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hi good afternoon my name is david dousset and i'm here to speak about master data management for retail banking I'm going to do a presentation which is really geared at how master data management can be used in in the banking environment and the benefits and it's not going to be highly technical but it kind of a roadmap of where to start and how to proceed and hopefully some examples of some use cases that will resonate so I guess I'll start off first by just talking a little bit about who light house is and what we do we're a regional consulting company were part of a larger corporation called converged technology solutions but lay house is located in lincoln rhode island with offices in New York City and Ontario and in addition converge is a national company a North American company with offices throughout the US and in Canada we have about 200 consultants been working in the IT industry for 23 years founded in you know 95 very stable company lots of expertise and really client focused just in terms of of our customers over 75% of our customers that we started with 20 years ago still our customers of our company lots of them have long term relationships and we do a lot of repeat engagements where we're happy to say that we're really a hundred percent committed to our customer success and are focused on that really at all costs we we want to make sure we deliver value to our clients and that's our primary commitment to them in terms of what we do we're going to be four focusing on the far right column here the analytics but the company has deep experience in security hybrid cloud in analytics which master data management is just one component and that we're going to talk about today but we do extensive work in business intelligence predictive data integration governance which is really part of bethenny of management as a part of that data warehousing and geospatial and location intelligence so our goal is to become the go-to partner for software and hardware purchases in the security hybrid cloud analytics and infrastructure domains and really to do that by adding value in terms of master data management for retail banking one of the things that we've been really focused on is is sort of what what industry is and what domains have a specific benefit in in the end that possibly they have not been able to you know approach given technology of the past and I think overall mbm has has sort of gotten of a reputation for being in many cases difficult to implement and long term cycles and expensive and so typically in banking you would see MDM in in some of the very large banks but as you get down to regional and community and you know that type of banking structure less and less of that and what we're seeing in in today's marketplace is that a lot of the community banks are merging to become super community banks or state banks or regional banks and with the acquisitions with the mergers with the change in footprint the need to really understand who their customers are and how they are maybe interacting with the different financial services operations the branch network including acquisitions is is really something that many banks are struggling to get their hands around their arms around I should say and in addition even for banks that are just local of there's different relationships the banks will have with different consumers whether it be as a business owner as an individual depositor as a credit card holder and sometimes those systems can be varied within the banks and to have a sense of who is who is who and what products that they might utilize is sometimes a little bit more difficult to get like I said there aren't around then you might imagine so we feel that MDM and especially in the new IBM Express for from IBM is is a great offer for our clients but this presentation is not really going to be specifically about that it's going to be more MDM as a as a capability as opposed to it as a specific product from any specific vendor so the idea of MDM what we're going to talk about strategy and blueprint business value critical success fact is what single view and retail banking might look like talking about the reference architecture implementation approaches and some customers or success stories there is really around how do we how do we enable MDM for for banking without the added cost of a large you know implementation so let's look at what the capabilities are and what what we're trying to do within MDM in in banking and so the first thing master data management what we want to do is make sure we capture the data one time we store it in a way that guarantees that the data is in a single place of reference and it's made available to those who need it wherever they are both internally and when we talk about MDM you know there are various domains and in in this particular case I'm going to specifically talk about customer but obviously product and location and employee are all viable and important aspects in banking I think customer and product you know which resources that the bank offers that our customers take advantage of and which other ones could they become the real key to to to success so in terms of why a bank would wouldn't want to implement a master data management strategy among those things that are important to customers in addition to marketing capabilities is regulatory and compliance functions so know your client is a regulation or capability that is required to make sure that people are gettting accounts for for real for the real person right so there's a lot of examples today of people who are under false pretences recommending or excuse me not recommending applying for relationships within a bank with false identification false information and being able to handle that kind of thing and knowing what relationships you already have are key and the money in laundering and that part of the Patriot Act knowing who who is who is is important the second aspect is knowing that the data is centralized and cleansed and is is of high quality so that policies and procedures can be put in place and with all the various silos of information maybe the enterprise data warehouse the CRM the collection systems any other systems to make sure that this common information across all of those platforms and then linking that information to the products that are available whether they be mortgages or equity lines of credit credit cards capital marketing of data for an exchange Treasury etc so is and then also a lot of banks today offer either their own insurance or in conjunction with some affiliate insurance for life homeowner annuities etc and so knowing that this specific customer hit has this type of relationship