit to to the enterprise and leverage that across the whole organization so in that sense you know kind of a success story it's an easy win and the impact is great because that shadow system was bubbling out reporting that just created a lot of reconciliation one absolutely I was probably more information the questioner wanted but I think we covered it so my problem I I do that sorry very good no no you guys so just moving on here though I hear a lot of data centric presentations that acknowledge the importance of change management but not many of the offer guidance on how to successfully adopt it how it is why is that is it just difficult like why is that so you're hitting on exactly the question which is that the hardest thing for most organizations to do is in fact change change is all about doing some things differently than you've done them in the passed and there is a formal discipline called change management even my university Virginia Commonwealth University has a formal group we have about 50,000 employees we have about 50,000 people at VC about 15,000 employees in there and to get them to do something differently we need a group called change management that comes in designs ways of I'm going to be very cynical about the description making it more difficult to do the old way than to do the new way in order to do it that's one way to think about change management so what you're really doing is explaining the benefits of doing this and saying look if you think about how data is used after you input it you'll probably give the organization better instrumentation to fly the the organization on kind of a thing johnathan anything on that to transfigure now other than yeah I mean other than just it to me it's always been a component of the discussion and it's always been very sort of culturally driven so as you're as you're working you're operating model you talked about what should be in a strategy but part of that you know what's the operating model of the group it's got to accommodate the reality of that organization which day one might be very different from what you want it to be you know five years gets to that maturity discussion but you know I wish I could say for me it's not been very formulaic other than you know high empathy high engagement high communication and you know follow the best practices you know over communicate structure be consistent all those types of things absolutely key key to recognize is that if your organization does not possess this expertise that expertise is a necessary but insufficient condition for success in data governance because data governance is about doing things differently and if we don't give people rationale and reason and support to do things differently they are likely to revert back to the norm and that will not produce the results we have back to Jonathan's first slide your second implementation of data governance is likely to be the one what talks about strategy love it so storytelling is so important when rallying sponsorship and organization alignment to data governance any tips or tricks to share with the audience for how to formulate their stories well in 10 minutes I gave you a bunch of stories and I'm pretty sure everybody on this call could come up with their own story so practice again this is a discipline you can go off and take classes there's a master class same group that does the master class series on the web the webinar groups on how to do storytelling it's it's not a huge amount of things but if you learn a little bit about storytelling you will learn how to take things that happen in your environment and turn them into stories and when you do that then you start to practice them practice them on friends and family if they don't put you off and say you're boring you're probably doing an okay job with your storytelling stuff on the other hand I can tell you that if I come home some nights and tell my wife I got this great data governance story that we did this week she'll go and not till I've had my drink right then you can tell me this story so there are clues that can tell you whether you are doing storytelling well or not and it's a skill just as change management is a skill not that we data governance people should learn all those skills but you need to have them at accessible if you don't if you can't tell this again another pitch you'll hear a lot is the elevator pitch all right you get on the elevator and the 20th floor and the boss comes on on the 19th floor looks over at your badge and says Peter you work for me tell me what to do and if I stammer and stutter and don't produce anything valuable by the end the guy's going to remember Peter and put me on the list of people to get laid off the next week because clearly I'm not adding value to the organization on the other hand if I say yes ma'am the I'm the person who put in place the data governance constraints that keep us from sending the same bill to our customers three times in a row probably won't get me fired so you've got to have this ability to come up with a 30-second a three-minute a 30-minute and a 3-hour versions of the kinds of things that you're doing those by the way are key elements to a data governance communication plan another part of another seminar that we do but we'll certainly get to get to down there Jonathan any any guidance for storytelling well I think you know my particular thing that I watch on record is that you know stay away for the details at the end of the day we get a site data people get excited about things that normal people don't right and then you got to stay away from what it's like semantics and ontology and vocabularies you know people just don't want to hear that so all of the things that Peter said and just money on again complex and detailed and probably nobody cares except us which is good right we've gotta have somebody care about it but at the same time if you've ever wanted to get into a discussion of accounting practices and the way they do taxes differently in Oklahoma versus Idaho that's probably not your standard dinner table conversation so data is a very interesting topic and keep in mind this thing this guy's name by the way is Wally Easton and if you google him or so go out onto YouTube and get his clue he's really quite good at what he does but do you want your organization getting good at this I'm sorry it's just not the right place of knowledge workers right yeah I'm impressed that your wife is even willing to listen to you the story exactly well though she is a comptroller Jonathan so I get to listen to her stories as well so fair fair polite right goes around comes around again there you go permit election there we go I'm here I know yeah I think humanizing data fits with data governance or change management or don't you think the two things should be separated are you saying change management should be separated from data governance I sure hope nobody believes that change management is a part of data governance for sure but