Effective Data Governance A Practical Guide to Implementing Corporate Data Governance Using Master Data Management Solutions

Riversand Technologies, Inc. Powering Accurate Product Information Effective Data Governance A Practical Guide to Implementing Corporate Data Governance Using Master Data Management Solutions A Riversand Technologies Whitepaper

Table of Contents 1. Introduction... 3 2. The Data Governance Business Problem... 4 3. Data Governance and MDM... 5 4.1. Data preparation... 8 4.2. Data Quality Measurement... 11 4.3. On Going Data Quality Tracking & Improvement... 12 4.3.1. Data Profiling... 12 4.3.2. Data Standardization... 12 4.3.3. Data Cleansing... 12 4.3.4. Data Enrichment... 12 4.3.5. Duplicate Prevention... 13 5. Supporting Data Governance Processes... 15 6. Business Rules and Data Validations... 17 7. Data Access and Security Control... 18 8. Organizational Considerations... 19 9. Conclusions and Call to Action... 21 Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 2 of 22

1. Introduction Many of today s large, global companies are developing Data Governance initiatives to implement policies, procedures and organizations to manage and improve strategic corporate data assets. A primary goal of these initiatives is to establish authoritative, highly accurate master data which can be leveraged throughout an organization. These initiatives offer substantial benefits defining Data Governance policies, procedures and organizations and achieving effective Master Data Management (MDM) will allow companies to ensure high levels of data quality. Data Governance initiatives have been proven to be especially important for large organizations with global and/or decentralized operations (e.g. multiple autonomous business unit, enterprise systems, geographies and/or brands). However, Data Governance initiatives are crucial to any organization looking to improve data quality metrics and their overall business results. The best results are achieved when Data Governance and Master Data Management (MDM) are complementary initiatives, each having a direct, positive impact on the success and business value of the other. In fact, corporate Data Governance strategies should be implemented in concert with Master Data Management solutions. This is because MDM solutions offer a single repository for data management and the systems synchronization capabilities needed to implement and deploy Data Governance policies in a central location. Effectively and efficiently aggregating and transforming raw data into master data or a single golden version of the truth enables companies to better support business processes, ensure enterprise system consistency, reduce errors/costs, sell and market more effectively and operate more efficiently. MDM solutions offer a robust, centralized, and collaborative environment for key stakeholders and data stewards to transform raw data into master data. MDM solutions also provide the capability to implement workflows, access controls and business rules needed to coordinate tasks across the various stakeholders within an organization in order to orchestrate the governance of data, metadata and data relationships. This whitepaper will discuss how leading companies are using Riversand s MDM solution, MDMCenter, to support MDM projects and Data Governance policies, processes and organizations. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 3 of 22

2. The Data Governance Business Problem To rule is easy, to govern difficult. Johann W. von Goethe In today s information driven business environment, data management and overall data quality are corporate imperatives. Capturing and leveraging comprehensive data on various key corporate entities such as customers, products, vendors, employees, assets etc. is critical to business success. Unfortunately, many companies struggle to effectively manage all of their data assets. Companies continually fail to realize data driven strategic insights and operational efficiencies. These companies are forfeiting significant business benefits and they will be at a disadvantage against more sophisticated competitors. Many companies are hindered by poor data quality. The impact of poor data quality has been quantified by a number of leading analyst firms. For instance, a study by A. T. Kearney found that: Nearly 30% of the item data in catalogs used by retailers and manufacturers is incorrect. Correcting those errors costs between $60 and $80 each. Companies spend an average of 25 minutes per SKU per year manually cleansing out of sync item information Nearly 60% of all invoices generated have errors; each invoice error costs enterprises from $40 to $400 to reconcile. 43% of all invoices result in some form of deduction In addition, a study by AMR Research found: 10% 50% of orders require manual rework due to bad data 37% of invoices error out due to bad item numbers or prices. 30% inbound shipments need reconciling as Item numbers don t match with those on file. 47% of supplier invoices don t match original purchase order 68% of invoices have pricing exceptions For its part, Gartner reports that 25% of critical data within large businesses is somehow inaccurate or incomplete. Gartner also reports that 50% of failed or delayed IT implementations are due to lack of attention to data quality issues. 1 The business impact of poor data quality is clear and companies are investing in Data Governance and Master Data Management initiatives to correct the situation. 1 Enterprise Data World, May, 2009 Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 4 of 22

