Building a business case for MDM in the world of banking

Transkript

1 successful Building a business case for MDM in the world of banking Calvin Farmer Don Elliott don.elliott@anz.com October 2010 cfa@platon.net

2 Contents How did we get here? Can we really build a business case for MDM? Understanding the nature of MDM and its true cost to the business Approaching the business case top down An industry study - Banking 2

3 Mængdestat isti k Posthus Produk tions gruppe Mængdestat isti kkode Hu ss tan d stal Modtagergrupper Gadenavn/Husnr Posthus Produk t Produk tions gruppe Format Mængdestat isti kkode Mængdestat isti k CD S STA Blad fort eg ne lse Opret, flyt og slet blade Gruppenr Ru mnr Omdelingsnr Gadeopl ysninger Mængde st at istik Vejedat a_antal Vejedat a_vægt Posthus Produk t Produk tions gruppe Format Blad fort eg ne lse Mængdest at istik Faktureringsgrundlag (manuel indtastning) Adresse udtræk Adressens gruppenummer Kredskontor Postk ontor Posthus Postdist rik t Land Da to Ug eda g Nøglet al VÆGT (Bisco) Nøglet al (manuel indtastning) Bestill ingsoplysninger Mængdest at istik Adresse udtræk Kundeantal pr. ku nd e typ e Hu ss tan d stal Gadeopl ysninger Gruppeoplysninger Re olop de lin ge r Stednavne Hu ss tan d stal BOS DI A D9 6 FAK FKS FLY GTT HU S MIS MSS MVA OCR OPD PIA PKV PLS PMS PPS PR O PST STM TTS US F VAP BSP D9 6 DP B IBS MOD MVA OPUS PMS PLS PPS PØS TID VAP BOA BSP D9 6 MVA ODS PMS PLS PPS STP PMS BOA Bladt itler og ford eling Mængdestat.total pr statistikkode pr. prod.grp. Behandlede mængder Ce ntra le st amd at a timefo rb rug Hu ss tan d stal Produk t opl. Anmeldte partier PLS PR O PIA Re alisere de mængder Gadeafsnit Postbok skunder Re olop de lin ge r Stavevarianter Stormodtagere VÆGT Vejedat a Kundeoplysninger Akt ivit ets koder Produk tions gruppe Stedkoder Dr ift sd øg n Kørsel s datoer Produk ter Funktionskoder DP B MVA PMS SPB VAP CD S No r me r Grundtider Beregn. parametre Afl.steder Hu ss tan d stal Postmængder Timebehov STREGKODE- HUS LÆSER GEISCO STREGKODE- LÆSER Hæ nd els e timefo rb rug (Mødelister) Medarb.oplys. Akt ivit ets koder timefo rb rug Syge st at istik Sort eringsopl. Planlagt tje n est e His toris ke mængder Vejedat a Hæ nd els e Mel ding Vars ling Anmeld. Medarb. Opl. De mog r afis ke oplysninger FKS MIS MSS US F BOS CD S D9 6 PIA PR O STA PIA CD S D9 6 PIA PR O STA TID CD S tr af ikta l Stamdata Massepart i oplysn. Faktisk forb r ug t tid BSP Særlige yd el ser Anmeldt massepart i Mål -tal Medarb. oplysninger Prognoser Plan Plan Beregn. parametre TID (Scanner) BOA MOD VAP PLS Time behov Time behov Fravær Trp. ordre Opl. Post mængder Møns ter sp ec ifik atio n Planlagt møns ter Taste bilag Lønoplysninger Time behov timefo rb rug OCR Navne og adresser Virksomhedsnavne og adresser D9 6 Produk t og pris opl. Ordrer Opsamlings prognoser Massepart i oplysn. Prognoser realiserede mængder m.m. Udbetalt løn PMS Produk t og pris opl. og planlagt timefo rb rug Transport ordrer timefo rb rug Udbetalt løn Forsendelses st at i Trp. ordre st at us Budget teret Forventet salg mængde/k r Forventninger Pøs-st ed s alg og omkostninger Budget teret salg og omkostninger Kundeopl. Trp.ordre status ED I fakt ura Re gnsk abstr an sa kt ion er Skrankesalg Trp. ordre på stregkoder