yes you think humanizing data is its with data governance or change management well no I would I would incorporate them in when I was with the Defense Department doing this type of work thirty years ago the senior executive on the project a woman named Mary Howard Harding Smith and I say her name reverently because she was one of my favorite people to work for in the entire world she said if we do this for something that nobody understands we'll be out of how to work in just a little bit so she said we're going to do this in a way that affects people and and again gets to that humanizing concepts in there if we again my little story about the hospital director right if we've got everybody using the default hospital admission code and we'll just say the hospital admission code default code is knee surgery and everybody takes the default the hospital director in a couple years looks back over the data and says wow we're doing lots of knee surgery I'd better reorient our knowledge skills and abilities towards knee surgery if I don't have knee surgery and you're I'm clearly not serving my community whereas humanizing that process would have been to say to the intake clerks that we're picking up the data on the way in you know you guys almost caused this hospital system to fail because you were telling everybody we were doing knee surgery we weren't really doing knee surgery so I absolutely believe tie it as tightly as you can to something that affects people's behavior and you will be able to get that behavior changed in a way that makes sense people remember what's personal right so do that and going back to the previous conversation about where where everything should sit so why do you recommend CEO instead of CIO and then and then Peter you gave your answer and Jonathan could you explain a little bit deeper as well defend your thesis sir all right yes so I think I was the short answer is that the CIO is shocked and maybe not the CIO themselves but further in the trenches are very much focused on a different set of priorities and deliverables and every organization I've seen where the data shop is being under the CIO or in the IT realm whatever they call that pyramid the the data work is important but not always is it's critically urgent as some of the other stuff so if the guy's got to keep the data center running or kind of rework some tables to make so that I can index and find data more effectively you know the data center running it always wins always and so you consistently come up with this difference in priorities that shifts the data work out and unless you control for that and there are CIO shops that are able to do that then the data style always falls behind and the IT shop always has a good reason for it falling behind because the alternative is that the data center went down or I'm going to pick that I mean that's a real story but you know it's always something right so I think that's one reason it's a very tactical and real in my experience the other one is that data is a bridging function there was a slide in here with the data in between the two jigsaw pieces and so you need people with that skill set that can really understand people process impact and enabling technologies right in that in that sort of example that there's four things and one of them is i-team right so in that context again it's a balancing act and I find that this CIOs shock just has a hard time of maintaining that balance in any kind of consistent it's always a tension right nothing to add exactly what he said okay we got three minutes here I think we can squeeze in one more question quickly both speakers if they would be available to communicate with us if we have sensitive projects I'll get you those if that information for both of you I'll send that out in the follow-up email I'm going out by innovate Thursday and good outfits it'll is the data asset registry how flexible Oh parallel thank you good much better work so how are you going to manage it if you don't know what you have I recently did a group within the Army and the data governance strategy for them was to say that look you were being given permission and and trust you're being entrusted to manage some army data do you know what that is and they kind of looked around the room said we'll you know I assume it's this and that the other thing because they deal with this set and the other thing and I said yes are you sure that's all of it and they said well I think so but what if the army is actually trusting you to manage why and you don't know that why is part of your portfolio so part of this is to formalize the process just real quick you will never complete your data inventory you will never complete your data registry we've never seen it I'm putting words in Jonathan's mouth but I think in 30 years in his case and 30 years in my case we've never seen anybody finish that so the important thing is not to have a complete inventory but to start the process where when you discover new piles of data that are lying around you have a way of registering them quickly easily and efficiently and so in that sense pivotal absolutely if you how are you going to manage it if you don't even know it's part of your portfolio there so many examples of what Peters talking about I'm not going to go into that but I had a slightly different twist with respect sort of if you do a capability assessment whether informal or just do your own and organization I don't 90% of time maybe but you know some certainly more than 80% of the time you're going to find that your organization has skills has capabilities with respect to data that people just don't know about again and again it pops up so your registry your catalog when it comes to data you need to kind of Google eyes your data is one of the things that we're talking about from time to time absolutely absolutely well that does bring us to the half hour there thank you both so much for this fantastic presentation very emotive and very fun and very engaging so thank you both in Santa Jaques for response during today's webinar as well to help make it all happen and I think there are attendees for being so engaged in everything we do and all the questions that have come in just love all of that and again as a reminder I will send a follow-up email by in a day Thursday with links to the slides list the recording also have the links to the past webinars computer though so you guys didn't look at the webinar that was referenced from last month and I hope you all have a great day thanks so much thanks John Sedlar to work with you and Shannon great to speak with you as always we love working with you guys so we'll let let them take a sentry read like eyes yep sure have a great day bye bye everybody you

Channel: DATAVERSITY

Views: 6,023

Rating: 5 out of 5

Keywords: data strategy, data governance, dataversity, dataversity webinar, data stewardship, data management, dmbok

Id: rX8VMqCEBCE

Length: 91min 19sec (5479 seconds)

Published: Tue Jun 18 2019