3. Data Governance and MDM We shape our tools and afterwards our tools shape us Marshall McLuhan Over the years, companies have made significant investments in data management and data quality. But why do some of these efforts fail to offer maximum ROI? In the past, many companies have adopted an independent, standalone Data Quality tool (in some cases multiple tools) to support their Data Governance initiatives. This approach is myopic the tool deployed becomes a limiting factor in achieving overall Data Governance goals. Although standalone data quality tools have their place in the enterprise, they are not designed to provide many of the overall breadth of capabilities needed for a holistic approach to Data Governance. Years of experience has shown us that if customers do not adopt a more robust set of data governance capabilities then the solution becomes a short term fix and future projects will be needed. Master Data Management offers the robust set of tools needed to successfully deploy corporate Data Governance. Gartner defines MDM driven governance as the specification of decision rights and an accountability framework to encourage desirable behavior in the ongoing authoring, storage, enrichment, publishing, consumption and maintenance of master data. It specifies the processes, roles, standards and metrics that ensure the effective and efficient use of master data in enabling an organization to achieve its goals. In organizational terms, it needs to bring together individuals from different parts of the organization and different levels of seniority to fulfill different roles related to data quality. The model of the different branches of the U.S. and other governments, comprising the executive, legislative and judicial branches, plus the administrative branch, can be a useful guide. The executive branch (top management) sets, and hopefully owns and communicates, the business vision, providing executive sponsorship. The legislature (the business unit stakeholders) discusses and specifies policies and processes. The judicial branch (a steering group of senior management) provides an arbitration facility to ensure decision making when the stakeholders can't agree. Finally, the administrative branch (data stewards and end users) enacts the policies and processes on a day to day basis. This model is analogous to a Data Governance organization s role in managing data quality within an organization. By implementing advanced MDM solutions, customers have been more successful in attaining their overall Data Governance goals. Data cleansing & MDM implementation projects are typically run in parallel with initial data loads loaded in the production MDM system before go live. To meet these needs as well as on going data quality requirements, Master Data Management solutions have evolved to provide broad, robust and integrated capabilities to maximize the scope and value of Data Governance initiatives. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 5 of 22

And independent research validates our claims. Aberdeen Group looked at the impact of MDM to Data Governance initiatives. Among the surveyed companies that Aberdeen considers "best in class" those using MDM solutions to and focus on customer data quality problems the results were striking: 94 percent reported improved data integrity; 95 percent reported improved data usability; and 89 percent reduced the time required to make customer data ready for business use.2 More recently, Information Difference conducted a comprehensive study which investigated the relationships between data governance, master data management (MDM) and data quality (DQ). The aim was to find the current state of practice in data governance, the levels of effort involved and its success rate, and understand how well integrated it is with data quality and MDM initiatives. The key findings from the survey are summarized below: Among the 257 respondents, 31% have already implemented data governance and have had active data governance implementations for a median of 2 years. A further 40% plan to implement within one year. A significant number of organizations (39%) are electing to implement data governance alongside MDM (and data quality). Only 18% of organizations with data governance in production have implemented this stand alone ; most have done so in combination with data quality and/or MDM. Organizations are ensuring that business plays a central role in owning and driving data governance. Roughly one third had ownership in the IT camp, the rest jointly or solely with the business camp. 80% indicated that they were measuring data quality (93% of those planning to implement data governance intend to measure data quality). Among those planning to implement data governance, 67% told us they intended to use functionality which they expected to find in data quality or MDM tools, 24% plan to use spreadsheets and only 14% considered building their own tools. Of those in production with Data Governance, 37% have already implemented both data quality and MDM, and a further 32% have already implemented data quality or MDM and plan to complete implementation of both within the year. The top two benefits of data governance (for both those already in production and those planning implementations) were strongly focused on the speed and quality of decision making ( better quality and faster decision making and ability to respond faster to business change ). 2 Aberdeen Group Winning Master Data Management Strategies for 2008 2009 Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 6 of 22