Re gnsk abstr an sa kt ion er Land Adresse Modtager TNT EDI BLA BOA FAK GTT (Vægt) (Håndscanner) K-BOK S IMS MVA ORG SPB TTS TTT VAP Kundeopl. sa lg Posteri ng BOA OPD TID (C entral) D9 6 DC S LBS MOD OCR-BR(sort.) OCR-PK(sort.) PD B PLS 'SOM' Fili al Server Stregkode Hæ nd els e Vægt Stregkode Hæ nd els e EPG pakker Akk. opl. om ku nd e rs portoforbrug Agregeret realiseret salg De bitor kr av Månedst al Betaling Kunde- og sa lgsopl. Kundeopl. Indbe tal ing (Realiseret salg) (Stor/Lille) Lokal del Adresseopl. (Image) Re gnsk abstr an sa kt ion er Salgs opl. sa lg Kundenummer Kundenavn Månedligt aggregeret forb r ug sa lg Stregkode Hæ nd els e Vægt Da glige summer til fakt ur alin ie r Hæ nd els e Hæ nd els er Stregkoder Central del Faktura til udprintning Postens Mailbureau Da nsk E MS -fo r s. beh. i udl. Forespørgsel på forsendelse sa mt s va r Kundeoplysninger Faktureringsgrundlag for registrerede pakker Træ k på ko nt i BG Bank Ud l. EMS-f or s. beh. i Danmark Posthus Produk tions gruppe Mængdestat isti kkode Hu ss tan d stal Modtagergrupper Mængdestat isti k Gadenavn/Husnr Posthus Produk t Produk tions gruppe Format Mængdestat isti kkode Mængdestat isti k CD S STA Blad fort eg ne lse Opret, flyt og slet blade Gruppenr Ru mnr Omdelingsnr Gadeopl ysninger Mængde st at istik Vejedat a_antal Vejedat a_vægt Posthus Produk t Produk tions gruppe Format Blad fort eg ne lse Mængdest at istik Faktureringsgrundlag (manuel indtastning) Adresse udtræk Adressens gruppenummer Kredskontor Postk ontor Posthus Postdist rik t Land Da to Ug eda g Nøglet al VÆGT (Bisco) Nøglet al (manuel indtastning) Bestill ingsoplysninger Mængdest at istik Adresse udtræk Kundeantal pr. ku nd e typ e Hu ss tan d stal Gadeopl ysninger Gruppeoplysninger Hu ss tan d stal BOS DI A D9 6 FAK FKS FLY GTT HU S MIS MSS MVA OCR OPD PIA PKV PLS PMS PPS Re olop de lin ge r Stednavne PR O PST STM TTS US F VAP BSP D9 6 DP B IBS MOD MVA OPUS PMS PLS PPS PØS TID VAP BOA BSP D9 6 MVA ODS PMS PLS PPS STP PMS BOA Bladt itler og ford eling Mængdestat.total pr statistikkode pr. prod.grp. Behandlede mængder Ce ntra le st amd at a timefo rb rug Hu ss tan d stal Produk t opl. Anmeldte partier PLS PR O PIA Re alisere de mængder Gadeafsnit Postbok skunder Re olop de lin ge r Stavevarianter Stormodtagere VÆGT Vejedat a Kundeoplysninger Akt ivit ets koder Produk tions gruppe Stedkoder Dr ift sd øg n Kørsel s datoer Produk ter Funktionskoder DP B MVA PMS SPB VAP CD S No r me r Grundtider Beregn. parametre Afl.steder Hu ss tan d stal Postmængder Timebehov STREGKODE- LÆSER GEISCO STREGKODE- LÆSER Hæ nd els e timefo rb rug (Mødelister) Medarb.oplys. Akt ivit ets koder timefo rb rug Syge st at istik Sort eringsopl. Planlagt tje n est e His toris ke mængder Vejedat a Hæ nd els e De mog r afis ke oplysninger FKS MIS MSS US F BOS CD S D9 6 PIA PR O STA PIA CD S D9 6 PIA PR O STA TID Mel ding Vars ling Anmeld. Medarb. Opl. CD S tr af ikta l Stamdata Massepart i oplysn. Faktisk forb r ug t tid BSP Særlige yd el ser Anmeldt massepart i Mål -tal Medarb. oplysninger Prognoser Plan Plan Beregn. parametre TID (Scanner) BOA MOD VAP PLS Time behov Time behov Fravær Trp. ordre Opl. Post mængder Møns ter sp ec ifik atio n Planlagt møns ter Taste bilag Lønoplysninger Time behov timefo