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  • 1. Enterprise Integration Management withMicrosoft SQL Server 2012Delivering Credible Consistent Data to Every OrganizationWhite PaperAuthor: Graeme Malcolm (CM Group)Published: May 14, 2012Summary: Enterprise Integration Management (EIM) is a growing priority fororganizations that want to gain a competitive advantage by basing keybusiness decisions on credible, consistent, data. Some of the challengesinvolved in implementing an effective EIM solution include:  Integrating data from an increasing number of diverse sources and in a growing number of formats into a common platform for decision making  Empowering information workers who understand the business to manage data governance, while ensuring IT maintain controlSQL Server 2012 provides a comprehensive platform for EIM, which makes itpossible to:  Integrate any data from applications and systems across the enterprise  Make trusted decisions based on cleansed and standardized data  Empower business users to manage data governance and easily gain insights from the dataFor the latest information, see
  • 2. ContentsIntroduction ....................................................................................................... 1The SQL Server 2012 EIM Platform .................................................................. 2 SQL Server Integration Services ................................................................... 3 SQL Server Data Quality Services................................................................. 6 SQL Server Master Data Services ............................................................... 11Integrating Data across the Enterprise and Beyond ........................................ 15 SSIS Connection Managers and Data Sources ........................................... 16 SSIS Extensibility......................................................................................... 17Empowering Business Users .......................................................................... 18 Empowering Users to Manage Data Quality ................................................ 19 Empowering Business Users to Manage Master Data................................. 20Make Trusted Decisions on Credible, Consistent Data ................................... 23Conclusion ...................................................................................................... 25The information contained in this document represents the current view of Microsoft Corporation on the issues discussed as of the date ofpublication. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part ofMicrosoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of publication.This white paper is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THISDOCUMENT.Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this documentmay be reproduced, stored in, or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical,photocopying, recording, or otherwise), or for any purpose, without the express written permission of Microsoft Corporation.Microsoft may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in thisdocument. Except as expressly provided in any written license agreement from Microsoft, the furnishing of this document does not give you anylicense to these patents, trademarks, copyrights, or other intellectual property.© 2012 Microsoft Corporation. All rights reserved.Microsoft, SharePoint, SQL Server, Visual Basic, Visual C#, Visual Studio, Windows, Windows Server, and the Server Identity Logo aretrademarks of the Microsoft group of companies.All other trademarks are property of their respective owners.
  • 3. IntroductionAn increasingly competitive and difficult business environment means thatorganizations need to get any competitive advantage they can in terms ofmaking smart business decisions. Most organizations recognize the value ofbasing decisions on credible, consistent data data, and at a time whenbusinesses, their customers, and third-party services on the Web aregenerating increasing volumes of information, there’s no shortage of data toinform those decisions. The problem is that data is usually created and storedin isolated application silos with varied levels of consistency and accuracy; andthe challenges of integrating and standardizing the data can preventcompanies from getting the comprehensive “single view of the truth” needed todrive effective decision making. “Everywhere you look, the quantity of information in the world is soaring. According to one estimate, mankind created 150 exabytes (billion gigabytes) of data in 2005. This year, it will create 1,200 exabytes. Merely keeping up with this flood, and storing the bits that might be useful, is difficult enough. Analysing it, to spot patterns and extract useful information, is harder still. Even so, the data deluge is already starting to transform business, government, science and everyday life.” The Economist: “The Data Deluge” (Feb 2010 )Many organizations are looking to enterprise integration management (EIM) asa way to integrate, consolidate, and cleanse data for decision making. A goodEIM solution can integrate day to day business operations and support datawarehousing and business intelligence (BI) to help organizations learn fromtheir data and become more effective. As early as 2010, Garner observed: “In 2010, exploiting business data and information is a nearly universal priority for organizations. During the next decade, mastering EIM is considered a critical success factor by CEOs, CFOs, CIOs and other business executives. These executive leaders know their organizations generate an enormous amount of high-value data and information; however, most