 Hello, and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining the latest installment of the monthly Data Diversity webinar series, Advanced Analytics with William McKnight, sponsored today by Elation. Today, William will be discussing analytics ROI best practices. 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. For questions, you'll be collecting them via the Q&A section, or if you'd like to tweet, we encourage you to share highlights of questions via Twitter using hashtag ADV analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so. To open the Q&A panel or the chat panel, you'll find those icons in the bottom middle of your screen for those features. And just to note, the chat defaults to send to just the panelists, but you may absolutely change it, the chat to network with everyone. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now, let me turn it over to Miles from Elation for a brief word from our sponsor who helped make these webinars happen. Miles, hello and welcome. Hi, there. Well, I was going to share the screen, but you'll need to make that possible. Oh, well, yeah, that certainly helps, right? It does help. There you go. Okay. And I'll put this into my show mode. Yeah, so, you know, what you're going to get to dig into in the next presentation is going to be how do you justify the investment in analytics and prove that out? I wanted to share a few things that we've learned from our customers along the way in terms of how they both use advanced analytics and the problems they've had along the journey and the value that they've gotten out of the process. So I'm going to start with Albertsons. Albertsons wanted to move to personalization. One of the big roads forward for digital transformation is to change that customer experience. So one of the things they needed to do was move from just sending out a flyer to somebody that might have some things on it that were interesting to a flyer that would actually be personalized to things that their buyers care about. And so what they had was they had to quickly do something. And they were told, you know, go find this file called RTPP. And they did the person who was leading this initiative. And it was it was an initiative that involved multiple countries didn't even know what RTPP standard for. And so what they were able to do is to go into the data catalog as a point of actually finding these things and quickly find out what RTPP was, what was it to find out what tables, queries and joins matter, and then start the process in the US and transfer it to the Philippines and document it in the catalog itself. And then they were able to get this delivered in a very quick timeframe, which was actually four days. So there was an ROI of being able to very quickly meet this business goal. And so one of the things I like to say, and you're going to dig into the ROI in just a little bit, but it's always good to sign the business ROI. Yes, there's time savings. And these people were able to save a lot of time savings. But the real outcome is on the business side itself. You know, if you can transform and use data to develop analytical models and actually deliver a changed outcome, that's a big thing. So one of the things we found, and this is actually from a Harvard Business Review study that Tom Davenport did back in 2017, is that 80% of the analyst time is actually spent going and trying to do the same things Albertson was doing. Where's the data? And then prepping that data. So and then finding out at the same time is that data trustworthy or it's not trustworthy. So if we can do anything to shave that time, it has impact. Yes, we can talk about analyst productivity or the productivity of the data scientists themselves. But if you can actually go and do something where you're taking a time out from the building of models, you can make your analyst stretch over a longer period of time, your data science stretch over a longer period of time. If you don't follow Kirk Bourne, or Boone, I'm sorry, is a really interesting gentleman. And he's also written about this extensively in the articles he's published. And he's got one of the largest followings I know in the tech community on Twitter. And one of the things he said that I thought was really interesting is that as much as we like to dig into the models and try different models and see what the outcomes are like, they oftentimes we could call the people who do this work better data plumbers versus data scientists. And so if we can make people more productive at this process, there's a real outcome at the end of the process. Whoops. So one of the things that we see increasingly is that we want to get people out there and working with the data in particular the data scientists as we've been discussing all the way along, who are really involved in engaging on self service. So when I've asked CIOs and CDOs, what do they think? What's the value if you could self serve over having somebody having to go find the expert? It's a shorter time to value just like we saw with Albertsons. There's lower business costs because you can stretch people across a bigger thing. It means you can attach to a bigger agenda. Business agility, if you see, again, what Albertsons was talking about, they wanted to move quickly. They wanted to use data as an advantage. And then obviously, you know, other things that matter are decision making and transformation. And so these are big agenda items that when you talk to CIOs and CDOs, they're trying to to get after. And, you know, we've really come up with four principles for doing this well. The first is that you need to fix the data value chain. You want people to be able to self serve rather than put a request in because the request can take time. And in many cases, the data already exists. So we can make it easier to acquire immediately the ability to determine whether that data is trustworthy and where if there are issues, there's issues with the data itself and then focus the data engineers on creating new assets. What you're doing is you're extending the IP that an organization has. So these were really some quick ideas I wanted to give you. Obviously, in order to self serve BI, you need to allow people to find stuff directly. Just as I said earlier, like Albertsons, they need to be able to discover it's current and ready and all of those kinds of things. And one of the nice things is catalogs enable you to do this and relations catalog in particular makes it easy because you're using natural language and then you're able to look at queries that exist, sometimes modify them and then address the data quality issues directly as you discover the data that you like. So that's what I wanted to share today. So I'm all done and I will stop the sharing and look forward to the Q&A, which will happen in another few minutes. Miles, thank you so much for kicking us off with this great presentation