rb rug OCR Virksomhedsnavne og adresser D9 6 Produk t og pris opl. Navne og adresser Ordrer Opsamlings prognoser Massepart i oplysn. Prognoser realiserede mængder Fordelingsnøgler Fordelingsnøgler m.m. Udbetalt løn PMS Produk t og pris opl. og planlagt timefo rb rug Transport ordrer timefo rb rug Udbetalt løn Forsendelses st at i Trp. ordre st at us Budget teret Forventet salg mængde/k r Forventninger Pøs-st ed s alg og omkostninger Budget teret salg og omkostninger Kundeopl. Trp.ordre status ED I fakt ura Re gnsk abstr an sa kt ion er Skrankesalg Re gnsk abstr an sa kt ion er Land Adresse Modtager TNT EDI Trp. ordre på stregkoder (Vægt) (Håndscanner) Kundeopl. sa lg Posteri ng BLA BOA FAK GTT K-BOK S IMS MVA ORG SPB TTS TTT VAP BOA OPD TID (C entral) D9 6 DC S LBS MOD OCR-BR(sort.) OCR-PK(sort.) PD B PLS 'SOM' Fili al Server Stregkode Hæ nd els e Vægt Stregkode Hæ nd els e EPG pakker Akk. opl. om ku nd e rs portoforbrug Agregeret realiseret salg De bitor kr av Månedst al Betaling Kunde- og sa lgsopl. Kundeopl. Indbe tal ing (Realiseret salg) Lokal del Adresseopl. (Image) (Stor/Lille) Re gnsk abstr an sa kt ion er Salgs opl. sa lg Kundenummer Kundenavn Månedligt aggregeret forb r ug sa lg Stregkode Hæ nd els e Vægt Da glige summer til fakt ur alin ie r Hæ nd els e Hæ nd els er Stregkoder Central del Faktura til udprintning Postens Mailbureau Da nsk E MS -fo r s. beh. i udl. Forespørgsel på forsendelse sa mt s va r Fordelingsnøgler Fordelingsnøgler Kundeoplysninger Faktureringsgrundlag for registrerede pakker Træ k på ko nt i BG Bank Ud l. EMS-f or s. beh. i Danmark The breeding ground for Data Quality Systemoversigt September 1998 Udgivet af : Data Administrationen (FOS-SS/SA/DA) LBS RAB SAC DCS (H59) KRS STA BPO FLY PLD Sk ranketerminal OCR sorteringsmaskine Bladadresseringsanlæg DIA HUS RKL NGL BSP BOA PRO BLA K-B OKS OCR OPD MVA CDS TID CDS PLS CCB PIA CPB MDL DART DC PAS VAX DS Danmarks statistik FPS SPB PFS EDI PMS D96 TML PPS ODS MIS BOS TNT PØS RIS LPD PTS GTC GTP IMS KOS PTS GTD CMB MSS FKS USF Internet STP Systemoversigt September 1998 PKV FLY MDC PKS SCANNER TTS PTS GTT FAK Udgivet af : Data Administrationen (FOS-SS/SA/DA) LBS RAB SAC DCS (H59) Sk ranketerminal OCR sorteringsmaskine Bladadresseringsanlæg DIA KRS STA NGL BSP BOA PRO BLA K-B OKS BPO PLD OCR RKL PAS OPD MVA CDS TID CDS PLS CCB PIA CPB MDL DART DC VAX DS Danmarks statistik FPS SPB PFS EDI PMS D96 TML PPS ODS MIS BOS TNT PØS RIS LPD PTS GTC GTP IMS KOS PTS GTD CMB MSS FKS USF Internet STP PKV MDC PKS SCANNER TTS PTS GTT FAK CRM ERP DW ODS Ent. Bus Billing For at minimere diagrammets kompleksitet, er nedenstående 'afhængigheder' ikke indtegnet. Signaturforklaring For at minimere diagrammets kompleksitet, er nedenstående 'afhængigheder' ikke indtegnet. Signaturforklaring STM KUN PST CPR register DS Danmarks statistik MOD RISC/6000 eller Supermax Eksternt PC VAX STM KUN PST CPR register DS Danmarks statistik MOD RISC/6000 eller Supermax Eksternt PC VAX ORG KST TOM Mainframe Alpha ORG KST TOM Mainframe Alpha DPB PDB STD ADG VAP Produktionsteknologisk udstyr Systemet findes flere gange på diagrammet DPB PDB STD ADG VAP Produktionsteknologisk udstyr Systemet findes flere gange på diagrammet Are you solving the problem or just adding to it?