68% had named individuals with the authority to resolve the inevitable disputes regarding data definitions and ownership. Also about half those in production had a formal job description for their data stewards (or equivalent roles). 74% of those planning to implement considered that implementing data quality was essential to a successful MDM implementation. 3 Riversand s integrated MDM solution, MDMCenter, has advanced capabilities to better support Data Governance initiatives. Key areas of our integrated MDM solution important for Data Governance initiatives include: Providing Users/Experts/Data Stewards access to a single central version of the truth of master data to work on the data for which they are responsible A flexible data modeling environment so all corporate data can be staged, cleansed and promoted to production master data environments An integrated workflow management to ensure tasks and approval processes provide all stakeholders at acting in an orchestrated fashion Integrated data validations and Data Quality Management ensures that all of the data being worked on (either via bulk upload or manually) by Data Stewards/Experts meets corporate standards Ensures all Cleansed data can be syndicated to subscribing systems through manual, scheduled or triggered syndication, ensuring master data is distributed to all subscribing systems in a highly automated fashion Administrators can establish access rights (view/read/write/edit) for modules, functionality or data down to the attribute level so that data stewards are limited to certain tasks and areas of responsibility Advanced search and data visibility to limit data duplication Providing reports which illustrate, quantify, and benchmark data and business processes across the entire organization (including drill down by product, division or brand) For our customers, the modules of Riversand s MDM solution, MDMCenter, and Riversand s expertise come together to support advanced Data Governance organizations, processes and policies. It is important to note that just as MDM is crucial for Data Governance, effective Data Governance is vital to MDM projects. Without a Governance strategy, an MDM initiative will most likely fail. It is important that a Data Governance framework is created at an early stage in an MDM project and the Governance framework for MDM should be seen as part of a wider need for governance of all information assets. 3 How Data Governance Links Master Data Management and Data Quality, The Information Difference Company, Aug 2010, Page 1 51 Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 7 of 22

4. Data Quality Metrics and Goals If you cannot measure it, then you cannot improve it Lord Kelvin One of the first steps in any Data Governance initiative is to establish an organization s Data Quality Metrics and Goals. Riversand works collaboratively with our customers to identify and quantify key data quality metrics and goals. It is very important that these metrics and goals are: Relevant to the Business Quantifiable Actionable In practice, establishing data governance metrics and goals is an evolutionary and iterative process. There are a few key steps in this process: 1. Data preparation 2. Data quality measurement 3. Ongoing improvement and tracking of data quality metrics. 4.1. Data preparation Typically, the first stage of a Data Governance project is to identify and measure the current state of a company s data. The data from customer s current system (or a decent sized sample set) can be uploaded as is to Riversand MDMCenter s staging environments for a data quality assessment. Then Riversand and our clients can analyze the data using integrated tools and run various data quality reports on initial data loads for Riversand Professional Services and clients to interpret. Some examples of the out of the box reports that can be run are: 1. Missing values 2. Wrong classification 3. Wrong values (attribute values not available in the valid value list / table) 4. Style Guide Deviation (deviation from schema definition like attribute lengths, data types, precision, data lengths, etc.) Additionally, various summary reports can also be generated like 1. Fill percent for attributes: What percentage of data is populated (global or by attribute) 2. Valid data percentage: What percentage of data is valid (global or by attribute) Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 8 of 22

In addition to all these, Riversand s MDMCenter also has ad hoc reporting capabilities where customers can create ad hoc reports on the data and group the data based on different needs. Since these reports can be generated, saved and published on a regular basis, it allows the management to see the current state of data and also the overall progress being made within the application. These reports can be then be exported in various different formats and progress tracked. See screenshots below for examples: Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 9 of 22

These reports are just a few examples of the data analysis capabilities within MDMCenter. This initial analysis helps establish a baseline for on going data quality measurement. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 10 of 22

4.2. Data Quality Measurement Data Quality is a multi dimensional concept. It can be assessed based on a number of different criteria including: 1. Completeness Are data values fully populated? Does the data fully represent the entity? 2. Authority Is the data believable and creditable to the business? 3. Consistency/Normalization Does the data have consistent units of measure or same format? 4. Relevancy/Value Add Is the data important to the business? 5. Accuracy Is the data correct? 6. Timeliness How current is the data? 7. Objectivity Is the data unbiased and owned by the appropriate stakeholder? 8. Accessibility and Availability Can the data be accessed when needed? Riversand s MDMCenter measures data quality across all of these dimensions. For example, a Data Completeness data reports Riversand has created for customers is shown below: An example of a Relevancy/Value Add Report is provided below: Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 11 of 22