and thanks to Elation again for sponsoring today's webinar and helping to make these webinars happen. And now let me introduce to our speaker for the series, William McKnight. William has advised many of the world's best known organizations. His strategies form in the information management plan for leading companies in numerous industries. He has a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks in leading database, data lake, streaming and data integration products. And with that, I would give the floor to William to get his presentation started. Hello and welcome. Hello, Shannon. Hello everybody and thank you Miles for leading us in here with that great presentation about self-service and that's one of the things that does factor into ROI as hopefully you will see throughout my presentation. I have some lofty goals for this presentation for you. I trust that my screen sharing is working fine. At the end of this hour, I want you to be able to navigate an analytic justification. So if you're being asked, what is the ROI on that project? Or should we continue with this project? Things like that. Or if you're forming a project that you want to propose within your company, you're going to want to think about the justification of course, going to want to think about the ROI. So today's focus is on ROI. It's on analytic ROI. It's on those projects that are clearly analytic projects, but it's also on the analytic components of projects that may or may not have a substantial analytic component to them today. However, I will assert that most projects are going the analytic way over the course of time and must get there. So right now we can talk about analytics ROI, but sooner or later it just becomes ROI of the company. So I want you to be able to distinguish between tangible and intangible benefits. I think a lot of times technology folks, people even like myself, you know, we can tend to get pretty excited about what others may not get excited about. You know, single version of the truth, clean data, things like this, those are intangible and I want to talk about that. If you're not going to measure it, it doesn't really factor into a strict ROI. I'm not saying it's not part of justification, but it probably doesn't factor into a strict ROI. I want you to be able to present an itemized ROI and articulate the value of analytics within your organization. Just generally speaking, why do we want to do this thing called analytics, which I will define as we get along here. And adapt the methodology that includes ROI attainment and measurement. So you're going to be able to show where the ROI is going to come from and then measure after the fact. Now these are some of the projects that I've been involved in in the past few years helped to justify these projects. Some of them are pretty big and just wanted to kind of put that out there. And so I know my way around getting projects justified in the organization is not something that we're ever trying to gain. It's something that we're trying to do because it's going to drive the ROI of the company. And I think a lot of people know that, but then they look at the mountain in front of them in terms of getting the thing justified. So if we take it step by step, we can get there and we can get down the path of doing the things that are absolutely essential to us as a company. We're so often just paralyzed by things that we shouldn't be paralyzed by. So hopefully I'll give you a nugget or two here today that helps you move that ball along and don't forget that we have to keep two hats on as we go. The architecture hat must stay on as well as the business hat and the architecture hat. By that I mean we're not doing damaging things to the company in the long term. We're actually making it more possible to get things done quicker and more efficiently after this project is done because we set up the future. That is good architecture. And we're also driving ROI and I know it's a balancing act and somebody or some buddies has to be able to put both hats on almost at the same time sometimes. So you've got to know right now am I wearing my architecture hat or am I wearing my business hat and really be able to go back and forth. Next slide okay let's define analytics. A little bit here whoops there I am. So I often get this question. So what are analytics? Is that a query? Is that a project? Is it a philosophy? What is it? So I say it's the process of utilizing data to enhance business processes. That's really it at a basic level. It's deeper though than simple knowledge. It's not select and then get doing a point query selecting a customer's information. It has depth. Now you might select something like customer lifetime value. That's something that the customer has accumulated over the course of time. There may be their origin to current spend levels. Now these are things that have depth. You're not going to find the answer to those questions in a single record in a database unless that record has already accumulated other records. And that's what I mean by depth. So you can either accumulate derevisions and calculations within your data or you can do that at runtime where you're pulling unique and advanced analytics. And that's all fine. It's all analytics. There's analytics projects. Projects that are really geared to improving the analytics of the company or improving something within the company whereby analytics are so ultra important to that goal. And I'm going to give you several examples as we go along. And then there's also analytics that are added to projects. And today a lot of us are thinking about adding machine learning and artificial intelligence to projects where it doesn't exist today. So that might be your cue in terms of how you define analytics. This kind of depends on where you are. Measuring is important to business. And this is a quote I've been dragging around for a while. The essence of a corporate culture is the firm's measurement system. It is the lens through which reality is perceived and acted on. Yes. How we measure the things of the company is quite important. So there's a lot of things we can measure. The bottom line is the bottom line but we don't all work on the bottom line of the company every day. So we have to figure out some things that we can work on that are important as well. So hopefully we get there a little bit today. I'm going to, before I get into the math and maybe some of the justification and show you some examples of analytics, I'm going to acknowledge the strategic as a place for learning and innovation. And I'm going to acknowledge that not all projects within the company by a good margin are looking for Nichols and Dimes within ROI calculations. A lot of them, a lot of the initiatives are for learning and innovation. It's to target the unknown upside and this is all for better or worse. I'm just talking about reality. The intuitive