White Paper: Extending Your Business Intelligence Investment with SQL Server Master Data Services. Prepared for: Intellinet August 29, 2011

Transcription

1 White Paper: Extending Your Business Intelligence Investment with SQL Server Master Data Services Prepared for: Intellinet August 29, 2011 Two Concourse Pkwy Atlanta, GA fax

2 Contents Contents INTRODUCTION TO MASTER DATA SERVICES... 2 CATEGORIES OF MASTER DATA MANAGEMENT... 3 ANALYTICAL MDM... 3 OPERATIONAL MDM... 3 ORGANIZATIONAL CHALLENGES... 4 KEY FEATURES & BUSINESS BENEFITS... 5 GETTING STARTED WITH MASTER DATA SERVICES... 7 PLANNING & PREPARATION... 7 TECHNICAL... 7 FUTURE TECHNICAL CONSIDERATIONS... 7 MASTER DATA SERVICES MODEL... 8 INTEGRATION INTO BUSINESS INTELLIGENCE ENVIRONMENT... 8 CONCLUSION... 9 APPENDIX: FURTHER READING Intellinet Page i

3 Introduction to Master Data Services Introduction to Master Data Services Master data consists of an organization s key reference and descriptive information for subject areas such as Customers, Products, Accounts, or Stores. Master data is typically used to describe, analyze and perform predictions on transactional data such as Sales. The subject areas (also known as domains) which are considered master data differ from company to company, as well as between industries. First introduced in the SQL Server 2008 R2 release, Master Data Services (MDS) is a software tool used to facilitate management of master data with the following features: Figure 1: Overview of SQL Server 2008 R2 Master Data Services An organization s master data, sometimes also referred to as reference data, is extremely valuable and is considered an organizational asset. If utilized to its fullest extent, it has the capability to offer rich analysis as well as operational efficiencies and compliance. Because master data often spans multiple source systems, originating from multiple siloed systems, having one authoritative source is beneficial. This is particularly true as an organization grows and matures Intellinet Page 2

4 Categories of Master Data Management Categories of Master Data Management Master data management (MDM) is often thought of in two distinct forms: analytical and operational. Analytical MDM Analytical Master Data Management is typically associated with reporting and analytics performed within a Business Intelligence (BI) environment. Supplementing the existing source system data with additional master data offers powerful reporting capabilities. A very common use case for Master Data Services (MDS) is between the source systems and a data warehouse. In this situation, Master Data Services serves as a central repository to consolidate, cleanse, and add additional contextual information before it is transmitted to the data warehouse. Typically most data is added to Master Data Services in an automated manner, using a tool such as SQL Server Integration Services (SSIS). Data which rarely changes may be managed within the MDS graphical interface rather than via automation. A Microsoft-based BI Environment, integrated with Master Data Services, is depicted below. Figure 2: Business Intelligence Environment with SQL Server 2008 R2 Master Data Services Operational MDM There are several different approaches to implementing Operational Master Data Management. The end goal for Operational MDM is to update the operational systems with master data, as opposed to Analytical BI which only extracts the data (i.e., one direction as depicted in Figure 2). Master Data Services does have the capability to integrate with BizTalk, to synchronize changes back to operational systems. Operational MDM is an extremely complex topic, and beyond the scope of this whitepaper Intellinet Page 3

