The document discusses the challenges clients face with bad customer data, including inconsistent data between systems, lack of data standards and ownership, difficulty retrieving archived data, and high costs of data issues. It provides examples of data quality problems that have cost companies millions or billions of dollars. The document advocates implementing data management and architecture practices to address these challenges and ensure accurate, consistent and secure customer data.

1. What’s the price of bad customer data ? Breakfast Session 15 September 2009 Hotel Sofitel Brussels

2. Table of Contents Client Challenges How we respond Bad customer data ?

3. « I get two different results from two different systems and, guess what, they are both wrong. » « Our business strategy is for us to be an information- driven company and to justify our decisions with hard data but that data is usually unclear and inconsistent when I receive it . » « I receive numerous reports every day but they all use different terminology and data formats – what does this all mean? » « One of the administrative people had a report on their desk with our hiring and firing numbers from last month. They said they got it from the company portal and that there is all sorts of “nasty stuff” out there. This information is supposed to be confidential – why were they allowed to get it? » What clients tell us (1/3) Client Challenges

4. What clients tell us (2/3) « I was told that we should have standards for our data models and data names so I asked for a copy. What I received was 5 years old and didn’t even include the company we merged with in 2003. » « I am trying to determine why we have different formulas for Inventory in different systems. Doesn’t anyone own or have responsibility for this data throughout the company? » « I just reviewed a Data Model of our business to prepare for an acquisition. It looked very nice but it used difficult terminology , a very old business model and had nothing to do with our current organization and strategy. » « I need to do a study that looks at our sales for the last 5 years. I was told that the data is all archived but no one knows where it is or how to retrieve it. » Client Challenges

5. What clients tell us (3/3) « I just got the budget request from the IT department and they want to spend $25 million dollars in 2006 to double our storage capacity because that is how much data we are generating each year. Can’t we store some of this data somewhere else? There must be a cheaper alternative. » « We did a great Data Cleanup effort last year but the data is becoming corrupt again . Shouldn’t that effort made sure that errors don’t creep in again? » « We are being audited by the SEC and they want to see how we calculate our asset data. There are values here that don’t quite make sense – I know who entered them originally but who made all the changes to them? » Client Challenges

6. The threats (1/2) « Data Quality problems cost U.S. businesses more than $600 billion a year. » (TDWI) « NASA lost its $125M Mars Climate Orbiter because one group of engineers used kilograms and meters, while another used pounds and feet. The error caused the spacecraft to fly too close to the Martian surface where it either burned up or broke up as it swung around the planet. » « Rogue trader lost $691 Million due to lack of data governance. » « Average costs per data mart are $1.5M to $2.0M and a data mart consolidation initiative can reduce costs by 50%. » (META Group) « Master data problems leads to $250M law suit of a large investment bank. » Client Challenges

7. The threats (2/2) « Overcharging customers due to master data issues lands a large utility in a $1B law suit. » « For the U.S. grocery industry, up to 1 percent of net revenue lost, and one in 2,000 of sales lost because the item was out of stock, were attributable to bad master data. » (Gartner) « Retailers and manufacturers can reduce their current supply chain costs by 1 to 3 percent depending upon their current state. » (GCI) « Inaccurate and time consuming product information exchanges between CPG trading partners caused an estimated $25M - $50M in extra costs across the supply chain. » (AT Kearney Study) « 30% of all operational errors are due to poor information quality. » (Reuters) Client Challenges

8. Direct & Indirect Impacts of poor Data Quality Hidden in business processes , data maintenance and integration costs. Typical Problem Areas: Missing Data Ownership and Accountability Data owner is not clear Data owner maintains data quality for users in other organizations Data owners tend to treat data quality as low priority work Lack of Data and Business Process Integration Data not modeled to reflect business processes. Inconsistent Data Definition and Standardization Different names are used for the same data; Same data name refers to different definitions in different organizations. Ineffective Data Relationship and Classification Data relations are not appropriately defined or are out of date. Invalid Data Entry Data is mistyped (or judgmental error) into the system. The data creator often does not know how that data will be used later. Impacts: Extra Time to Reconcile Data Maintenance of extra, manual cleansing rules Loss of credibility in systems Proliferation of separate, off-line data sources Extra Costs Increased costs related to verifying data Delays in project deployment Over budget and delayed projects due to time-intensive data cleansing Compliance Issues Inability to adhere to local compliance standards Suboptimal Decision Making Corporate strategy and decisions negatively affected by data quality issues Lost Revenue Lost business opportunities due to inaccurate data quality Customer attrition looking for better service elsewhere Client Challenges