4 Contents How did we get here? Can we really build a business case for MDM? Understanding the nature of MDM and its true cost to the business Approaching the business case top down An industry study - Banking 4

5 A derived business case A business case for MDM is nearly always an indirect business case. MDM is a foundational prerequisite for many IT related business initiatives. Having high quality and consistent data is only of value when it can be used to: Drives new business Reduces costs High Quality Data Reduces Risk 5 Increases agility

6 Useful motivators If you can sell bottled water, you can sell Master Data! 1 Fear will get you some of the way 2 as will hype 3 and if you re desperate, cynicism 6

7 What happens if we do nothing? 4 $? Costs Time You ll find broad agreement on inefficiencies but the business case is hard to quantify 7

8 Contents How did we get here? Can we really build a business case for MDM? Understanding the nature of MDM and its true cost to the business Approaching the business case top down An industry study - Banking 8

9 Have you factored in all of these in your business case? Scope Hierarchy Multi-Entity Enterprise / divisional Business Strategy alignment Geography Organisation Maturity/ Ambition Dependen cies Pain Workload Business Process Knowledge Project Priorities Policies Regulations Time to deliver benefits Politics Funding Sponsorship Change culture Divisional vs enterprise Technology/Infrastructure Architecture Tech choice Delivery capability EIM Integration Arch Target architectural style Synch/ Harmon. 9

10 What is the aspiration? SOE SOR 1. Data entry for Master Data attributes are dispersed Integration Approach 2. Dedicated SOE component is used for some Master Data attributes 3. Dedicated SOE component is used for some Master Data attribute sets 4. Dedicated SOE component is used for all Master Data attributes for one or more MDEs 5. Dedicated SOE component is used for all Master Data attributes (central maintenance) 1. Distributed SOR at the attribute level Data Entry Approaches 2. Centralized MDR for Master Data s identifying keys 3. Centralized MDR for a set of Master Data Entity attributes 4. Centralized MDR for a Master Data entity s attributes 5. Centralized MDR for all Master Data attributes (central maintenance) Consolidation Approaches Hybrid approaches Aspiration? There are a range of possible master data topologies what s right for your organisation?

11 MDM can be highly invasive Information Islands Introduce Master Data Repository (MDR) MDR Change System Of Entry Rationalize integration Reverse dataflow to OLTP Decommission MDR MDR Business functionality (reporting, calculation ) MDR Master data maintenance MDM can be extremely invasive in terms of operational s and will disrupt business process 11

12 Contents How did we get here? Can we really build a business case for MDM? Understanding the nature of MDM and its true cost to the business Approaching the business case top down An industry study - Banking 12

13 Connecting the dots to the strategy Strategic Goals Effeciency, Deliver Outstanding Customer Service, Growth Through M&A s, Compliance, Risk Management, One-Company Strategy Supporting Objectives Collaboration Across BU s, Agility, Increase Loyalty, Reduce Churn, Self Service, improve and understand customer needs, cross-sell/up-sell products Business Pains Lack of Business Insight, Resource Consumption, Opportunities Lost, unmanageable Risk, Inefficiency in Processes, Time to Market, Lost Customers, Credibility Suffering, Excessive Costs e.g. Procurement/Production/ Information Requirements Administration/Warehouse/Stocks Transparency, Trustworthy Information, Audit Trails, Readily Available, Documented Consensus, Simplicity, Optimized for Process Support Business Pains IT Requirements Data Warehouse, Master Data, Integration, Hamonization, Business Intelligence, Migration, Performance Management, Decommisioning Business Process Requirements Change Management, Data Management, Training, Governance, Process Mgt/Control/Improvement 13

14 Change is driven by pain Business pains Business opportunity MDM Business Case with benefits Organizational maturity 14

15 MDM in business terms: Common Master Data Usage Scenarios Collaborative usage scenario Objective: Ensure high quality and rigor in Master Data captured. Means: Workflows, check-in/out and approval to Control the Master Data authoring process. Analytical usage scenario Multiple Data capture Steps Validation before data becomes operational Integration Data Warehouse Analytics Data usage Reports Objective: Increase operational process efficiency through data consistency and timeliness. Means: managed data stores and near real time data synchronization. Data capture usage scenario Objective: Increase data completeness, reporting accuracy, and decision making Means: Master Data consolidation, alignment and cleansing Use business language put MDM in usage context 15