maybe they just have bought a house so we want to potentially upsell them a homeowner's policy or whatever the case may be would be very valuable and the the difficulty can be if you don't have a strong sense that multiple records within the organization are actually the same person then that can lead to a missed opportunity in terms of business value or the drivers behind master data management it's it's really the cost optimization and efficiency so how how do we lower cost and make ourselves as efficient as possible how do we manage compliant and risk compliance and risk so we want to make sure if we know that a customer is highly leveraged on a mortgage and if that that person applies for some other type of credit that we're actually looking at the same customer information and not two separate records that potentially could lead us to grant extra credit that may not be justified so compliance and risk management increasing reporting accuracy so again it all comes down to the we always say you know garbage in garbage out so the quality of the data and its consistency leads to reporting accuracy usability and sharing and I think the most important is to establish the authoritative source of information so when people do do reporting and they are getting the information that is mastered and secured and govern then we know that the information that we are reporting is actionable and is trusted and I think that's that's a very key component so business drivers plus mastering the data gets you to some enterprise benefits including cross-selling and upselling capabilities reducing time to market and I talked a little bit about access to improve credit market and liquidity risk management that's that's a very big deal in banks better decision-making overall and operational efficiencies the the just moving on to kind of the culture around the the data and the sort of the mindset oftentimes we go into clients and we talk about the business value of MDM and and they you know what problems that could summon sometimes there's just no recollection or not recollection no recognition is the critical is looking for of the value problems what problems we hear you know and so it's it's one of those things of the force of the trees if you don't realize you have a problem then it's very hard to deal with it so sometimes you know we have a situation yeah we we know we have data silos but but we're just not sure what to do about it and other situations we get into the customers are like you know we deal with a lot of spreadsheets and we we manage to get by when we get some stop to get some traction is we're doing some things in our you know our loan portfolio or our you know our investment portfolio but we're not we're not you know we don't have an integrated approach and ideally we want to get a client to the point where they they understand that they have a real opportunity and we can give them a unifying vision that prai's MDM is a good program and that it will lead to significant benefits and the ultimate goal is to get our clients to understand that data is the way things are done I mean and to make it an integral part of the day-to-day operations and have it be a continuing learning and improving capability that they can you know that the business on when you do have a good MDM strategy but your companies have better performance across the whole lifecycle Gartner indicates a 20% increase in efficiency and improvement across operations finance customer data management with with quality MDM in place and the best part of it is once you have a platform and up and a an architecture in place then it makes it really easy to integrate third-party data in analytics so what even bringing in new systems and new capabilities can be with a good architecture and a good a platform it's very easy to integrate new things including new acquisitions new new new new sets of customers or new portfolio products within within the existing existing bank some of the benefits of a single customer view so one of them is address compliance to meet reporting from Enoch Center sarbanes-oxley in Basel as well as consumer privacy creating data consistency and enhancing data accuracy very important and sometimes overlooked in in in different institutions controlling costs and reducing risks is a you know something that it's hard to quantify sometimes but obviously MDM gives you the exam of the ability to make sure you do take data and when it comes to my particularly marketing campaigns is is one example of where we see the benefits when you're contacting people multiple times because you have multiple records it can detract from the effectiveness you know if somebody gets three flyers in the mail on the same day they sort of feel like it's completely junk mail and might not pay the attention to it that it deserves to mention the cost of production and mailing and everything else same thing with statements statement generation when you know you might be sending three statements because not not the same statement potentially but three different statements for one relationship that should be consolidated into one all of those things could full help control costs and reduce reduce risk in addition to that if somebody moves and you have one version of the customer then one change of address changes or everything in that that has its real value from a new revenue capability it's meant imagine the call center within the bank so when you have a good MDM reference architecture and so many calls for a service it gives you a the call center staff a very nice opportunity to cross-sell and upsell and improve the customer experience because you have a full view of exactly who the customer is and what their relationships are within within the bank so it that goes right to the customer service as well and you know I point out a few others customer identification so we know exactly that we have the right person and the single customer view enables better business analytics into in general so when you're doing things like predictive analytics prescriptive analytics or even just reporting having quality consistent data in an MDM platform makes for a much better reporting and in better results in terms of pretty pretty predicting outcomes when you when you look at you know doing predictive analytics you looking at the population and the and the tendencies and the tendencies in the the behaviors of clients and trying to then take that sample set and forecast it over the entire population is much more accurate when the core data that you're analyzing is accurate so if I have myself in within the bank as three different people and I should be viewing that as one when I stop making predictions and use that as a guide it's going to very