also believe that much of the value is untapped and underutilized in managing their organizations” Gartner: “Business Leaders Must Learn Why, When and How to Exploit Enterprise Information Management” (October 2010).The reason for this trend is clear. Executives in many organizations believethat by bringing together as much information as possible into a trusted sourceof data for decision making, they can improve the financial performance of theirorganizations. In 2011, Gartner included the following strategic planningassumption in its research on data management and integration: “Through 2015, enterprises integrating high-value and diverse new information types and sources into a coherent information management infrastructure, will financially outperform their industry peers by more than 20%.” Gartner: “Key Issues for Data Management and Integration, 2011” (Feb 2011) 1
  • 4. Microsoft SQL Server 2012 builds on the data integration and managementfeatures of previous releases to provide a comprehensive platform for EIM,and support data warehousing and business intelligence solutions thatempower businesses to make critical business decisions on trusted data.Moreover, Microsoft’s data platform is designed to enable organizations tocapitalize on the wealth of business knowledge held by information workers –enabling business users to take on the role of data stewards, and manage dataquality and consistency with minimal support from IT specialists.The SQL Server 2012 EIM PlatformSQL Server 2012 provides all the components needed for an effective EIMsolution in a single product. Key components of SQL Server 2012 that help youbuild an EIM solution are:  SQL Server Integration Services  SQL Server Data Quality Services  SQL Server Master Data ServicesThese technologies work together to create an EIM solution that supports otherSQL Server technologies for data warehousing and BI, and which ensures thatthe entire business decision making ecosystem begins and ends with thebusiness user. Figure 1 shows how SQL Server and other Microsofttechnologies work together to provide a user-centric approach to businessdecision making. Figure 1: A user-centric approach to business decision making 2
  • 5. SQL Server Integration ServicesSQL Server Integration Services (SSIS) is a platform for data integration thatprovides a comprehensive and extensible solution for extract, transform, andload (ETL) operations. In an EIM context, SSIS provides a workflow and dataflow engine that you can use to integrate data from virtually any data sourceinto an ecosystem for business decision making. You can use IntegrationServices to automate tasks such as copying or downloading files, sending e-mail messages in response to events, updating data warehouses, cleaning andmining data, and managing SQL Server objects and data. Unlock andintegrate the data from any industry standard third party source like SQLServer, Oracle, Teradata, DB2, SAP, CRM, SharePoint, real time, cloud-basedapplications, and more.SSIS consists of a workflow engine that you can use to automate control flowtasks and data flows. Data flows consist of a sequence of data sources,transformations, and destinations arranged as a pipeline through which data ispassed between buffers. The buffer-based nature of the data flow pipelineenables ETL developers to maximize data throughput and optimize the overallperformance of the data flow.ETL developers can use SQL Server Data Tools, a graphical developmentinterface built on the Visual Studio environment, to create SSIS packages.Each package encapsulates a control flow, which may in turn contain multipledata flows. SQL Server Data Tools provides a simple to use, highly productivedevelopment environment that makes it possible for developers to quicklycreate and deploy complex ETL solutions. 3
  • 6. Figure 2: Creating an SSIS Package in SQL Server Data ToolsSQL Server 2012 introduces a new project-level deployment model for SSISpackages, enabling organizations to deploy and manage multiple related SSISpackages as a single unit. You can define multiple execution environments,with associated configuration settings in the form of variables that can bemapped to project-level parameters defined in the SSIS project. Projects aredeployed to an SSIS catalog on a SQL Server instance, and can be managedwith SQL Server Management Studio. You can also schedule execution ofindividual SSIS packages by creating SQL Server Agent jobs, enabling you tocreate fully automated ETL solutions that power your EIM data integrationprocesses. 4
  • 7. Figure 3: SSIS Project Deployment and ManagementWhen you have deployed a project in an SSIS catalog, you can monitor detailsof package execution easily though built-in reporting and status tracking, asshown in figure 4. This enables you to verify or troubleshoot packageexecution and monitor performance over time. 5
  • 8. Figure 4: Monitoring SSIS Package ExecutionSQL Server Data Quality ServicesThe ability to integrate data from multiple data sources into a data warehouseto support business decision making is clearly of great benefit to organizationsseeking a competitive advantage. However, decisions must be based on datathat is trusted to be accurate, consistent, and complete.Microsoft® SQL Server 2012 Data Quality Services (DQS) is a new offering aspart of SQL Server 2012 allowing customers to cleanse, match, standardize,and enrich their data to deliver trusted information for business intelligence,data warehouse, and transaction processing workloads. End users can evencleanse their personal files in unmanaged documents. SQL Server DataQuality Services (DQS) provide an approachable data quality solution fororganizations of all sizes to help improve the quality of their data.SQL Server Data Quality Service (DQS) provides a knowledge-basedapproach to managing data quality. Organizations can leverage the businessknowledge of their users to create knowledge bases that define known valuesand validation rules for the data domains used in data records for businessentities. For example, you might create a knowledge base for customer datathat defines the data domains, or fields, that are commonly used in customer 6