5 Organizational Challenges Organizational Challenges Every organization that has data is already implementing some form of master data management whether they realize it or not. It may reside in one or more source systems, or even spreadsheets. When taking on a specific initiative to make the process of master data management more formalized, there are some challenges. Data Stewardship. Are knowledgeable data stewards assigned for each subject area? Are the stewards of the data willing to serve this role objectively and impartially, working with the owner of the data when necessary? Data Governance. Is a governance process for defining business rules in place? Is a data governance committee, responsive for providing oversight of the data stewardship process, achievable across departmental silos? Does the governance committee have the authority and influence to bring consensus when appropriate? Are there audit, regulatory, or other compliance requirements the data governance process should oversee? System of Record. How many different systems contain similar data? Which system is considered the system of record for each domain area? Agreement on Meaning of Data. Can consensus be reached on the meaning of terms? How are different versions of the truth reconciled to the satisfaction of all? Consistency and Completeness of Data. Are the same descriptions used across different systems? Are there varying formats of the same data throughout various systems? Do some systems capture less detail, or different data than other systems? Accuracy of Data. How are data integrity issues surfaced? How, where, and by whom, are incorrect data, missing data, or duplicate data issues resolved? Does the cleansing of data have any impact on audited data, or other compliance? Security. Who is permitted to consume data across divisions and departments? Does this vary by job role? Who is permitted to update data? Are some types of data required to be encrypted? Obscure Sources of Data. Are there departmental spreadsheets or databases (such as Microsoft Access) which contain extremely valuable data? Is the existing solution adequate to continue serving as the authoritative source? Automation vs. Manual Maintenance. There is a balance between how much automation can or should occur, versus requiring a data steward to explicitly control the definitions and mappings of data. Is data integration expertise available to establish automated processes? Management of Change. Is a different historical representation required for current descriptions, hierarchies, and mappings of data? Offering a now versus then representation is the most flexible, but adds a layer of complexity. Do data retention policies affect the availability of data? Executive and Stakeholder Support. As with any business process improvement, it is crucial to have executive vision and support for the master data management strategy. Is stakeholder support throughout functional areas of the organization available? 2013 Intellinet Page 4

6 Key Features & Business Benefits Key Features & Business Benefits This section lists key features of SQL Server Master Data Services (MDS). MDS Feature Business Value Examples Putting it into Action Attributes New information available for analysis that is not contained in source system New classifications & characteristics for data Demographics for the geographical area is associated to each new sales location. This facilitates predictive analytics on sales volume, dollars, and new customers. The marketing system contains data related to products which is not contained within the sales system. Bringing these together greatly enhances the context in which the information is presented. Hierarchies Alternate hierarchies Natural groupings and explicitly maintained tree structures Support for local accounting regulations as well as corporate reporting requirements. Reporting for various rollups, such as: internal, external (GAAP), tax. Different hierarchies may accommodate different versions of the truth when required. Business Rules and Validation Data Quality Notifications notifications are sent to data steward when a required value has not yet been entered, or an invalid value is present. Validation catches data quality issues early, before end users see them. Web-Based Interface Serves as a data entry system as well as system administration A data steward uses the web interface for changes to organizational data, which changes infrequently so a manual (nonautomated) approach is most appropriate. Import Data Automated process to load data from source system, into MDS, on a schedule The data load process detects new customers in the source system, and transfers them into MDS. This keeps integrated data timely and up to date. The data load process identifies new financial accounts used within various subsidiary financial accounting systems. Each new account is merged into a cohesive reporting structure at the consolidated level Intellinet Page 5

7 Key Features & Business Benefits MDS Feature Business Value Examples Putting it into Action Export Data Automated process to load data from MDS, into another system, on a schedule The data load process exports MDS data to the data warehouse. The additional attributes and hierarchical data greatly enhance the context in which the information is presented in the BI system. After it s cleansed and de-duplicated, updated data may be integrated (pushed) back to the source operational system. Corrections may be made as appropriate. Versions Preserve a "snapshot" of the data as of a certain date/time Last year s inventory master data is stored in a snapshot, marked as final. New input or changes are reflect in the newer version. The newer version may be compared to last year s snapshot as needed. Collections Integration with SharePoint Workflow Subset of data contained in Explicit Hierarchies Facilitates approval processes Enables more complex business rules A subset of customers is analyzed for a marketing promotion to take place next quarter. An MDS business rule is set up to validate an Organization change. This business rule initiates a SharePoint workflow which first requires the Data Steward to approve the item. Then, because this data affects audited reports, a secondary approval is required by a member of the Data Governance Committee Intellinet Page 6