much skew the numbers and the accuracy of the predictions so that's that's another big benefit of MDM single customer view in terms of a blueprint and I just have this because I know people are going to have the opportunity to have copies of the deck we view the blueprint for MDM is kind of a six step process one is is first making the case for MDM why why do you need MDM and that goes back to the original slide that I showed about you know problem what problem so the first step is sort of making the case for MDM and at Lighthouse I think we have a very deep expertise in and the people that work with me and in my on my team have been doing this for a very long time and once the case is made for MDM the the we can really help along this journey so preparing for MDM you know we have consultants and and experts that will come in and help you understand the different kinds of capabilities and different implementation methods and strategies that can be used but the first case and the first thing you have to do is what are the business drivers what one of the different MDM methods of use and how ready are is the organization for MDM and the importance of governance to MDM and what kind of value can be expected out of the project and then to make sure that we define what is the different data types within the organization what needs to be mastered and what is just reference data what domains that do we want to master is it just customers a customer product visit and then how if if it is multi-domain how are we going to implement it at a multi MDM strategy and what capabilities do we expect and then to define a reference architecture for the for the MDM implementation the system of entry record and reference and look at architectural principles and guidelines with with the goal of making it easy to implement a highly available highly scalable and highly adaptable to changes in terms of again new data sources new new capabilities and then there's a sort of a strategic roadmap that we have here in terms of ways to roll it out and you know if you're looking for outside health we would even help develop an RFP where you could actually go out and solicit obviously we always love to do it ourselves but we were sometimes we're just asked to provide an advisory role and so again a roadmap and then finally the ongoing care and maintenance of the the MDM system the governance taking care of new data sources and change requests in terms of again the business objectives for the financial institution it's really around increasing customer profitability so getting more wallet share from your customer through cross-selling and upselling opportunities other benefits though provide an audit trail for regulators for the bank for for the stakeholders that have concerns and or responsibilities around
ompliance and or or it also risk management and regulatory compliance as I mentioned but risk management a huge factor for banking to minimize risk while maximizing profitability and Wall chair is hugely valuable and obviously one other is for lead generation and prospecting in terms of you know sort of the pyramid what we would view the pyramid of you know in use cases in success criteria so having a solid platform having a strategy for identity resolution and know your customer needs to increase profitability reduce manual work and improved operational efficiency and the single customer view improves visibility to relationship history and that's the the key the key is to get to the cross sell upsell by identifying various opportunities in the propensity to buy and patterns that may establish or show themselves from various customers that can be applied to others that will lead to a strong opportunity so so here's an example of what single view of a retail banking customer might look like and and how it would manifest itself so this is the on the slide here we have four different versions of Tom Tom B Tom bridge is Tom and different information in terms of net worth and you'll income where you went to school maybe different email addresses and so with the three the 360 view and through cleansing and deep implication we then come up with the Golden Record on the master record which is sort of a combination of all of the others and a superset of all the information so that's a you know a graphic a visual example of how how that would work but I put on the slides and some notes around you know it's so we take transaction data focus versus customer data so different transactions that may occur within the bank may may have snippets of all the different pieces of some of this information but when we can tie it back to a single entity and know that this is in fact the same person it provides a lot of value how this happens is not magic there are rules for names rules for modeling and entity processing and how to handle errors so when a customer record is trying to be matched up to the MDM system if you know if if it can do it automatically which is hopefully the goal ninety-nine percent of the time then then it happens but there are cases where there's a lack of standed or and their processing errors and then that record might have to get kicked out and be viewed by somebody whose responsibility it is to sort of audit that that record error and determine how best to fix it and incorporate it into the whole of the whole environment so that involves you know strategies around ownership of who owns the data and who's responsible for its accuracy outside of the actual MDM system and you know what sources of data are we are we maintaining and and we're working through within the MDM huh if you will so what are the custom and data quality impacts again we've talked to some it helps with risk credit risk market risk exposure for fraud and things of that nature anti money laundering and so and the best part of it I think is it helps reduce you know customer dissatisfaction on the flip side is enhancing customer satisfaction so that they feel a real connection to the bank and I think today when banks compete on on just one flip what a playing field which is just maybe product offerings it's it's very hard to differentiate yourself from a lot of the other banks because so many with technology today so much is offered at every Bank and so how do you how do you become the bank that is going to be the go-to bank for the community and basically you set yourself apart with with service and personalization and there's no better way to do that that with a salt and then with a solid MDM a platform so the benefits again reduced operational cost better customer experience unified view of the customer relationships and contextual analysis so what what kind of relationship that we have at that Bank and without excuse me with that