thinking employed by the highest paid person's opinions, these are definitely drivers within the companies. Although I will say I have noticed over the course of time that things are moving towards more analytics style and ROI style type of projects especially as we start to become more mature as a company with having all the projects in place that need to be in place now we're trying to optimize on a lot of those initiatives. So let's talk about the workloads because we have to be able to divide our work up. I'm going to use sheep as an example and fences as an example all right. So there's an art form here inherent within this slide which is how do we put the fences around the workloads and what do we combine within a workload? Is it data? Yeah sure is it processing? Is it people? Are we tying this out to sales? I mean what all is involved in a workload and we have to know because we're going to try to target some ROIs here and we're not doing the ROI of the company we're doing the ROI of a project or maybe something that we don't call a project but it's an initiative within a project like adding analytics to the supply chain okay something like that we have to know what we're targeting here we're not talking generally we have to get specific if we want to do ROI. So how do you prioritize efforts within the organization? A lot of us are really hamstrung by this we're paralyzed we're looking at dozens of possibilities and it can be very paralyzing today but I suggest to you that there's three basic things that you want to think about in terms of prioritizing efforts sure you can't do it all at once even though it's all great right? For one thing there's a bit of a technology people shortage today so that's going to that's going to limit some of what you can do as well as company cultures are not necessarily all about doing everything at once but it's more about prioritizing and metering out the benefits to the organization over time so the first thing I look at is easy to do if it's easy to do I want to do it yeah and easy as a relative right compared to where you are as a company in terms of maturity of projects and that sort of thing but if it's easy I want to do it if it's a prerequisite if it's going to set up something down the for the future I want to do that too I call that a prerequisite project it has to stand on its own two legs yes but if it also helps to set up other projects that are maybe bigger and more important than great you see the art form inherent within doing this type of thing and then finally ROI very important um so ROI is return on investment it is about cash flow it's about driving things to the bottom line of the organization so the next time you see ROI as a request I don't want you to cringe and I want you to say that's impossible as a matter of fact it's quite possible that whoever's asking you for ROI it's just sort of putting you off for the moment and maybe they don't even have a great idea as to what they mean by ROI but you're going to know after this presentation you're going to know how to come back with something that really will move that conversation along I would think quite a bit and maybe you find out at that time that oh we didn't want uh we didn't really mean cash flow or we didn't want ROI we wanted internal rate of return or the break even point or something like that that's fine that's fine you got the you got the conversation moving and a lot of this is really easy once you do the hard part which is figuring out what the cash flow is going to be we'll get there though then what makes it difficult is it's ordered benefits we're not just selling subscriptions to say a data warehouse or some data set that we're collecting and refining and curating we're not just doing that well maybe we are but probably we're not doing that probably we're trying to improve the supply chain probably we're trying to do targeted marketing probably we're trying to do predictive maintenance okay these things have uh an indirect relationship to the bottom line so why would you want to do predictive maintenance well I think we know you know we want to save money on parts we're going to keep the planes in operation or whatever it may be and that has a a direct effect on the bottom line but it's still you know a little bit removed and some things are quite a bit removed that's what makes it really difficult but you can't just walk away from it because it's like that if it's good for the company and you know it find a way and hopefully I help you a little bit with that today we're going to talk about program versus project justification you've got to know which one are you doing so a lot of times the program justification is typically going to be done on a lowest total cost of ownership basis you don't have to worry about how the 10 projects are going to use the data warehouse if you're building a data warehouse let's just say you do have to worry about how you build that data warehouse and you want to build it to scale you want to build it less expensively than the 10 other ways of the hundred other ways there is to there are to build a data warehouse today for example and a project more or less does have to stand on its own usually is done with ROI or some form of ROI so if you're justifying a project analytics might be seen as a standalone project now everybody has their own terminology and that's why I say things like this because I don't want to alienate anybody in terms of how you think about what analytics is what a what a data warehouse is what a data lake is what a this or that is because keep in mind that I or other people may have different definitions of these things so I say that what I've heard is that analytics can be formed as a quote unquote project within the company and even things that are as I've noticed over the course of time things like supply chain management which I do a bit of supply chain management used to be called supply chain management and then it's sort of morphed into some other things and now some people out there calling it analytics because it's so it's of course we do supply chain management but we're doing analytics over the top and very intensely within this project that we just call it the analytics project maybe you're justifying an analytics program which is sort of a let's say there's a corporate wide initiative to do more analytics to do more to bring more data to bear on how we operate as a company and so there might be something centrally that you're working on that you're going to roll out so to speak to the organization maybe one project at a time but for example analytics store enterprise data like a data warehouse maybe or a day late inclusion of data into that vessel whatever it is is based on governance hopefully and the goal of the program is to enable the component of the applications that it supports so we're not going to be responsible for the