  • 9. records (such as Customer ID, First Name, Last Name, Gender, Email, StreetAddress, City, State, Country, etc.). You can then perform knowledgediscovery against existing data to identify known values for these fields (suchas “California” and “Washington” for the State field), and define rules tovalidate any new domain values as they are discovered (such as a rule toensure that all Email values contain a “@” character, or that all Gender valuesbegin with “M” or “F”).DQS provides a client application for managing knowledge bases, as shown infigure 5. Figure 5: Data Quality Services Client ApplicationAs well as defining validation rules for domains in a knowledge base, you canidentify synonyms and common data entry errors for domain values, andspecify a leading value to which all instances of these values should becorrected. For example, your knowledge discovery might reveal that recordsfor customers who live in California most commonly have a State value of“California”; but often an application user will enter alternative values with thesame meaning, such as “CA”, “Calif.”, or they will commonly mistype the valueand accidentally enter “Californa”. Customer records with variants of the samestate value might have minimal impact in the line of business application inwhich they are entered, but if the data in that application is to be used foranalysis or reporting that aggregates vales by state, the presence of multiple 7
  • 10. values for the same state can result in some misleading information on whichto base business decisions.To avoid this problem, you can identify these as known values in the DQSknowledge base, and specify that they are synonyms that should always becorrected to a leading value of “California”. Then, when you use DQS toperform data cleansing, the resulting cleansed data will include consistentvalues for the state domain. Figure 6 shows a DQS knowledge base in which aCountry/Region domain includes the leading value “United Kingdom”, andseveral synonyms for this value that should be corrected. Figure 6: Correcting domain valuesWhile a DQS knowledge base is often primarily based on your ownorganization’s institutional knowledge about business-specific data, there aresome cases where it can be useful to incorporate external knowledge forcommon types of data, such as postal address or telephone number validation.The Microsoft Windows Azure Marketplace includes several commercialdatasets that are specifically designed for data cleansing and validation and forwhich you can purchase a subscription. When you have subscribed to one ofthese datasets, you can use it as reference data for a domain in a DQSknowledge base and supplement your own business-specific data validationand value correction rules. For example, figure 7 shows how external data,purchased in the Windows Azure Marketplace, can be used to validate and 8
  • 11. correct company names in a Company domain by referencing acomprehensive dataset of US registered companies. Figure 7: Using external reference data in a DQS knowledge baseYou can perform data cleansing interactively with the DQS client application byspecifying a data source such as an Excel spreadsheet or a table in a SQLServer database, and mapping the fields in the data source to domains in theknowledge base. Additionally, you can incorporate data cleansing into ETLprocesses by using the Data Cleansing transformation in an SSIS data flow, asshown in figure 8. 9
  • 12. Figure 8: Incorporating DQS data cleaning into an SSIS data flowAs well as using DQS for data cleansing, you can create matching policies andperform data matching to identify and consolidate duplicate records for thesame business entity. For example, it’s possible that a customer has registeredon your organization’s e-commerce Web site as “Jenny Russell”, but alsomade a purchase in a physical store where the name has been recorded as“Jennifer Russell”. The organization now has multiple customer records for thesame customer, which will affect the accuracy of any reporting or analysis thataggregates data by customer.With DQS, you can create a matching policy that compares multiple domainsacross records, assigning a weighted value for fields that are exact orapproximate matches. So your matching policy might compare customerrecords on FirstName, LastName, Address, Email, and DateOfBirth domains.When multiple records have enough matching domains to satisfy the matchingpolicy, DQS identifies the records as possible duplicates. For example, if adataset includes a record for Jenny Russell and a record for Jennifer Russell,but the address, email, and date of birth values for the two records are thesame, you can reasonably assume that these records might relate to the samecustomer. 10