consumer and how can we improve it and elevated customer service opportunities to help increase revenue in terms of lighthouses master data management implementation approach I'm not going to walk through just in the in the consideration of time I'll just touch upon a a pieces pieces of this and obviously have it available to you for but you know what is the current state of the organization in terms of data quality in terms of processes what what is the desired state and this is all happening in the discovery phase of before any kind of actual work is is getting done with with any kind of products or technology assessment of the data elements how critical are they what quality are they already and what is the complexity of implementation and so certain things come out for example with in banking you'll have deposit accounts that might have multiple names on them and how do we handle that type of entity resolution do we do we master two different names how are we handling that of you know particularly you know with us spouses or a parent and child sometimes the relationship is and sometimes the relationship is or so either one has access to the funds sometimes they both have to be present or available to access the funds when it might be related to things like trusts or other other legal relationships that might still be me in terms of my account but it might be in trust of or and so how do we handle things of that nature so all or it might be my my business so if I have a business and I have an account at the bank yeah do we view that and that relationship with with regard to the business as part of my my overall MDM relationship as a customer so data element sites that sources kind of get a sense of the candidates and then produce a roadmap a definition and then once we go through that its architecture and data modeling and as I say this there's you know in any technology implementation is this a roadmap that once followed and once a pattern can be established and the road to success is it's more shirred so MDM architecture data governance and technology implementations are all aspects that have to be considered in the MDM journey in terms of the reference architecture that we see in MDM the you know there are seven layers lifecycle management data management data persistence data services and integration data quality and data sources and what I've done is is sort of you know listed the components of each of those layers and things that have to be considered when you implement MDM and look at it in this in this context so you know a simple example here obviously is the internal and external sources what what are they but you know overall this really is here as a guideline firfer for you for for what what things to consider within each layer again master data were metadata reference data an audit do we do we how do we want to keep track of who's doing what to our data all these kinds of things security and transformation and visualization ETL all of these things are components of the sound master data management strategy and wanted to make that available to you for your reference so here's an example of a transactional system is in a customer reference architecture around retail banking different data sources transactions into the MDM hub and then how it how it would impact all the different consumers or constituents of that data so data warehousing portals agency management data stewards ATM other it's really a copy over overreaching capability that that impacts all aspects of the of the of the institution and you know in as I said in the opening in the early days the the ability to implement MDM was a very time-consuming lengthy process that and just the technology itself was very expensive and today with products like MDM Express from IBM its purpose-built it's more affordable and it's easier to movement and at the pricing and timeframes that smaller regional and community banking institutions can afford it in terms of Omni channels and retail banking so again the improving onboarding of customers improving satisfaction include improving campaign performance or all aspects of the surf in in some of these cases it can be the web interface is the walking into the branches the the the banking center is where people might come in for other other things besides just depositing and and withdrawing and cashing checks and things of that nature market effectiveness transparency loyalty of rewards is another thing that we're seeing within our banking customers that they're starting to want to track of the use of you know things like ATM debit cards in in the maybe even community businesses and offer a loyalty and rewards program so that there is a incentive to use that as a method of paying and it also ties it into the other local businesses to get you know not only additional revenue additional loyalty through serve with some type of a rewards program and again we talked about personalization and and cross-selling through enhanced data collection so the whole goal is to deliver orchestrated creation delivering optimization of personalized content rich digital and non-digital experiences anytime anywhere to delay customers that's how we we view it is we the whole goal is the customer focus and delivering better information to the bank so that we can then treat the customers with a whole new level of personalization an example a customer client example so in the banking industry implementing MDM and business stewardship 80 years in business full-service Bank complete range of financial planning and investments so limited the challenge was of limited access to all customers data and accounts due to multiple legacy systems I think we can all understand and appreciate that this is a common problem in business today as legacy systems multiple multiple databases data kind of everywhere and even even for those companies and institutions that have done a good job the onslaught of data coming from everywhere today is just hard to get your head around and and certainly very difficult to maintain so need to streamline maintenance and contact center costs and delays in accessing client information negatively impacting satisfaction so when you know I don't know you folks but when I get on the phone and I call my bank and they say can you hold please and if I'm waiting for 10 minutes and when they come back they still don't see the port and forget it that can be frustrating so the solution is employee MDM to implant single customer view and give them access to real customer information lots of deduplicating and here's the cool thing is once the data is be duplicated then not only is it quality improved and its accuracy improved and you know your cost reduced but you can then start looking