bottom line of all the 10 to 100 uses of the analytics that we build here but we are going to be responsible for how we build it I hope this is making sense this slide will help hopefully a little bit what is being justified an information management program or an analytics program which will store data for several projects so there are a question a question might be why use the data warehouse architecture versus just letting people do what they want out there at the department level and building data marks independent of whatever else has gone on before and why don't we just do that how about a project maybe you're trying to just fire a project which will use the data store to store data why do this project and the inclusion of new projects into an existing data store program let's say you have a data lake so should you put the data for this new project in the lake you have or build another one or put it in a relational database so the question becomes really why architect this project into that data store instead of building an independent data store so you got to know why you're doing certain things now obviously that last question is more of a TCO question isn't it it's not a question of how many more sales are we going to get or it's not a question of how we're going to reduce the expenses of the company it's about how can we spend the least to get to our get to our goals that's a TCO question which is very related to ROI but let's talk about ROI first it's just simply return minus investment over investment should always be supported within a time period if you tell me if I go to the bank and I say you know what's my rate of return here going to be on the savings account and I hear 5% I say great 5% a day that's wonderful oh no 5% every year actually that's probably a stretch today isn't it you see my point it depends on the timeframe ROI should be presented with assumptions and risks and be itemized because we're not sousayers here right we're not predicting the future completely accurately we're not saying we can but there's different ways that we can cut the ROI to say okay if we use these source systems these subject areas this business problem solved this number of users this blah blah blah probably 10 things then we should get an ROI of such and such and what you're doing there is you're forming the project because every project can go 100 different ways right you can you can say okay the project is going to be 10 source systems or you can say no the project is going to be the first source system so which is it that you're justifying here the ROI on the 10 or the ROI on the 1 it all depends on how you want to approach obviously the bigger the better you won't have to come back to the well maybe if you're if you're lucky but most likely you're going to have to roll things out on a smaller basis than multi a multi-year project you're going to have to show where the ROI is in the first let's say year and how it's going to build over time after that and you can use ROI for predicting and measuring your analytic success now just a word on self-service which Myles talked about but it's one of the many things that plays in here because if you do things right you are driving ROI self-service is a great example of this it's not just about TCO you might look at self-service and say well we're disintermediating IT out of the way in between us users and our data and that saves money okay yeah I grant you that but I've had this philosophy for years and I haven't done any measurement on it I'm not sure how I would but I believe that users have a limited window in which they do their analysis which dictates the depth of analysis if you have dirty data like Myles was talking about you know the Kurt Born quote all right if you have dirty data that and a bad architecture that your users are slogging through and they're really data janitors and not data scientists okay that's a problem because they're not going to be able to get very far they have a limited window in which they're going to analyze but if everything is readily available you've got a catalog there like Elation explaining what's in there they get it they're not having to think about these basic things that you've built once and you're using many times now as you do with a catalog well then you're going to be able to go deeper as a user you're going to be able to go seven levels deep let's say two and then you're going to get better results now what are those better results mean well there's a hundred things that they mean right but if they're in the supply chain area come back to that they're going to be able to improve the supply chain with that deeper analysis theoretically but also I think pretty realistically so these are some things that people tend to lump into analytics now I got straight analytics in there but I've got these other things in here in terms of domain so you might be building a data warehouse or building out your data warehouse same thing with your lake master data management is really hot right now maybe you're doing that maybe you're doing what I've called analytics here today maybe you're doing customer relationship management et cetera all right so I'm showing you here what the approach is for typically justifying those projects and I know I've got the word or in there a few times you're going to have to continue to think about some of these but a data warehouse let's just take the first one as an example should I build a data warehouse well are you building is it a project are you building it for a project are you helping that project is it just part of the project expenses and project benefits or are you building it kind of over here on the side you're going to build it and they're going to be your first customer there's going to be more and then the question becomes you know the question of the first example is what's the ROI of the project we're just part of that the question of the second example is why should I build it as a data warehouse why not just slam something together as a quote unquote data mark for this project okay I know that's kind of an old example we like to think we're past it but we're not really but there are definitely more modern examples within this slide and I'll go on because there's some examples here these are TCO examples and I'm not saying don't ever use ROI on this stuff I am saying though that what you really want to do here with these questions which we're all fielding right now by the way what you want to do with these questions is show the alternatives and the cost of the alternatives and the long-term cost of the alternatives is the cloud worth it well that's a big question we could probably spend a good a full session on that right should I do a data mesh I'm getting that question a lot today what should I use for the story to layer the platform you know which spec of AWS should I use for this project