  • 13. Figure 9: A Matching PolicyThe data cleansing and data matching functionality in DQS can helporganizations manage the quality and integrity of their data, and help ensurethat decisions are based on trusted information.SQL Server Master Data ServicesMaster Data Services is the SQL Server solution for master data management,focused on creation, maintenance and storage of master data structures usedfor object mapping, reference data, metadata management, and dimensionsand hierarchies for data integration operations. This includes businessintelligence and data warehousing, and integration between operationalsystems. With the Master Data Services Add-in for SQL 2012, business userscan directly manage existing database or data warehouse dimensions andhierarchies from within Excel without IT intervention. IT is still given oversightto track and reverse changes made by the business.With DQS, an organization can apply knowledge about individual data fieldvalues to cleanse datasets and identify duplicate records. However, largeenterprises often need to maintain data representations of core businessentities in multiple applications and systems across the business. For example,a company might store employee data in an HR management system and alsoin a payroll application; or it might store product data in a stock managementsystem and in an e-commerce product catalog. 11
  • 14. When the same business entities are represented in multiple systems, it canbe useful to maintain a definitive, master record for each entity to ensure thatany data relating to a specific entity is consistent across the enterprise. Youmay approach this challenge by designating one of your application datastores as the master system of record for a given type of business entity (forexample, you could use the HR management system as the definitive sourceof information for employees), or you could create a separate master data hubthat ensures consistency across all systems. The discipline of maintaining acentral data definition for business entities is commonly called master datamanagement (MDM), and SQL Server Master Data Services (MDS) provides aSQL Server-based solution that you can use to implement MDM for any kind ofbusiness entity. Figure 10: Managing data models with Master Data ServicesAs figure 10 shows, MDS enables you to create master data models for yourcore business entities. These models contain entity definitions, which in turndefine the data attributes for each entity. You can also organize your entitiesinto hierarchical relationships, so for example a product might belong to asubcategory, which in turn belongs to a category.After you have created a master data model, you can manage the data entitiesin the model to define their attributes (which you can categorize into multiple 12
  • 15. attribute groups for specific applications or user scenarios). Figure 11 showsthe attributes defined for a Product entity. Figure 11: An entity and its attributesWhen you have defined the entities and attributes in your master data model,MDS provides staging tables that you can use to load data into the model.Additionally, you can create subscription views for the entities and hierarchiesyou have defined so that applications can retrieve master data from the modelby simply submitting regular Transact-SQL queries. This database-orientedarchitecture for transferring data into and out of the master data model makesit easy to build a master data hub, in which new data is loaded into the MDSmodel to be brought under the governance of master data management, andapplications can consume master data to ensure enterprise-wide consistency.In many cases, SSIS is used as the “engine” to manage the flow of data intoand out of the master data hub as shown in figure 12. 13
  • 16. Figure 12: Using SSIS to insert and extract master dataWhen your master data model has been populated with data, you can viewand manage the data instances of the entities it defines, and create customhierarchies and collections of entities for specific business scenarios. Forexample, you could create an explicit hierarchy of products that are soldthrough a specific retail partner channel, as shown in figure 13. Figure 13: An explicit hierarchy 14
  • 17. You can also use MDS to validate the data in your master data model byapplying custom business rules. For example, you could define a rule verifiesthat all product prices are greater than zero as shown in figure 14. Figure 14: Defining a business ruleMDS includes many more features that enable you to implement complexMDM solutions and ensure that consistent data representations of keybusiness entities are used across the enterprise. The combination of this abilityto manage master data with MDS, the data cleansing and matchingfunctionality of DQS, and the data integration capabilities of SSIS, creates acomprehensive platform for EIM.Integrating Data across the Enterprise and BeyondOne of the key aims of an EIM solution is to consolidate the information frommultiple, disparate sources and provide users with a “single version of thetruth” on which to base their decisions. One of the main challenges toachieving this consolidation in many organizations is that the required data islocked in discrete application silos, or needs to be obtained from externalsources. 15
  • 18. SSIS Connection Managers and Data SourcesEarlier in this paper, you learned how SSIS provides a platform for creatingETL solutions that integrate data from multiple sources. One of the keybenefits of SSIS is the broad range of data connectivity it supports, fromrelational database systems to XML and flat files or Excel workbooks. Theprimary way in which SSIS connects to data sources is through an extensiblearchitecture of connection managers, a significant number of which areprovided “out of the box” in SSIS. Figure 15: SSIS Connection ManagersFigure 15 shows a range of connection managers, including ODBC andOLEDB connection managers that can be used to connect to a wide range ofcommon data sources, including SQL Server, Oracle, DB2, MySQL, and otherdatabase systems. You can even connect to and consume data from cloud-based databases in SQL Azure. Additionally, connection managers areavailable for enterprise applications such as SAP and Teradata.SSIS also includes a large number of connection managers for commonlyused data file formats, such as Excel, XML, or comma-delimited text files. Youcan combine these with control flow tasks to manage file system resources, 16