at enhancing data with customer information that's now maybe third party or you can start looking really at location information where is the customer in the world how far are they driving to the bank when they're at work when they're in the office when they're you know so all these types of things become unlocked with a single customer view in the bank and target specific offers to businesses and consumers based on there were overall relationship understanding of that person or that entity the proof customer data accuracy went from 15 and 96% IT support staff was reduced by 50% and deployed in more revenue generating areas so it's not a matter of eliminating staff it's a matter of refocusing and not having to have people spend time on redundant and not real value-added endeavors eliminating multiple client longings in some cases was achieved and providing instant instant access to new accounts so you could sign up for an account instantaneously instead of waiting sometimes up to 48 hours to be approved you know one simple example of that I pulled out from one one of our one of our clients is we were talking about how understanding who who is who and you know it's not that they treat people differently per se but a business owner who has you know potentially millions of dollars in value in relationship with the bank might get a little bit different treatment if they overdraft on that personal account once in terms of waiving that fee or maybe twice so maybe the rule is that you always waited for accounts wants to give them a you know a break if they make a mistake but somebody who has a very valuable relationship with the bank and who's you know providing value in other areas when that overdraft occurs the second or the third time how important is it to collect that $35 or whatever the fee is as compared to preserving their relationship and having them walk away going that's why you business in my company with that Bank look how they treat people and I think those are the kind of benefits that you get out of implementation of a strong mbm because now there's no doubt of who that person is and what the value is to the company and finally in a case of anti Munda money laundering so this is this is a compliance area and we had a customer that provides a full range of financial services from credit cards to personal loans and consumer banking and the idea was to improve investigative efficiency and really in the case of only happy money laundering when you have systems in place it makes you less of a target for investigation even because the the investigators are the the people who would would be auditing you for these kinds of issues are less likely to be as concerned if they know that you have systems in place and so the the idea is to minimize unnecessary alerts so instead of being warned of potential issues we can cut that down and reduce the cost of investigating false positive alerts the the bank deployed enemy resolution in comprehensive view of each individual and enemy across this portfolio resolving nicknames and other name variances a very key component of data quality and MDM so that being able to figure out those kinds of subtleties as I alluded to earlier it's very important and consolidating alerts across individuals so that investor receiveth at most one alert per per entity it just cuts down on the whole workload the benefits was ten million a year in cost avoidance and unnecessary investigations 58% fewer false positives and effective and efficient investigations ensuring that the bank is complying and because of that again it's like somebody who you know has a good software access management strategy within within their organization if you have a good software access management strategy then the software vendors that are selling you software are less likely to audit you around whether or not you're in compliant on your software utilization it's the same thing with the banks so the better they have systems in place the less chances they have of being audited or investigated or at least less frequently and and when they are they have better audit logs better tracking better capabilities to ensure that they are compliant that is what i have i appreciate your attendance and your if there's anything that we can do a contact information is available through the portal and more than happy to help thank you and have a good day all right so that kind of concludes the the powerpoint part of the demonstration and what i'd like to do now is turn it over to Jason Mathieu from IBM so he can give you a live demo of the of the product and then we'll follow it up with Q&A so thank you Jason all yours thank you so much Dave and thank you for making time for our demonstration today my name is Jason Mathew and I work from the offering management or product management team here at IBM and on behalf of my team we are very proud to present to all of you I'd be a master data management Express so Dave went through a really rich presentation just now on why Master it is so important for retail banks and we at IBM have recognized the exact same when we as a team came together just over a year and a half ago we asked ourselves what are the major challenges that organizations are facing from an MDM perspective how can we help them adjust those challenges how can we make mastering data easy right easier not only for retail banks but for any organization I'll cause any vertical but we thought about retail banks and particularly saw a great opportunity to say that mastering data in the banking from a ban
ing perspective has always traditionally been more challenging for whatever reason so that's why we we went ahead and saw the benefits of creating a lightweight quick to deploy products such as IBM MDM Express so that retail banks in particular can can really get an MDM up and running and not have to worry about the deep technical knowledge and know-how and and also a lot of the the baggage that comes with some really big enterprise MDM products so today I'm going to walk you through a demonstration of MDM Express we don't need we won't be taking a lot of time but just enough time to show you just how quick and easy it is and just how powerful it can be for you and your organization when it comes to getting a single customer view to really knowing and understanding who your customers are so this is the login screen for MDM Express I'm just going to go ahead and put in my username and password so when you sign into MDM Express as I mentioned there are a couple of themes here that really define what MDM Express is and again why we built it and one of those is intuitiveness right so with intuitiveness there's a really large thread there of simplicity right where we wanted users to come in