should I use big data cloud storage or no sequel or should I use rdbms what should I use there is master data management worth it that's that's a TCO example really because my philosophy on master data management which I talked about in a previous dataversity is that we're all doing it we just may not be doing it very well so to do it the right way is a sort of a centralized approach and why should I do that and so I went through that in that in that webinar you can go find it but and I've probably covered a lot of these actually I haven't done data mesh yet we'll talk about that later but there are variations on the ROI theme payback period analysis return on investment net present value internal rate of return now I'm not going to go into the math of these things don't work and don't you don't have to get to get your calculators out now but these are simple formulas in excel you can figure them out but the hard part is coming up with the cash flow and when you come up with the cash flow I suggest you come up with different possibilities and know that as you come up with the different possibilities for the cash flow for the ROI of your analytics project that people will hone in on one or the other and I think as I put these out here you can probably think about who you know which which profile that Marion finance is going to hone in on and which profiles the CEO going to hone in on etc it kind of a lot of it depends on where the company is in terms of spend now I've been doing consulting for 24 25 years so I've seen a lot and I've seen companies go through we're spending a lot we're we're going after things we're we're type a you know we're we're knocking it out of the park we're going to double down etc I seen them go from that to whoa we're pulling every pulling all the reins in we're slowing everything down we're looking at worst case we're thinking about worst case in a matter of a month and then back to the back to the former in three more months and this this happens maybe not on that type those type windows but this happens a lot so kind of depends on what stage your company is in are they going to hone in on your best case that you present your plan case which I suggest is in the middle and the worst case most everything goes wrong and I think it's perfectly okay to kind of show that if most everything goes wrong whatever that means to your project that you may have a negative return on investment it's only realistic now keep in mind that Marion finance that everybody in finance they will tend to zone in on that and say well this is what's going to happen no I didn't say that's what's going to happen I've got these other cases but we got to be realistic so let's talk about tangible versus intangible metrics because if you're going to show ROI you have to show tangible intangible doesn't work tangible are returns that you decide to measure many more activities have a measurable return than you may think you might have to think hard you might have to think deep remember my example of predictive maintenance it's it's a few levels but it's there usually one to two returns are reasonable to measure for each project don't ask me to measure 10 returns on a projected project I'll be here all day and then some all month really or two that's not exactly reasonable the first two are going to be the big heavy hitters and then the benefits tail off from there that's been my experience so I'm just going to measure the one or two and I'm going to say this is probably what you're going to get intangible returns are simply the definition is return you decide not to measure I had somebody want to justify a project once on and this was quite some years ago as you'll know from the example but on the paper say because we won't be printing we'll be going and we'll be interacting with the data on the computer well I said that was way too small potatoes we can't do that it's true but we can't do that that's not going to justify an analytics effort spend of a million dollars okay so so much for the paper let's let's keep going I decide decided not to measure the paper I could have and it would have probably shown a little bit of return but I decided not to measure the paper there are other more important things let's keep going there's some other intangible benefits here that we like to throw around and think that well this is the end of the story okay Mike drop I said clean data Mike drop the the organization is going to go for it not quite not quite in my experience single version of the truth improve customer satisfaction we're getting a little closer with that one maybe enhance the corporate image more time for staff development faster response to customers getting close there we get a data mesh I can tell you at some level of the organization these things do not cut the mustard so let's uh let's let's note them and enjoy them and measure them if you want but they don't really go into any real justification what does go into real justification all right that's what I want to get at here I definitely want to leave you with a lot of good things that you can hopefully find your way into now here's some examples increase in sales that's number one that's number one if if I can do that for a company with a project wow I'm going to get I'm going to get funded I'm going to get funded if I can create efficiencies and processes if I can drive out inefficiencies in other words if I can reduce inventory if I can reduce returns or fraud or any form of computer security if I can enhance it if I can meet service level agreements that we have these these are the this is slide one of tangible returns stop right here hopefully you can find your way within these things and by the way this brings up a point that I want to make here mostly I get data and analytic professionals on a webinar like this we hold the keys to our company's success in the future but we're way too often not a sort of enough with that information we think that we're on the receiving end of what the project should be I think we should be on the on the front end of what the project should be because really as I look around companies only we know only we know about artificial intelligence and machine learning and what it can do so it be who's us to think about what those things that we know about can do for these important things within our companies increasing sales creating efficiencies of process and so on so don't let the projects be formed without this great information we have in a partnership with our business peers within the company and I know that's gotten a little bit harder with everybody working from home but we got to got to find a way here's some more well actually let me drill in on how analytics increases sales because I said that was the big one right so let's talk about it a little bit more predict and prepare for likely sales if we know they're coming we're