9. The bottom line Our clients face these challenges every day and they find them very difficult to solve ! And they have to meet legal requirements and regulations (example: Sarbanes-Oxley/SOX) They need to start managing their data now! Client Challenges Hard facts and figures are essential to making decisions in a high performance company. Managing by whims and instincts is becoming a path to extinction. Data is quickly becoming the lifeblood of an organization and a valuable enterprise asset. In the past, the focus has been on the use of the data but very little has been done to manage its quality and integrity.

10. Table of Contents Client Challenges How we respond Bad customer data ?

11. What is Data Management & Architecture ? Accenture’s Data Management and Architecture (DM&A) practice addresses how an organization manages its data. The fundamental focus of DM&A is ensuring that the data that underlies an organization is available, accurate, complete, and secure. DM&A is not just the technology to manage data. Effective data management includes Processes, People and Technology. How we respond Accenture Information Management Services Holistic Framework Business Intelligence Measure, Analyze, Optimize BI Diagnostic Custom Data Warehousing Large Data Warehousing ERP Data Warehousing Advanced Business Intelligence Delivery Business Intelligence Sourcing Portals & Content Management Document Lifecycle Document Management Imaging Web Content Management Records Management Business Process Management Collaboration Enterprise Search Data Management & Architecture Structured Approach Data Governance Data Structure Data Architecture Master Data & Metadata Data Quality Data Security Data Management & Architecture is part of Accenture Information Management Services

12. DM&A Capabilities Overview How we respond Data Governance Data Structure Data Architecture Master Data & Metadata Data Quality Data Security DM&A Capabilities Data Creation Data Storage Data Movement Data Usage Data Retirement Data Ownership Data Stewardship Data Policies Data Standards Data Modeling Data Taxonomy Data Migration Data Storage Data Access Data Archiving Data Retirement Master Data Management Reference Data Management Metadata Management Data Profiling Data Cleansing Data Monitoring Data Compliance Data Traceability Data Privacy Data Retention

13. DM&A Capability Definitions How we respond Data Security is the processes and technology to protect data from unauthorized access, viewing, modification or deletion whether the intent is accidental, intentional, or malicious. Data Security initiatives should be in concert with enterprise-wide Security efforts including physical security, network security and technology security. Data Security Data Quality is the ability of data to satisfy the stated business, system, and technical requirements of an organization. Data Quality is typically measure in terms of completeness, timeliness, accuracy, consistency, relevance, and integrity. Data Quality Master Data is the fundamental business data in an enterprise. Master Data is typically long-lived and used across multiple applications. Master data can also be considered the language of doing business – the business objects and classifications that describe overall business information. Well-managed Master Data typically consists of hundreds of categories including customers, products, suppliers, key performance indicators, etc. Metadata is structured information about data or, simply, “data about data”. Master Data & Metadata Data Architecture is the processes, systems and human organizations required to store, access, move and organize data. Data Architecture Data Structure is how data is organized in a specific enterprise. The Data Structure includes multiple levels of an enterprise ranging from overall corporate data models down to the level of an individual system. Data Structure Data Governance is how an enterprise oversees its data assets. Governance includes the rules, policies, procedures, roles and responsibilities that guide overall management of an enterprise’s data. Governance provides the guidance to ensure that data is accurate & consistent, complete, available, and secure. Data Governance