and see that hey this is like no other MDM product ever used it's simple it's it's it's obvious as far as what I need to do and where I need to go so the first screen that any user is going to see is this dashboard and we built this dashboard to really call out three major or four major components the first is being able to quickly understand what data we actually have in our MDM either based on the number of Records that we've brought in right so the actual data that exists as well as the number of entities that are available and think of entities as a master record in addition to highlighting these these high-level statistics we also built this so that it mimics or is in line with the user's traditional workflow right everything that we want MDM Express to be needs to map back to what our users are doing and that's from a thanks to healthy dose of what's called design thinking here at IBM where we built out every single action so that it's in line with our users natural work processes and it doesn't deviate from again how they approach work on a day to day basis so as I said one of our goals is to enable the users and everything that they do to make this easier for them and the first piece of or the first step is being able to enable enable users to add data to the to an MDM system and gain actionable business intelligence without the need for truly engaging IT as every as they would traditionally so to start of course we're going to click on the bottom left-hand corner here the first is is upload data so when I click on upload data again the first the next acts I'm going to see is will what is the file that I want to bring into our system there are two ways we can ingest data into MDM Express the first is through a flat file that I'm going to go through here through our user interface and the second is through actual API so you can use API and the real-time integrations that we've built in to bring data into our system to match that data etc so when I click on upload data I'm going to be able to select a specific file in this case I'm going to see with CSV file and I'm going to then name what's called our collection so once I upload this file it's going to create a specific collection in our system and I'm going to name it and give it a description so that I can refer to it at a later point so I'm going to I'm going to call this banking customers I'm going to add a quick description here I'm going to then click on next so when I click on next you know the next component to this is really being able to map and define the data in the file to the model that we've built in MDM Express in this model if you didn't notice that in the last screen is what's called a person data model this person data model is what we're going to be able to use to define what a customer actually looks like being able to map the data from the file to our model is what's going to be key for the actual matching process a Dave talked about matching of course being a key component to mastering the data without matching you really can't do much I'm so being able to map these fields is typically a very time-consuming process but again for MDM Express we've tried to make this as intuitive as possible one of the major ways we've done that is by allowing customers to be presented with suggesting mapping attributes so as you can see here each of the columns the 20 columns from my file or automatically recognized and for a majority of the files we can actually leverage another technology in the product to suggest to our customers what the particular what we believe the particular value to be in that column so in this case let's say if I were to scroll down and see that the sample value from column 5 is male or female then we suggest the suggested mapping attributed gender I can either select that to be yes that's absolutely right or I can go back into a model and I can choose any one of these other attributes that I think is more relevant when I scroll down if I see Kathy or JP I can say that's either legal name given name or let's say that's a nickname given it either way the the process is going to be the same while going through and for each column going to map that to what from our file to what we believe makes sense in the the data model so once you go through that process the next natural step is clicking on finished collection so for the purposes of our demonstration today I'm not going to click on finished collection and again that's only because I've already actually gone through and uploaded some data here to make this as quick and seamless as possible so when the file gets uploaded into MDM Express we analyze that and we try to determine what each column in the in the file might be and then we again start matching that to data that does or doesn't already exist within our within our system so once everything clicks looks good I would have clicked on finish again and now what we seeing is our collections tab so the collections tab that we have here again these are literally collections of data and there are different types of collections that we have the first is the upload collection so this is the file that I went ahead and already uploaded and the second is what's called the match file the match collection the match collection essentially is the data that has been matched or mastered based on what was already uploaded so you can see here in this upload file there are about 10,000 records or so but in my match file there are about 5000 or almost 6000 entities I'm going to show you what that looks like but as you can guess what essentially happens is that up from the 10,000 records we were able to duplicate that and create about almost 6,000 unique records or rather 6,000 unique entities so when I click into person the next screen I get is this right so this is every entity that has been formed based on the based on the data that I that I've ingest and the data that was matched so if I were to type and let's say Julie Jackson I know Julie Jackson is a pretty lifelong customer she's based in in Arizona and Tucson and I want to see well what data has come together to form Julie Jackson at the left of each entity you'll see a number right this number is representative of the number of records that have been consolidated or brought together to create that particular entity so when I expand Julie Jackson you'll see four distinct records you'll see Julie Jackson from a CRM you'll see J Jackson from an ERP you'll see again Julie Jackson from a CRM and jewel Jackson from let's say a Salesforce system now again this pretty much captures the essence of what master data management is all about right I have four different Julie Jackson's or variations of Julie sitting in three different systems the address