16 MDM Strategy at different levels IM Program IM Strategy Master Data Strategy -Mission - Architecture - Technology - Organization - Policies Some decisions are made for Master Data in general.. MDE Strategy MDE Strategy Product MDE Strategy Supplier.while others are made for each Master Data Entity separately to accommodate the differences MDE Strategy Customer Solution Projects Solution Project Charter A Solution Project Charter B 16

17 MDM HUB Architecture and Technology considerations Build What is your project delivery track record? In-house expertise? IT time to deliver/other initiatives. Buy Huge disparity in prices? Market consolidation/product maturity? Vendor expertise and experience? Reuse Is there an existing that might be fit for purpose? 17

18 Contents How did we get here? Can we really build a business case for MDM? Understanding the nature of MDM and its true cost to the business Approaching the business case top down An industry study - Banking 18

19 An Industry Study - Banking The fundamentals of banking have not changed in 400 years.. Products - Deposits, Loans, Exchange, Guarantees, Custody. Liquidity availability of funds to meet Customer demand Risk - Understanding & pricing Risk, Provision for bad debts Margin arbitrage, tempered by risk premium however their operational environment is rapidly changing Regulatory Compliance (Basel I / II / III, Privacy, AML, FATCA, etc) Customer demand for rapid response & a seamless experience Infinitely configurable products from mortgages to exotic derivatives Multi-channel engagement Merger & Acquisition Globalisation 19

20 More hard facts for building a business case Organisational Maturity Master Data pervades every process within your organisation To understand the end-to-end business case you will need detail statistics on organisational process takes time and effort to build / may never completely achieve Master Data initiatives cannot typically be justified in their own right Business Pains Be prepared for a negative NPV Does a mature, shared infrastructure cost model exist? Business opportunity Typical Approaches Fear, Uncertainty and Doubt will only get you so far, you need to validate and/or show results quickly Data quality initiative (show some numbers) Ride the wave / follow the money Piggy back an enterprise initiative (use data quality ) 20

21 To build a business case, we must express benefits in Business (Banking) Terms Quantative Benefits Increase Revenue Reduce Costs? Regulatory Compliance Even though the business may not trust the data, it will be difficult to build a business case on clean data alone There are typically three categories of business benefits for any initiative: Increased Revenue Up-sell and Cross sell are often touted as the biggest benefits, but require detailed statistics on current conversion rates to quantify. This also requires mastering of multiple entities (e.g. customer, account, product) and is the hardest to achieve Freeing up front-line resource from mundane effort may look to have the biggest benefit multipliers, but will this necessarily result in more sales? (there may be multiple initiatives that impact front-line efficiency) Cost Reduction Reduction in error rates and redundant/duplication of effort Often easiest to identify and pin-point, but may not be enough to support the entire project costs. Compliance A whole industry in itself, and potentially means you have a reduced requirement to justify (just have to do it!) Look at the fines for non-compliance, and correlate to bad data!

22 Cross-Sell & Up-Sell Single View of Customer To enable Cross & Up sell, you need to have a clear view of your customer interactions today this is a Business Intelligence problem Master Data Management may only be part of the answer Source: US Size of Retail Customer s Financial Spend, (Bradway Research, 2011) How does my share of wallet compare against the industry baseline? Why are we not competitive in areas of deficiency? How can we leverage areas of strength? These questions are answered by a Marketing Strategy, which will require a business change strategy to implement. An MDM hub is not a quick fix to solve this puzzle.

23 Cross-Sell & Up-Sell Go to Market Strategy When a clear view of the business problem/opportunity is defined we can define a business strategy to meet the opportunity Technology strategies need to align to this they are not a pre-requisite If large scale change is required, it is always prudent to test the model with a limited proof of concept exercise Source: Multi-Generational Lifecycle Value Potential, (Bradway Research, 2011)

24 1 Compliance & Cost Reduction There has been a mountain of new regulations across banking in the past 10 years these regulations have driven a significant increases in Economic Capital and others carry with them significant fines & penalties A very small number of customer data elements drive the vast majority of calculations used for capital provision poor Data Quality can be shown to have significant impact Provisions cover Expected Losses Economic Capital covers Unexpected Losses warchest surplus capital Buffer Surplus capital Insolvency Loss Risk $10 s to $100 s of Billion Earnings Risk Market Risk Operating Risk Credit Risk Source: Basel II Framework Diversification benefits