4.3. On-Going Data Quality Tracking & Improvement Riversand s solutions provide a five step path to achieving high data quality. The five steps include: 1. Data Profiling 2. Data Standardization 3. Data Cleansing 4. Data Enrichment 5. Duplicate Prevention 4.3.1. Data Profiling The first step is to profile the current state of your data to understand the nature and scope of the specific quality problems needing correction. 4.3.2. Data Standardization Data Standardization is important to organizations because it improves matching success for legacy applications. For example, should Road in an address be spelled out, or abbreviated? If abbreviated Rd. with a period, RD with no period, or some other way? Riversand will recognize all the variations, and let you create a common, consistent representation that will improve search success with conventional software. 4.3.3. Data Cleansing The data profile reveals problem areas in your data. The next step is to resolve those problems. Data cleansing requires eliminating duplicates, identifying related records across multiple databases, and reconciling inconsistencies. Riversand has the tools and expertise to efficiently and systematically eliminate duplicates. 4.3.4. Data Enrichment Data profiling often reveals missing information blank fields, or (more problematically) fields which contain default or invalid data (which are unlikely to be correct). Riversand provides probabilistic based profiling tools that can identify a significant number of such records, and reporting tools that help you decide what to do about them. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 12 of 22

4.3.5. Duplicate Prevention Once duplicates have been eliminated from existing data, the rate of new duplicates can be cut substantially in two ways: Eliminating duplicates at the source. Riversand software technology enables users to find existing records despite inconsistencies or inaccuracies. Riversand can implement advanced parametric or unstructured search capabilities tightly integrated with existing applications, or running independently of them. Either way, there s no reason to replace existing systems investment. With Riversand, even search queries that contain only fragments of the data will often yield the desired record, eliminating duplicate creation at the source. Monitoring new records after they re created. The same software that recognized duplicates and overlays in the original data can continue to work on new records as they re created. In the unlikely event that an operator creates a duplicate or overlay, the software compares the new record against the current (clean) database, and flags potential problems for administrators to address. This on going monitoring includes both structured and unstructured attributes. Riversand s Data Quality Management module, integrated with MDMCenter and our Workflow module, provides a powerful solution to model complex and flexible Data Governance strategies. Organizations, policies and approval workflows are all set via a single interface and are immediately enforced. This module enables companies to easily track and improve data quality on an on going basis. In addition, Riversand s MDMCenter solution provides a seamless solution for data staging, validation, approval and single click publication to an enterprise master data repository. Because our Solution is web based and includes advanced security and access permissions, all relevant stakeholders whether they are vendors, employees, partners (including Riversand) or customers can be involved in the on going process of Data Governance and Data Quality Management. There are additional aspects of MDMCenter that can be used to constantly monitor and react to the quality of data on an on going basis. For instance, MDMCenter s validation capabilities can also be used to generate a quick report on all the errors for a specific set of items or the entire catalog. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 13 of 22

Below is a snapshot of the report generated by the validation service: The validation service creates a report that goes down to the smallest level of error in the data, like missing attribute values, invalid data based on data type, invalid data as per valid values, etc. Another capability within MDMCenter is the concept of data state. Various states can be defined for an item, each state measuring one or many different aspects of the quality and completeness of an entity. Some example states that are available are: all required attributes populated, vendor specified, all attribute data valid, at least one accessory available, all attributes populated, item duplicate, item has a match in master, item classified, address complete, etc. Furthermore, MDMCenter s business rule engine constantly monitors any updates to the entity to keep the state values up to date. Every entity when viewed also displays these states for the user to indicate any errors or incomplete conditions. Below is an example of how state data is shown within a sample data record within MDMCenter: Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 14 of 22