may or may not be the same the telephone numbers may not be the same the birthdays the social security numbers so on and so forth and how can any banking institution look at this data without having been mastered and say that these were really all the same person right you can look at the address or you can look at one or two other attributes but this is the challenge that organizations face and especially imagine having to do that at scale right so this in my mind this is one of the easiest ways to look at why mastering data so relevant and important especially when means to when it comes down to thinking about and focusing on on who our customers are and being able to serve them appropriately so this is Julie Jackson as an entity formed by four distinct records I'm going to come back to Julie Jackson but I just wanted to talk to you about what that person collection actually looked like there's a third collection here and that's what was called a search collection search collection is the ability to actually create a specific grouping of customers or records or entities based on very based on very specific rules so imagine you again as a bank and you want to create a dedicated marketing campaign to all of your female customers who are living in the New York State area well how do you do that well thanks to our advanced search functionality we can actually add very specific rules that allows us to set those attributes based on based on based on criteria that we deem relevant so if I go down to primary residence I'm going to choose primary residence state I'm going to make that equal to New York and when I click on save what that's going to do is it's going to give me around 464 records for all of my customers that are currently living in New York well that's awesome but I said that I wanted to focus on all of my female customers that are living in New York so I'm going to select gender I'm going to make that equal to F I'm going to click Save and now I'm seeing 177 records so these are the female customers that are living in New York and then I can click on create collection and then creating this collection will allow me to create a unique description oops and then based on that unique description I can refer back to this at a later point when my self or any one of my colleagues want to come in and actually refer to this specific set or subsection of my customers based on again my targeting marketing campaign email blasts direct mailing campaign etc so that's what we're looking at when we think about the search called collection and search functionality within MDM Express and of course we also have a simple search functionality as well which I glossed over but naturally I typed in Julie Jackson if you remember from the example of that brought back to the Jackson typing in Julie will bring back any customer that has Julie not only within the legal name field or wherever we see Julie relevant across the entire profile of that of that particular customer so that's what we're looking at when we think about when we think of bringing data in being able to manage or work with the data that we have and being able to recognize what a mastered entity actually looks and feels like now this is all to say I'm doing this within our user interface and a bunch of you may have questions on well you know we need our api's we need this data operationalized and flowing regardless of an operational analytical use case right everything that i'm showing you today can be done via our REST API so that we have and again the more information you want to know about them being expressed the more that we can share with you happy to share that documentation we're appropriate but again as a reminder everything that I'm showing you today and what I'm going to show you can be done through our API so this brings up the next natural question at least next natural question for me is you know mastering data is awesome but a key component of mastering data is being able to add in context that myself or my organization will only know everything that mastering data represents is not only representative of automation of what a machine can do but of what a human can add in as well right and that's where we have our lightweight stewardship functionality and when we think about lightweight stewardship again the emphasis on lightweight from the perspective of I want to bring records or entities together that should be together I want to break things apart that shouldn't feel together or I want to be able to actually edit records or entities from within the user interface I want to show you each of those components right now when we think about data stewardship specifically so again let's say I'm a data analyst I'm investigating some matches created by data that we recently added into our into our bank's MDM so let's say again I'll do a simple text search and for consistency let's say we type in Julie's action again so we type in Julie Jackson that's going to bring back again Julie Jackson number one and any other any other record or entity that has Julie or Jackson as part of it so when I click into Julie Jackson again I know as a data analyst let's say who Julie Jackson is but as I mentioned before I have some context that at the system or the computer will not and may not have right so when I expand Julie Jackson my intuition tells me or my gut tells me that the third the third Julie Jackson actually has a different home telephone number the birth date is the same the address is the same the name is the same but for whatever reason I have a feeling that this Julie Jackson a really strong feeling that this Julie Jackson is not the same Julie Jackson as the other three all right now let's say I go and verify that in with my other with my other colleagues and I come back and say yep in fact this Julie's action should be its own or her own entity as opposed to being part of the originally mapped one so what can I do here what are my options well on the left hand side when I click on these three little dots I have a few options here I can either delete Julie Jackson and say Julie Jackson doesn't exist for whatever reason I can move Julie Jackson from this entity to another or I can just say hey listen Julie's action should be by yourself I'm going to unlink her completely so I'm going to click on unlink and what we've done here is we've created again make it as simple and easy as possible to our users we've created this ability to actually preview what you're about to do and then what are the implications of doing this right so the preview is a new entity will be created for the record we've chosen to unlink and then we can actually leave a descriptive reason for why we're doing this and again thinking back to how