14. The DM&A Capabilities have a Process, People and Technology component DM&A Capabilities Process People Technology How we respond Data Governance Data Structure Data Architecture Master Data & Metadata Data Quality Data Security Compliance & Security Policies Local, National & International Laws Pre-test Anonymization Data Quality Rules & Policies Data Cleansing Standards Compliance Rules Data Definitions Master Data Metadata Reference Data Data Sizing, Storage & Movement Architecture Data Retention & Deletion Policies Phys Data Models Data Taxonomy Logical Data Models Business Process Flows Data Policies Data Standards Business Data Ownership Data Workflow Security Software Access Rights Management Data Audit Trails Data Anonymization Data Profiling, Quality & Monitoring Tools ETL Tools Audit Reports Master Data Mgt. Tools Reference Data Architecture Metadata Repository Archiving Tools Storage Management & Hardware Technical Architecture Data Modeling Tools Design/CASE Tools Data Rules Library Automated Notifications (Workflow) Corporate Security Auditors Compliance Dept. Data Administration Data Quality Services Team Data Governance DBAs Business Data Administration Data Stewards Data Owners Solution Architects Storage/ Technical Architects DBAs Enterprise Data Architects Data Modelers Data Stewards Business Data Owners Data Czar or Mgt. Committee

15. Our Value Proposition Improves the enterprise core data quality (i.e. quality of customers, products, suppliers, etc. data). Improves the decision quality by securing reliable, high quality master data “just in time”. Improves the availability of key data – speed of access, data timeliness, common (user) support. Improves all management activities through enhanced abilities to access consolidated data across the enterprise and more quickly integrate new data (new customers, new products, etc.). Eliminates/prevents redundant & non-coordinated data quality activities within the company. Enables development projects to deliver consistent high quality results faster and cheaper . Reduces cost of manual data reconciliation & alignment efforts, error fixing, etc. Reduces the data redundancy cost by consolidating & eliminating duplicate masters. Reduces reporting costs as a result of continuously consistent and updated data. Reduces application development & maintenance costs , by having clear data quality standards. Reduces redundant SW/HW purchase & license costs associated with disparate systems Enables 360º view of the enterprise and corporate performance management improvement. Provides competitive advantage by enabling customer behavior insight & predictive modeling. Enables process automation and faster data exchange also with business partners . Improves the forecast management through more effective logistics and inventory control. Increases the accuracy and the return of marketing campaigns . Enables effective customer retention/churn management by providing integrated master data. Improves the customer service quality by enabling higher responsiveness to customer needs. Offers a robust solution able to support business change & growth in the right pace & price. Improve “profitability” by optimizing cost to serve for both low cost margin customer & high-value customer segments Provides optimized margins by category and product consistently Improves “time-to-market” efficiency by providing more accurate master data in a timely manner. Revenue Quality Cost Value Proposition Many factors support a strong business case for effective Data Quality Management How we respond

16. « I get two different results from two different systems and, guess what, they are both wrong. » « Our business strategy is for us to be an information- driven company and to justify our decisions with hard data but that data is usually unclear and inconsistent when I receive it. » « I receive numerous reports every day but they all use different terminology and data formats – what does this all mean? » « I call them Products, other people call them Parts, one system calls them SKUs – and our customers call them by their catalog names and numbers – are they all the same thing? » « One of the administrative people had a report on their desk with our hiring and firing numbers from last month. They said they got it from the company portal and that there is all sorts of “nasty stuff” out there. This information is supposed to be confidential – why were they allowed to get it? » What clients tell us (1/3) Data Structure Master Data & Metadata How we respond Data Governance Data Quality Data Governance Data Quality Master Data & Metadata Data Security Data Structure

17. What clients tell us (2/3) « I was told that we should have standards for our data models and data names so I asked for a copy. What I received was 5 years old and didn’t even include the company we merged with in 2003. » « I am trying to determine why we have different formulas for Inventory in different systems. Doesn’t anyone own or have responsibility for this data throughout the company? » « I just reviewed a Data Model of our business to prepare for an acquisition. It looked very nice but it used indecipherable terminology, a very old business model and had nothing to do with our current organization and strategy. » « I need to do a study that looks at our sales for the last 5 years. I was told that the data is all archived but no one knows where it is or how to retrieve it. » Data Governance How we respond Data Structure Data Governance Master Data & Metadata Data Governance Data Structure Data Architecture