Another key capability for maintain data quality over time is Search and Data Visibility. MDMCenter s Entity Explorer saved search functionality can be used to create various different kinds of searches based on the state and validation service results to consolidate and get a full summary of the current state of data. It also helps to avoid data record duplication. Therefore, MDMCenter allows users to identify records based on key criteria and completeness and quickly assign items to individual groups of users to work on. This makes the overall process of measuring the quality of data, assigning the error or incomplete data to the right user and, most importantly, fixing the data error a completely seamless process. This integral data quality services along with other MDM services makes MDMCenter an industry leader and visionary. Another important aspect for maintaining data on ongoing basis, is implementing good data governance processes. Following are some examples: 1. How will users search for item they are looking for? 2. How will users create a new item request? 3. Who will cross verify this request? Usually it s done by a Data stewards/sme. 4. How is approval of new item & promotion to the enterprise catalog achieved? 5. What are the change management processes for adding new classes or attributes? Riversand has recognized and met the needs of organizations to collaborate across a global organization to implement business, data governance and master data management processes. 5. Supporting Data Governance Processes Time is the measure of business. Sir Francis Bacon Riversand assists our customers in understanding, optimizing and implementing their Data Governance processes with our solution. During the Requirements Gathering and Design phase of the project, Riversand s team works with customers to identify the notification needs for every Data Governance use case and then appropriately recommend the best approach for modeling the workflow. Based on the requirements, Riversand can then recommend the best practices for workflow setups, so that the client can configure existing workflows or design new workflows as a part of the ongoing process. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 15 of 22

Riversand s Workflow module has delivered breakthrough improvements in productivity related to the processes associated with defining and implementing Data Governance policies. The solution allows for changes in data, business rules, data models or governance policies to efficiently move through the appropriate review and approval workflow. Some of the key benefits of Riversand s Workflow module are: Improved processes for greater efficiency Higher productivity Lower costs Effective collaboration and communication Advanced Workflow facilitates collaboration and communication within the enterprise Better resource utilization Uninterrupted operations Provides your resources anytime, anywhere access to business processes, documents and other artifacts to ensure uninterrupted operations. Greater accountability and traceability Advanced Workflow immediately brings transparency, traceability and accountability to Data Governance processes. Every step in the workflow has a rich set of configurable properties to completely define the step. For most of the steps in the workflow, escalations and timeout can be configured to ensure users act on their work items in a timely fashion. Any time the escalation times out, users will receive notifications with information about escalations and potential change of ownerships. The current user and the manager both can be set to receive notifications. And over time, Data Governance processes can be tracked, benchmarked and optimized to improve efficiency. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 16 of 22

6. Business Rules and Data Validations Principles and rules are intended to provide a thinking man with a frame of reference. Karl Von Clausewitz A key differentiator for MDMCenter is its integrated Data Quality Management (DQM) module which offers data cleansing, normalization, enrichment, classification, data review and quality checking capabilities. The DQM module ensures that data flowing in and out of MDMCenter is accurate, normalized, complete and of high quality. DQM capability is pervasive throughout the MDMCenter Solution, helping user migrate and maintain clean data. The DQM module allows for one or many administrator roles to assign and manage work across a team of users, including Riversand s Data Quality Solutions Content Experts (or other 3rd party stakeholders). MDMCenter s Data Quality Management module automates and streamlines workflow processes to substantially decrease the effort and time involved to review, approve and stage content in a format simple for publishing to Master Catalogs or enterprise systems. Riversand clients are able to quickly and easily implement their Data Governance strategies using this module as part of their overall MDM strategy. The MDMCenter Data Quality Management module enables automated data cleansing, content review and quality checking. Riversand s tool allows for one or many administrator roles to assign and manage work across a team of users. The power of MDMCenter s Data Quality Management module automates and streamlines workflow processes to substantially decrease the effort and time involved in implementing Governance policies throughout an organization. The product integrates seamlessly into the MDMCenter interface to enables effective review, approval and staging of content in a format simple for syndication to Riversand s data master repository or other enterprise systems. In addition to our technology, Riversand provides a comprehensive range of Data Quality Services that encompasses the content creation process. Riversand has experienced resources to augment customer teams to support this process. Our teams consist of domain experts in a number of areas such as Content Analysts, Classification Experts, Compliance Specialists, Quality Analysts, and Chemicals Domain Experts. These resources are either onshore or offshore to ensure a cost effective, efficient team for our clients. In addition to Riversand s Content services, Riversand has extensive experience integrating its Solutions with additional 3rd party content providers for data enrichment. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 17 of 22