25 In the absence of an end-to-end analysis, follow the pain Simple sound bites or DQ pain points can be used to generate awareness of the cost of poor Master Data Management Increased Revenue Customer Application drop-out rates are you losing Customers because of your slow and error-prone application processes? Customer retention do you have high defection rates because of process/data errors causing Customer dissatisfaction? Time to Market for new Products are you losing first mover advantage because you cannot introduce new products efficiently? Cost Reduction Compliance Huge Backlog of DQ Issues what opportunities are you missing because your bad data remains bad? Correct Industry Codes: Under Basel II, industry codes directly drive capital allocation for bad debt provision absence of an industry code attracts the highest risk premium how clean are your customer industry codes? Customer Risk Grades again drive capital allocation are these correct across all s? Are they up-to-date? Generally the main driver. Significant growth in regulatory obligations, many which are specifically driving Master Data Quality

26 Delivery Considerations Tension between a multi-year investment and the business need for rapid returns Setting expectations Quick wins Reconciliation reporting (validates the problem) Data Quality activities Establishing the longer term viability Identify and leverage those who care Embed reconciliation and DQ as a Audit/delivery mandate Link DQ KPIs to bonuses Early establishment of ownership Who cares? Raise awareness and gather requirements Change Champions to sponsor and drive the ongoing work 26

27 Some Advice Link MDM to key business initiatives. E.g. position an MDM initiative as valuable support or a prerequisite for larger business solutions (preferably some strategic top-of-mind projects). Put the business people in the driver seat Data Quality is at the core of MDM and is concept that people understand start with DQ Even better, consider a feasibility piece upfront Consider your organisation s Information Management Maturity when setting your level of ambition - it will be a key contributor or hindrance to your success Don t forget to factor in the cost of ongoing operations and governance 27

28 Master Data Management Don Elliott e: Calvin Farmer e: 28

29 Who really cares? Tying data capture to the core business processes to identify ownership Banking Value Chain Pre-Sales Sales Origination Fulfillment Servicing Executive Entity Owner Customer Entity Business Data Owner Business Data Owner Business Data Owner Data Steward Data Steward Data Steward Relationship Banking Credit Risk Operations Process Owners Customer ID Contact Name Company Legal Entity Name CCR SI Industry Code Relationship Banking Pre-Sales & Sales Origination Pre-Sales & Sales Credit Risk Origination Operations Servicing Servicing Data field created in this process Data field updated in this process

30 Maturity assessment Think Locally, Act Locally At the initial stage an organisation has few defined rules and policies regarding data quality and data integration. The same data may exist in multiple applications, and redundant data is often found in different sources, formats and records. Companies in this stage have little or no executive-level insight into the costs of bad or poorly-integrated data. Think Globally, Act Collectively Reaching the Proactive stage of the maturity model gives companies the ability to avoid risk and reduce uncertainty. At this stage, data goes from an undervalued commodity to an asset that can be used to help organisations make more informed decisions. A Proactive organisation implements and uses master data repository solutions. $$ Think Globally, Act Locally A Reactive organisation locates and confronts data-centric problems only after they occur. ERP or CRM applications perform specific tasks, and organisations experience varied levels of data quality. While certain employees understand the importance of high-quality information, corporate management support is lacking. Think Globally, Act Globally At the Governed stage, an organisation has a unified data governance strategy throughout the enterprise. Data quality, data integration and data synchronisation are integral parts of all business processes, and the organisation achieves impressive results from a single, unified view of the enterprise. Confirm your capability and reconcile with your aspiration

31 The Role of a Master Data Repository - examples An MDR can play various roles in the s landscape to support different MDM usage scenarios The role will often differ between Master Data Entities Business Driver MDE Required Usage Scenario Know your customer (KYC)/US Patriot Act Customer/ Supplier and analytical Global Data Synchronization (GDS) Product Collaborative and operational Better Enterprise Reporting All Analytical Basel II Customer Analytical Customer Relationship Management (CRM) Customer/ Product and analytical Procurement Excellence Supplier/ Material and analytical Faster/New Product Introduction Product Collaborative 31

32 The External Data - Customers and Suppliers Why buy external party data? Access to accurate name and address data Access to corporate structures Know who you are actually doing business with Unethical organizations Organizations that are known to be bad payers How reliable are my critical suppliers? Get your standard classification of your business partners Minimize risk Access to predictive data about customers and suppliers Avoid doing business with restricted parties Largest global supplier is Dun & Bradstreet 32