  • 19. FTP connections, and Web services to create complex workflows that processand consume data files.SSIS data flows can include distributed transactions for data sources thatsupport them, so you can use them to create reliable ETL processes thatproduce consistent data. You can also use the checkpoint capability of SSIS torestart failed data flows without repeating workflow tasks that have alreadycompleted successfully.If your data resides in SQL Server or Oracle databases, new features in SQLServer 2012 make it easier than ever to identify and extract modified datathrough enhanced support for Change Data Capture (CDC). These featuresmake it easy to detect data that has changed since the previous dataextraction cycle, and restrict data retrieval to include only the modified rows.This significantly improves the performance of your ETL workflows whileensuring that the information your organization uses to make businessdecisions reflects the latest version of the data.SSIS ExtensibilityIf your data resides in a source for which no connection manager is provided inSSIS, you can take advantage of the extensibility of SSIS and either procure orcreate a custom connection manager to suit your needs. SSIS components arebased on Microsoft .NET base classes, and developers can easily createcomponents that inherit from these classes to implement custom connectionmanagers, data sources and destinations, transformations, and control flowtasks.For example, many organizations store business data in SharePoint lists, andneed to consume this data in ETL processes. A custom component toconsume SharePoint list data is available from the CodePlex Web site(, and you can install and use this to integrate SharePointdata into your EIM solution1.You can also create your own custom data sources, transformations, anddestinations by using the Script workflow component, which is provided asstandard in SSIS. The Script component enables you to implement customfunctionality by creating a Visual Studio Tools for Applications (VSTA) script ina supported language such as C# or Visual Basic, as shown in figure 16.1 For more information about integrating SharePoint data into an SSIS data flow, see 17
  • 20. Figure 16: Implementing a custom script in an SSIS data flowThe ability to create custom component and scripts makes it possible to builddata integration solutions for virtually any data. For example, you couldintegrate event data from a data stream generated by plant machinery orsensors by creating a custom SSIS solution that consumes data from SQLServer StreamInsight2.Empowering Business UsersOne of the key differentiators of the Microsoft solution for EIM when comparedto competitors is the notion that business data belongs to the business, not toIT. The IT department is great at managing application and data infrastructure,but knowledge of what that data actually means and how it should be cleansedand made consistent is best understood by the information workers who use itin their day-to-day roles. SQL Server 2012 gives IT specialists the tools theyneed to build a comprehensive data integration solution and manage datagovernance and compliance across data infrastructure, but also gives businessusers intuitive tools that they can use to manage the quality and integrity oftheir own data.2 For more information about using StreamInsight and SSIS, see 18
  • 21. Empowering Users to Manage Data QualityThe DQS client application provides an intuitive wizard-based tool with whichbusiness users can create and manage knowledge bases, and perform dataquality tasks such as data cleansing or matching, as shown in figure 17. Thisability to manage data quality with minimal technology or database expertisemakes it possible for business users to take on the role of “data steward”, andmanage the integrity of the data used by the business. Figure 17: A wizard-based approach to data quality managementAfter performing a data cleansing or matching operation with the DQS clientapplication, user can export the results as a Microsoft Excel workbook asshown in figure 18. This enables them to use a familiar tool to examine andverify the suggestions that DQS has generated before applying them toproduction data. 19
  • 22. Figure 18: Data cleansing results in Microsoft ExcelEmpowering Business Users to Manage Master DataExcel is also the primary tool with which business users can manage masterdata. With SQL Server 2012 Master Data Services, information workers canuse the MDS Add-In for Microsoft Excel to create and existing database ordata warehouse dimensions and hierarchies from within Excel as shown infigure 19. Excel provides a familiar and intuitive environment for managingmaster data, and business users can build and publish master data modelsquickly and efficiently, without specialist support from IT or externalconsultants. 20
  • 23. Figure 19: Managing a master data model with ExcelWhen the master data model is built, Excel continues to provide a user-friendlyenvironment for adding and editing entity records to the model by usingstandard Excel functionality to type individual attribute values or copy andpaste entire ranges of cells that represent multiple entity instances.Users can also save and share queries against the master data model, andeven validate data against the business rules defined in MDS as shown infigure 20. 21