going to be able to take them we're not going to be waiting for months to make a to make the sale and then it goes away we're going to be ready for it that might mean people power that might mean something to do with our processes being ready our customer service being ready in a certain way but if we know what the sales are likely to be not in general but from a customer set from a certain customer what have you then we're going to be better off identify customer product likelihood to purchase so we're not wasting time on pushing products to customers that they're not going to buy because it just doesn't match them we're going to be better about the matching when we have more information more analytics remember that's depth of data we're going to be more better more better more better in terms of identifying customer product likelihood to purchase we're going to be better off in sales because we're going to be more efficient about retain customers trending to attrition well how do we know how do we know that they're trending to attrition by the way every company is different in terms of the profile of the customer trending to attrition maybe they're pounding on the customer support maybe that doesn't matter maybe that's actually a sign that they're sticky you know you've got to look at really what it comes down to for me is you've got to look at who has attrided and what were their patterns and let's bring that back and look at some of the early stage of that pattern within the patterns of our current customer set and start to determine what we're going to do about it and we probably don't want to keep every customer because some are not profitable or some we're going to target heavier than others I'll put it that way so we can't have one thing that we do for customers that are going to attrition we have to have 10 things that we might do for customers that are going to attrition give them a new phone if you're in that if you're in that business free minutes coupons etc support customer engagement activities segment the customer base actionably not randomly or because the book said to do it this way or that way but segment them in a way that you can actually take action can you segment your customer base according to their size made and that that might determine who within your company supports them in a better way something like that obviously optimizing pricing increases in sales there's other ways but I wanted to show you some of the next level ways that analytics can improve sales which I've said is pretty important now let me get into some more tangible returns remember I want everybody out there today to find their way within one of these tangible returns that I'm sharing with you today this is slide two of them your project today that you're working on it's hopefully in one of these and I bet it is if you don't if you're saying no it's not I I I bet it is you just haven't thought it through to this level so I would I want you to have the walking around knowledge of what is the ROI of the project I'm working on today I mean put that on a sticky and put it on your mirror your proverbial mirror right because that's important that's what you're all the whole team is driving towards today and every day until it happens right it's that goal whatever that goal is so you should have a goal you should know why you're doing what you're doing or somebody should if it's not going to be you actually I think everybody should but somebody should at a deeper level back to the slide okay procurement savings savings in material costs for processing saving some volume purchasing I won't read them all there you get there you see some more hopefully you can see see how analytics is going to improve these returns to use the ROI you have to have tangible returns it comes from these things it comes from saving on downtime saving on cost savings cost that is time to market saving on cycle time maintenance costs production volume optimization capacity utilization saving on the number of defects etc so and there's also things that are maybe a little bit more removed from tangible returns but there's still there's things like net promoter score now for some of you now we I think we know what that is right that's a customer measurement of their happiness with us as a company right so for some of us we can say we've we've measured how net promoter score goes right to the bottom line goes right to the bottom line for others of us we have not been able to do that so it's going to be a little bit more removed sorry we're going to have some dogs barking here things like net promoter score things like average deal size we can drive that up the revenue by campaign revenue by channel the churn rate customer lifetime value by segment acquisition acquisition and retention cost by segment all these things are tangible and we can hang our hat on them and here's some more examples increased revenue per customer increased customer acquisition okay i'm back reduced cost of marketing campaign sorry about that decreasing customer attrition improved employee productivity hmm some of the things are a little bit more removed from the bottom line but they all are not so far removed that I would call them intangible by any stretch now let me give you a real example okay this is in healthcare let's say we're let's say we want to analyze our claims and reroute claims to best of breed providers in the network so if someone's having a coronary artery bypass or c-section or something like that we can make sure that they are matched with the best provider for that now let's say we do this successfully we're going to be able to attract more customers if we do this right I think that providing good services around these things would be something I'd be interested in in my healthcare provider so we're going to be able to get more customers yes we're going to say in year two just arbitrarily I'm going to say year one is however long it takes to actually do the project but in year two we're going to attract 25 new customers in year three we're going to attract 30 new customers now the average customer premium let's say it doesn't change it's 125 thousand obviously we're talking about corporate customers here the percent gross profit from the customer premium we should know that already we should know what that is and plug it in here now obviously I'm a little bit high level here we would want to do this with some more detail but nonetheless that's what it is these are the total customer benefits in year two I hope to get 625,000 more in premium if I do this in year three 750,000 more in premium if I get that great premium is great right but let's look at claims saved if we're actually doing work better within the network we're going to be able to save on claims nothing in year one but in year two save about 100 claims year three we're going to save about 150 claims average