Riversand s DQM module s capabilities include the following: Multiple Taxonomy/Schema Definition and Management Classification Attribute Extraction and population to schema Data Enrichment Normalization Description Auto generation Duplicate Identification & Advanced Matching Data Staging The MDM solution will be the house and engine for all data governance rules and the cleansed master data. A place where rules can be easily maintained and also applied onto new master data or even rule changes massively applied retroactively. A place where new data can be easily and efficiently compared with old data as a reference for consistency and standards. A place where a DMO or just data stewards can analyze, slice and dice, and report on existing master data and take short and long term business decisions based upon. 7. Data Access and Security Control Efficiency is doing things right; effectiveness is doing the right things. Peter Drucker To ensure that data stewards work efficiently and focus on their assigned responsibility, MDMCenter uses a complex security model that controls access down to the attribute level. Users are associated to roles and security can be specified at the role or user level for granular control. Access is granted based on the Organization(s) that the user/role belongs to and they can access only the data that belongs to their organizations. The security model in incorporated into the workflow process allowing for controlled access to specific data in different stages of the workflow. The security model can be integrated with 3rd party systems including Microsoft Active Domain. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 18 of 22

8. Organizational Considerations An organization's ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage. Jack Welch To manage Data Governance across the enterprise, companies need an active Data Management organization (DMO) in place. While systems, software, technical people, etc. are commodities that tomorrow, and even today, can be found and bought outside the organization, data cannot. The organization s data is the only real IT asset that cannot be purchased or produced outside the organization, and thus a DMO is a must. However, companies typically have many questions related to developing an effective DMO. What is a DMO? What does it do? Here are some of the responsibilities of a DMO: Data Foundation: If there is an MDM system in place, the DMO will own it Data Governance: Will manage and maintain data business rules Data Quality: Will set, enforce, and monitor minimum levels of the data quality required for the organization Data Processes: Will set and maintain data workflows and processes within and across the different business units Data Operations/Changes: Will analyze, assess, and the impact of data changes is controlled and leveraged for the benefit of all business units Should a DMO report to the business leadership or to IT leadership? If the IT organization is mature enough, the DMO could report to IT leadership and could be effective in its mission to manage the enterprise s data. Who typically works for a DMO? A DMO typically consists of people who manage structured data, documents, content, information security, identity data, and the delivery of useful business information to business people. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 19 of 22

What is a typical Organization Chart for a DMO? One role in the above chart that might be new for some companies is the Master Data Steward. In order to do assign accountability to assess, manage and report on data quality, many companies assign master data management roles to subject matter experts within their organization these experts are typically called master data stewards. Master Data Stewards are usually business users who understand the content and context of corporate data. They can be dedicated roles or assigned as part time resources with dotted line reporting into the DMO. In many cases, data stewards are responsible for only a portion of the attributes associated with data records the portion for which they have the most knowledge/authority and have been given open access to manage within the MDM system. Tasks for each data steward are routed appropriately within workflows to ensure efficient orchestration and collaboration in developing a holistic master data records. Riversand has experience assisting companies such as Dresser Rand, VF Corporation, and Cytec Industries with their data management strategies by conducting appropriate assessments and making recommendations for best practices in developing DMOs. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 20 of 22

9. Conclusions and Call to Action Do you want to know who you are? Don't ask. Act! Action will delineate and define you. Thomas Jefferson The advanced Data Governance capabilities within MDMCenter and the expertise of Riversand in supporting Data Governance initiatives offer organizations significant business benefits as they develop and deploy their MDM and Data Governance strategies. As we have seen, there are significant advantages and opportunities in developing MDM and Data Governance strategies in concert. In doing so, companies will be able to more quickly and accurately achieve a golden master record. This desired end state will in turn enable companies to better support business processes, ensure enterprise system consistency, reduce errors/costs, sell and market more effectively and operate more efficiently. If you would like to learn more about these concepts in order to ensure the success of your Data Governance initiatives, please contact your Riversand representative or contact Riversand directly through its website at www.riversand.com or via phone on 713 934 8899. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 21 of 22

COPYRIGHTS Copyright 2001-2013 Riversand Technologies, Inc. All rights reserved. Any technical documentation that is made available by Riversand Technologies, Inc is the copyright work of Riversand Technologies, Inc and is owned by Riversand Technologies, Inc. TRADEMARKS Riversand, the Riversand logo, and Riversand MDMCenter are U.S. trademarks or registered trademarks of Riversand Technologies, Inc. Other brands and product names mentioned in this guide are trademarks or registered trademarks of their respective owners and hereby acknowledged. COMPANY INFORMATION For more information on Riversand Technologies, Inc., visit www.riversand.com. Copyright 2001 2013 Riversand Technologies, Inc. All rights reserved. Page 22 of 22