8 Getting Started with Master Data Services Getting Started with Master Data Services Planning & Preparation Undertaking a master data management (MDM) initiative with a tool such as Master Data Services (MDS) is more about the process than the technology. Initial actions to consider taking, before even installing the software, include the following: Executive sponsorship Governance processes Governance and stewardship persons Envisioning and needs analysis Data profiling Data quality analysis Plan for data cleansing Timing of ongoing reviews Documentation requirements Naming conventions Integration into existing data warehouse environment Technical Requirements To get started with SQL Server Master Data Services, you need: Version: SQL Server 2008 R2 Edition: 64-bit Enterprise or Data Center (or Developer edition for individual developer machines) If SharePoint Workflow is to be used, additional requirements include: SharePoint 2010 (any edition) Visual Studio 2010 Professional The 2008 R2 version of Master Data Services has a separate installation process. Future Technical Considerations The current version of Master Data Services is delivered as part of SQL Server 2008 R2. The next version, SQL Server 2011 or 2012 (known as code name Denali as of this writing), will have the following major enhancements: Restructuring of import tables. The staging table structure, used for importing data, is significantly upgraded in the new version. Any data load packages (using SQL Server Integration Services or another data integration tool) created using the current version of MDS will need to be upgraded to the new structures when SQL Server is upgraded. With knowledge of this upcoming change, efforts can be made to minimize the impact. New Excel add-in and updated interface. A new Excel add-in will be available which provides an alternative to managing MDS models and data. The new interface will be Silverlight-based. These are exciting advancements coming to MDS. Awareness of this upcoming change may affect the training plan for data stewards and administrators. Integration with Data Quality Services. A new tool will be available to assure data quality remains high. This new tool will work in conjunction with the new Excel add-in Intellinet Page 7

9 Getting Started with Master Data Services Master Data Services Model An overview of the tasks involved to set up an MDS model includes: Create a model Establish entities and attributes (data elements), as well as their relationships to each other within the model Set up hierarchies (note: deciding if a hierarchy should be derived or explicit is a very important decision) Create a process for importing data and populating MDS model (ex: SSIS packages from a database or spreadsheet, and/or manual entry), including tasks such as data cleansing and de-duplication Establish a process for extracting data from the MDS model (ex: to Data Warehouse) Define business rules (these are likely to evolve over time, as needs arise) Specify security requirements Integration into Business Intelligence Environment Adding new master data into an existing data warehouse environment is not trivial. It involves enhancing the existing data model. Considerations include: Enhancements to existing dimension tables (such as Customer or Account) and/or creation of entirely new dimensions Determine which master data fields maintain historical records in the DW when changed (including relationship of MDS versions to a Type 2 dimension) Association of master data to numeric fact data (such as Sales or Inventory) Determine the level of detail to be stored within the data warehouse Identify corresponding enhancements to Analysis Services cube Locate existing reports which may be enhanced with new data 2013 Intellinet Page 8

10 Conclusion Conclusion Although Master Data Services is still maturing as a product, the current version has many features and benefits. An organization with an existing Microsoft BI investment may be able to yield big rewards relatively quickly by incorporating MDS into its BI environment. When getting started, strongly consider starting with a small project with one subject area. Customer data is generally thought of to be one of the more complex areas, as is financial data. Select a subject area with meaningful business value, yet a finite scope. A phased approach, with brief timelines, will increase the chance of success early in your master data management effort. Maintain focus on the business value, processes, and strategy, rather than the technological aspects. Pay extremely close attention to data quality: it is one of highest risk facts in an MDM initiative. Best of luck in your master data management efforts Intellinet Page 9

11 Appendix: Further Reading Appendix: Further Reading Following are links to additional information on the product Master Data Services, as well as master data management in general. Microsoft Product Information Microsoft SQL Server Solutions & Technologies: Master Data Services Articles by Tyler Graham of the Master Data Services Team at Microsoft Master Data Management from a Business Perspective Master Data Management from a Technical Perspective Implementing a Phased Approach to Master Data Management Bringing Master Data Management to the Stakeholders MSDN Article by Roger Wolter and Kirk Haselden The What, Why, and How of Master Data Management CIO Magazine Article Demystifying Master Data Management 2013 Intellinet Page 10