  • 24. Figure 20: Validating master data against business rules in ExcelCase Study – ArevaAreva is a French nuclear power company that provides governments and utilities withsolutions for low-carbon power generation. A critical part of the company’s business isproviding its customers with up-to-date, accurate data. This was a challenge because Arevahad no centralized management of its master data. Instead, the company stored and managedcustomer data at each individual subsidiary site, where multiple databases were used. As aresult, data was often redundant and, in some cases, inaccurate. To validate the data andmake it useful for customers, Areva employees had to manually make changes, a costly andtime-consuming process that often meant customers did not receive data on time.In October 2011, Areva began to implement a new centralized master data hub based onMicrosoft SQL Server 2012 Enterprise data management software, focusing on SQL Server2012 Master Data Services and Data Quality Services. The new solution consolidates dozensof databases containing legal information, internal organizational data, and specific customerdata. It uses SQL Server 2012 Integration Services to clean and transform data.Areva takes advantage of SQL Server 2012 Master Data Services to enforce the company’sprocesses, validations, and rules to provide the most correct data. This eliminates theprocesses it previously had in place to keep the company’s data current. With SQL Server2012 Data Quality Services the company further improves data quality by profiling, cleansing,and matching its most critical data. 22
  • 25. In addition, Areva is using the SQL Server 2012 Master Data Services Add-In for Excel, a newtool that gives users the ability to gather data and publish it to the database from withinMicrosoft Excel 2010 spreadsheet software, which the company had already been using.Areva used these new technologies to build an identity access management application thatstores centralized, updated personal employee data from the IT, Financial, and HumanResources departments of all 330 of the company’s subsidiaries. The application includes a 2-gigabyte database with 48,000 rows of data in its largest table.The new solution improves data quality and timeliness. It also helps Areva make betterbusiness decisions. What’s more, because the solution is based on familiar tools, it doesn’trequire special training. Finally, Areva saves time and money by eliminating manual datacleansing.Make Trusted Decisions on Credible, Consistent DataThe overall aim of any EIM solution is to enable business users to rely on thedata they use to make critical business decisions. With SQL Server 2012,business users can use DQS to define and manage the knowledge bases onwhich data cleansing and matching rely; and they can manage the consistencyof business entity data through Master Data Services. The result of this user-centric approach is a solution that maximizes the value of business data,quickly and cost-effectively.To complete the solution, users must be able to easily consume thestandardized data they have created, and use it to make effective businessdecisions. To be of any practical use, a user-centric EIM solution must supportuser-centric BI.SQL Server 2012 leads the way in self-service BI, delivering insights fromtrusted data directly to users. SQL Server PowerPivot, shown in figure 21,provides a massively scalable, but easy to use Excel-based data analysis toolwith which business users can slice and dice data, and easily share theiranalysis through SharePoint. 23
  • 26. Figure 21: Analyzing data with PowerPivotSQL Server 2012 also introduces Power View, a user-centric tool forinteractively visualizing data in an intuitive and easy-to use interface, as shownin figure 22.The ability for business users to take on the role of data steward with DQS andMDS, and to directly analyze and visualize data with self-service BI tools likePowerPivot and Power View enables them to take an active role in thecomplete EIM lifecycle. This user-centric approach empowers organizations touse their IT resources to manage data infrastructure and integration processeseffectively, while reducing the burden on IT to manage business data andanalytics – helping reduce the overall cost of implementing EIM and facilitatinga dynamic approach to business decision making that promotes businessresponsiveness and flexibility. 24
  • 27. Figure 22: Interactive data visualization with Power ViewConclusionA good Enterprise Information Management (EIM) solution should start andend with the business users who drive the success of the company. SQLServer 2012 empowers business users to manage the quality, integrity, andstandardization of the data they use every day allowing them to trust that theyare making decisions on credible, consistent data. In this model, IT still retainsoversight of the organization’s data infrastructure. With SQL Server 2012Integration Services, Master Data Services, and Data Quality Services, youcan easily bring together data from all across your enterprise, and use the dataquality and governance rules defined by the business to create a reliable,trusted source of data for business decision making.For more information:Microsoft SQL Server Product Site Server Developer Center Server TechCenter 25
  • 28. CopyrightThis document is provided “as-is”. Information and views expressed in thisdocument, including URL and other Internet Web site references, may changewithout notice. You bear the risk of using it.This document does not provide you with any legal rights to any intellectualproperty in any Microsoft product. You may copy and use this document foryour internal, reference purposes.© 2012 Microsoft. All rights reserved. 26