18. What clients tell us (3/3) « I just got the budget request from the IT department and they want to spend $25 million dollars in 2006 to double our storage capacity because that is how much data we are generating each year. Can’t we store some of this data somewhere else? There must be a cheaper alternative. » « We did a great Data Cleanup effort last year but the data is becoming corrupt again. Shouldn’t that effort made sure that errors don’t creep in again? » « We are being audited by the SEC and they want to see how we calculate our asset data. There are values here that don’t quite make sense – I know who entered them originally but who made all the changes to them? » Data Governance How we respond Data Architecture Data Quality Data Quality

19. The threats (1/2) « Data Quality problems cost U.S. businesses more than $600 billion a year. » (TDWI) « NASA lost its $125M Mars Climate Orbiter because one group of engineers used kilograms and meters, while another used pounds and feet. The error caused the spacecraft to fly too close to the Martian surface where it either burned up or broke up as it swung around the planet. » « Rogue trader lost $691 Million due to lack of data governance. » « Average costs per data mart are $1.5M to $2.0M and a data mart consolidation initiative can reduce costs by 50%. » (META Group) « Master data problems leads to $250M law suit of a large investment bank. » How we respond Data Quality Data Governance Data Governance Data Architecture Master Data & Metadata

20. The threats (2/2) « Overcharging customers due to master data issues lands a large utility in a $1B law suit. » « For the U.S. grocery industry, up to 1 percent of net revenue lost, and one in 2,000 of sales lost because the item was out of stock, were attributable to bad master data. » (Gartner) « Retailers and manufacturers can reduce their current supply chain costs by 1 to 3 percent depending upon their current state. » (GCI) « Inaccurate and time consuming product information exchanges between CPG trading partners caused an estimated $25M - $50M in extra costs across the supply chain. » (AT Kearney Study) « 30% of all operational errors are due to poor information quality. » (Reuters) How we respond Master Data & Metadata Master Data & Metadata Master Data & Metadata Master Data & Metadata Data Quality

21. Data Governance Definitions DM&A Definitions Data Ownership is the responsibility for the creation of the data, and the enforcement of enterprise business rules. Data Owners usually refers to the business owners of Master/Business Data. Data Ownership Data Stewardship is the accountability for the management of data assets. Data Stewards do not own the data; instead they are the caretakers of the enterprise data assets. The Data Stewards ensure the quality, accuracy and security of the data. Data Stewardship Data Governance is how an enterprise manages its data assets. Governance includes the rules, policies, procedures, roles and responsibilities that guide overall management of an enterprise’s data. Governance provides the guidance to ensure that data is accurate & consistent, complete, available, and secure. Data Governance Data Standards are the precise criteria, specifications, and rules for the definition, creation, storage and usage of data within an organization. Data Standards include basic items like naming conventions, number of characters, and value ranges. Data Standards may also dictate specific quality measures, retention rules, and backup frequency. Data Standards Data Policies are the high-level and/or detailed rules and procedures that an enterprise utilizes to manage its data assets. Data Policies might include adherence of data to business rules, enforcing authentication and access rights to data, compliance with laws and regulations, and protection of data assets. Data Policies

22. Data Structure Definitions DM&A Definitions Data Taxonomy is the classification of data within an enterprise. An alternate definition is that Data Taxonomy is the terminology used within an enterprise when looking at its data. Data Taxonomy applies to both structured and unstructured data. The Data Taxonomy could be the product catalog including components and part numbers (structured data) and it could be the classification or grouping of documents (unstructured data). Data Taxonomy Data Modeling is the creation of Data Models that capture business requirements and present them in a structured way. Data Modeling enables an enterprise to communicate its data entities, attributes, and relationships, support system development and maintenance projects, and underlay most enterprise data initiatives. Data Modeling is generally done at both the Enterprise and Business Unit levels. Data Modeling Data Structure is how data is organized in a specific enterprise. The Data Structure includes multiple levels of an enterprise ranging from overall corporate data models down to the level of an individual system. Data Structure