claim size $25,000 total claim cost savings you see it there and then you add the two together that's the total impact so about 3 million in year two about four and a half million in year three that's driving right to the bottom line and so some people will ask at this point well what about year four what about year five yeah it's there it's there but don't get carried away these projects do have a life span and then we got to we got to do something better I would say at that point now I'm really believing that these projects are going to be around and supporting the company and driving to the bottom line for a good 10 years but still we're going to be limited in what we can propose in terms of a time frame for the benefits for a project like this and then we have abstracted ROI which is impossibly measurable at the individual level we cannot ever figure out at an individual level individual customer level let's say how what we're going to do is going to impact the ROI of that customer but we know that if we take a tranche of 100 customers that's going to affect say 10 of them and those so we can do it at the group level these are things like improving customer lifetime value improving which spend that style customers are in or indirect measures like I talked before about net promoter score net promoter score is going to if we can improve that let's say for our middle death style of customers that's going to improve the profitability of that middle death style and their profitability is unique versus the other nine death styles if we've broken our customer base out in that way so this is what I mean by doing things at a group impact level how does what we're doing work on the group and we can use that in our measurements so in summary here is the methodology cost analysis I didn't really go into that by the way if you want some cost analysis for analytics platforms with machine learning today I highly suggest you take a look at last month's diversity webinar because that was the focus of last month and then we have tangible benefits analysis notice it's tangible benefits it's not intangible benefits it's just the things that we've decided to measure and then we match the two together that's your cash flow we put it into the formulas that are appropriate for our company we put them on a probability distribution best case worst case plan case we list the intangible benefits but we're not going to hang our hat on it but it's there let's list them out and then that drives into our prioritization and our planning decisions not that we are all going to do all all all of the bullets here right we may stop at we're providing it all the way through the intangible benefits and then somebody else gets to do the prioritization and planning or we could take a more leadership position and say we're going to be proposing etc recommending on that last bullet how to prioritize how to plan and take it all the way remember if you come with reasonable approaches you are way far ahead of the game if you're not given the approach like an A to Z cookbook of how to do this then write it yourself write it yourself justify the project in your own way prioritize the projects in your own way and give it a go and deal with the feedback that comes from something like that much better than sitting back and waiting and doing nothing all right so in summary for the whole presentation target the business deliverables of the project use a repeatable consistent process using governance I didn't really talk a lot about governance but I think it's pretty important it's pretty important to the rules the data rules that go into a catalog like a nation but it's also pretty important for helping to understand how we are going to justify projects in the future that whole deal of justifying projects is something that if a company can get really good at that and can get really good at designing and forming the projects they are so far ahead that is really something that we try to work on with all of our customers because that just it helps us get projects done and it helps the company ROI is an important component of justification but not the only one I've shown you some things about it here today but it's not the only one use lower TCO for program justification so remember you got to know if you got the project hat on or if you got the program hat on and if the program hat is on that's a way of doing things that's the architecture hat so that's more of a total cost of ownership I could do it this way or I could do it one of these other ways here's how here's how much it's going to cost to do it one of these ways isolate project benefits and costs that's the that's the slide with the sheep right that's putting the ring posts up around the sheep that's herding the sheep into their projects you've got to be able to do that as well if it's important you can measure it and you can improve it and the project will almost always pay for itself so if a project should pay for itself you should do that project however there's a there's a system within your company that you have to exceed a threshold and that threshold varies according to company temperament at that time I talked a little bit about that and finally even though I've given you some steps I guess I've given you some bath judgment is essential judgment is always essential not taking anything away from strategic projects not taking anything away from judgment with this presentation you got to keep that on throughout and that is a summary of the presentation I've been William McKnight the president of McKnight Consulting Group and I welcome you to submit your questions for a little Q&A that I'll bring Miles back in now for I'll turn it over to you Shannon for the Q&A I am thank you so much thanks to both of you for this great presentation just to answer the most commonly asked questions just a reminder I will send a follow-up email by end of day Monday for this webinar with links to the slides links to the recording and anything else requested throughout lots of questions coming in if you have questions for either Miles or William or both feel free to submit them in the Q&A portion so diving in here how do I show ROI when my team is foundational such as providing the catalog so that people can find the data quicker simply current time compared to reduce time I'll start on that one I think that's one component that you can certainly measure but I don't know if that'll win the day I think you have to do a little bit more there if you're just going to say that well people are going to be able to get their data faster I want to know more about pick some examples of how that's going to help so what how is that team over there getting data faster how are they going to be able to drive some ROI with that information and I'm not talking about you know we don't we we need less person power to do this work if we have a great