data stewards or analysts work with master data there could be multiple users in your MDM making different changes making or taking different actions so being able to leave those descriptions and explaining why we're doing what we're doing is going to be pretty relevant so I look at the action and look at the preview of my action I click on unlink and by clicking on link what we've done now is you can see the original Julie Jackson that had three or had four now has three and Julie Jackson with three five three five nine home telephone number is now Julie Jackson all by herself right so this is what actually happens when you unlink or rather yeah when you unlink records from specific entities and again you can move these back together if you want to or you can link records together and then actually look at what the implications are the the major takeaway here is that mastering data by itself at least from an operational perspective perspective is not truly mastering data without being able to bring in that manual intervention of saying that data is or shouldn't be together or should be together so on and so forth all of this is relevant when we think about being able to steward the same data that Deborah mastering and the the final component of the almost final consenting the last component I want to talk about is really when we think about managing consent for our users so I'm sure that there are some of you listening today that are familiar with gdpr outside of the US and within the u.s. we have CCPA that's going to be implemented pretty
shortly here and then who knows how many other policies are going to start popping up nationally that have to the the second-to-last component I want to show you here and talk about is consent management or being able to manage consent for users so some of you who may be listening in today are familiar with could be familiar DPR outside of the US and within the u.s. we have CCPA of course in California and potentially other policies that are going to start popping up across across our country where organizations need to start showing more responsibility for the data that they're managing especially data for their customers and banks are no exception so understanding that we've created a key piece of MDM Express to revolve around consent management model and the reason for that is you cannot manage consent users ability or understanding what users opted into what or opted from what communication without having mastered those customers to begin with in the example of Julie Jackson if you thought these four or three Julie Jackson's with three or four distinct people then the consent that you managed for each of those individuals would have been distinct in and of themselves right and we both know we all know that if that was the case then the implications of any communication in in case you Lee Jackson had opted out or asked not to be disturbed could have been very detrimental right so again with that said if I click into settings here and I click on consent model this actually allows me to create first what's called a processing purpose a processing purpose helps us as an organization and our customers understand how is our data or how is their data going to be used so I'm going to start by creating a processing purpose here I'm going to call it marketing analytics and I'm going to add a description I'm going to say customer data to be leveraged for marketing analytical purposes and then I can create or add a specific language let's say English because we can be collecting consent in multiple languages and this is the script that I've actually typed in other words let's say if you had a customer service representative who was calling on customers and asking them about their consent or asking for consent do you consent to this particular part processing purpose of course I'm sure you'd be a little more articulate than that but either way if I click on finishing finish purpose now you'll see that one processing purpose has been created I can go back view it I can edit it accordingly so now that that's been created I'll go back to my master data catalog and I'll click into let's say Julie Jackson again and if I view on the details of Julie Jackson I'll get again a pane with sort of the major details anything from our contact info Asian health card SSN but right at the top here you see a section now that shows consent and when I view and edit all consent I can see what processing purpose have been created marketing analytics I'm going to edit that and here I'm actually creating or setting whether or not the consensual would tell you whether the consent was given so I can type in Julie Jackson name as the the consent giver I'm going to say consent was given it was uh it was collected in English and then I'm going to click on save and then by clicking or clicking on save now you'll see that Julie Jackson as far as the entity has been given has given one consent and then that's actually ricocheted down or been pushed down to all of the Julie Jackson the records of Julie Jackson's that were formed together for that particular came together for that particular entity yeah very powerful and very important when we think about privacy policies from the perspective of being able to manage consent and then even being able to purge data from your systems mastering your customers is going to be a key step before you go ahead and even think about managing any type of consent day and the very last component I want to talk about is how do we get data out of our system we talked about bringing data in getting data out and the really two is one of course is again is our API and all the rest API is that we have but then the second again is through our user interface and through our user interface when you click on whether it's the matched data the original uploaded file even the search collection that we created back at the beginning this allows us to actually create a new export and then once you create that export you can download it and consume that data I went ahead and already created an export about a little more than a month ago now but what I click on that that's actually going to bring the data into a into a CSV file for us to actually look at it and we necessary so this wraps our demonstration this wraps up our demonstration for IBM MDM Express I hope everyone who is listening and watching to really see the benefits of what MDM Express could bring to your organization especially when we think about a lightweight MDM product so really excited to be talking to you about this and and Dave will pass it back to you thanks again Jason really appreciate it and now hope that you'll all take it an opportunity to come visit our website at wwlp.com [Music]