23. Data Architecture Definitions DM&A Definitions Data Storage is the physical storage of data on an enterprise’s (or outsourcer’s) hardware. Data Storage Data Access is the various mechanism used to view, add, change, or delete data. Data Access includes transactional, analytical, and archival systems. Data Access Data Migration is the automated movement or migration of enterprise data such as from a transactional data base to a specific data store. Data Migration is sometimes defined to also include the migration of data from transactional systems to data archives. Data Migration Data Architecture is the processes, systems and human organizations required to store, access, move and organize data. Data Architecture Data Retirement is the removal of data from Data Storage. Data Retirement is not simply the deletion of data. Data Retirement is a process that may include long-term retention of key information and historical data for future analysis or reuse. Data Retirement must adhere to Local and National laws especially as it relates to Data Privacy. In some circumstances, data may be unretired such as a transaction with a former customer. Data Retirement Data Archiving is the storage of an enterprise’s data on a secondary storage medium. Data is archived to minimize the cost of online data storage. Depending on the archiving process and technology, archived data can be accessed in near real-time or only after an extended period. Data Archiving

24. Master & Meta Data Definitions DM&A Definitions Metadata is structured information about data or, simply, “data about data”. Metadata DM&A considers Reference Data to be a form of Master Data. Reference Data can sometimes be defined as code/decode data or external coded information. Reference Data Master Data is the fundamental business data in an enterprise. Master Data is typically long-lived and used across multiple applications. Master data can also be considered the language of doing business – the business objects and classifications that describe overall business information. Well-managed Master Data typically consists of hundreds of categories including customers, products, suppliers, key performance indicators, etc. Master Data Metadata Management is the tools and processes used to manage Metadata. Typically there are three types of Metadata that is managed: 1) Business metadata; 2) Technical metadata; 3) Operational metadata. Metadata Management is used to define, create, update, migrate, and disseminate metadata throughout an enterprise. Metadata Management DM&A Considers Reference Data Management to be synonymous with Master Data Management. Reference Data Management Master Data Management (MDM) is the collection of processes and technology that ensures that Master Data is coordinated across the enterprise. MDM provides a unified Master Data service that provides accurate, consistent and complete Master Data across the enterprise and to business partners. Master Data Management

25. Data Quality Definitions DM&A Definitions Data Monitoring is the automated and/or manual processes used to continuously evaluate the condition of an enterprise’s data. Information obtained from Data Monitoring activities is used to plan and focus data improvement initiatives. Data Monitoring Data Compliance is the ongoing processes to ensure adherence of data to both enterprise business rules, and, especially, to legal and regulatory requirements. Data Compliance includes 4 items: Controls, Audit, Regulatory Compliance & Legal Compliance. Data Compliance Data Traceability is the tracking of the lifecycle of data to determine and demonstrate all changes and access to the data. Data Traceability helps an enterprise demonstrate transparency, compliance and adherence to regulation. Data Traceability along with Data Compliance can be considered part of a Data Audit process. Data Traceability Data Cleansing is the process of detecting and correcting erroneous data and data anomalies both within and across systems. Data Cleansing can take place in both real-time as data is entered or afterwards as part of a Data Cleansing initiative. Data Cleansing Data Profiling is the systematic analysis of data to gather actionable and measurable information about its quality. Information gathered from Data Profiling activities is used to assess the overall health of the data and determine the direction of Data Quality initiatives. Data Profiling Data Quality is the ability of data to satisfy the stated business, system, and technical requirements of an organization. Data Quality is typically measured in terms of completeness, timeliness, accuracy, consistency, relevance, and integrity. Data Quality

26. Data Security Definitions DM&A Definitions Data Retention defines the policies and rules that an enterprise utilizes to keep data online, in archives, and in backups. Data is generally retained for regulatory and legal reasons as well as for historical analysis or Business Intelligence. Data Retention Data Privacy is the legal right and expectation of confidentiality in the collection and sharing of data. Data Privacy is an evolving area with numerous local and national laws. Data Privacy is also known as Data Protection. Data Privacy Data Security is the processes and technology to protect data from unauthorized access, viewing, modification or deletion whether the intent is accidental, intentional, or malicious. Data Security initiatives should be in concert with enterprise-wide Security efforts including physical security, network security and technology security. Data Security