catalog in place but I think a great catalog drives a lot more than that I think it drives more benefits to the organization in terms of getting people enabled faster getting them getting them enabled at a deeper level so this gets back to the slide the conversation that I had about driving in on depth depth of analysis and I think that's where you find the ROI and things like that where's the what is the depth of analysis without it and what is the depth of analysis with it and when you have that depth of analysis what more can you do how will that improve the supply chain the this that and the other thing that you're really driving at you know in terms of your benefits so I'll add that the way CIOs think about this is that there's a fundamental change going on in organizations today historically you know we would make decisions by a little bit of gut feel and a backward looking report today where people are trying to do is to decide faster to drive more agile organizations and in that world what you want to do is you want to enable people to directly discover business users to directly discover data sources to immediately be able to assess that that data is of sufficient quality for a decision and make decisions at the speed of today's business now there's outcomes like you've seen today from Bill around you know you're fixing the supply chain or you're enabling as Albertsons did to actually have a more responsive you know more a sective set of customer engagement so the outcomes are going to come from that but the big driver of this is just the speed of business and the fact that if I go to a request model I don't succeed now there was a question in the chat I just wanted to go after quickly that had to do with data quality obviously there's a lot of techniques to cleaning data the biggest thing is that you need to be able to discover where data has quality issues now you can do that through collaboration but you can also do that through data quality tools and what they try to do in a mix of automations and things that you create is to create metrics that are going to tell you gee is this data trustworthy or not and so and then obviously then you may have to go back and remediate that data to make it fixed but the first thing is knowing that the data is trustworthy for decision maker whether it's an analytic model being created or simply I've got a metric and I'm trying to make sure that we're delivering against it I love it thank you Anne I'm going to see if I can slip in one more question here any thoughts on ROI in a government context where bottom line is it always emphasized? Yeah I get that question a lot when I when I give a presentation of this form I think you just have to target something else something that may or may not have bottom line spend and profit related to it however before I just completely absolve the government organization from ROI I will say that I have noticed that they've gotten quite a bit more I'll say progressive lately in terms of looking at things like ROI and so I wouldn't necessarily say that if you're in government that you don't have to care about this at all maybe the questioner is in government and knows that this is true that they really don't have to so there I would say be progressive be the one that does care about it be the one that does show it you don't have to obviously if it's not part of if it's not a huge part of the justification great you've done it you've still got something that you can focus the team around in terms of where you're going but I think there are definitely in whatever we do there are metrics and the idea here is to find the metric that's important and drive the team towards that putting that on that proverbial sticky on the mirror and driving the team towards that metric whatever it may be you know I just add that you maybe you're not talking about rural rates or something like that in a government context but I think there's this notion of stewardship that you think about in government and and when you're a good steward you're using the resources as efficiently and effectively as you can as well as oftentimes if you're able to be a little more efficient it means you can take on a bigger portion of the mission of the organization and maybe help deliver that more effectively yeah I love it so just a very quick elevator pitch we got you know just a minute here can you provide more insight and cleaning the data and how does it affect quality assurance so I actually have a whole presentation on this that I believe I've given in this series at some point in the past it's titled something like data quality return on investment and there I go through the process of talking about how you can have different levels of data quality that will impact the bottom line of that particular project in different ways and you can find the point at which it makes sense because obviously you have to put an investment into data quality it's not free so you have to put that investment in you can put in different levels of investment and get different levels back but sometimes you're past the point of diminishing returns as we say with data quality investment however I would say most projects if not all projects really do need some measure of data quality they have to be working on quality data just to be effective period and beyond that though we can get into you can you can achieve different levels you're never going to get 100% but you can achieve different levels with different levels of input and it's all about showing how the different levels of data quality drive to the bottom line of that initiative let's say it's targeted marketing you're going to have a better mailing list let's say that means more people are going to get it which means more people are going to uptake it and that sort of thing so that's always there it might just be a little bit a little bit removed depending upon the project and I'll just end with a humorous story and that is that there was a large consumer goods company that I met with several years ago and they very explicitly wanted the business to understand how bad the data was so they didn't suggest data quality initially and then they they gave the business the data and the business immediately understood that there were quality issues and and they said oh well that'll be some extra money to your program so it was kind of funny that they let the business discover on their own how bad their data was sounds good perfect I love it thank you both so much for this presentation but that is afraid I'm afraid that all the time we have for today again just a reminder I will send a follow-up email by end of day Monday with links to the slides links to the recording thank you both so much thanks to Elation for helping to make these webinars happen appreciate it and thanks to our attendees for being so engaged I hope you all have a great day thanks all