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Analytical Master Data

The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Michael Olschimke – 1st expert on this subject based on the ideXlab platform

  • Chapter 9 – Master Data Management
    Building a Scalable Data Warehouse with Data Vault 2.0, 2015
    Co-Authors: Dan Linstedt, Michael Olschimke

    Abstract:

    Master Data Management (MDM) enables organizations to create and use a ” single version of the truth ” [1]. This is especially helpful when using conformed dimensions, as discussed in Chapter 7, Dimensional Modeling, which require a defined set of attribute values. In some organizations, the source for such conformed dimensions comes from a leading source system, but in many other cases, the Data comes from multiple systems. It is not only required to combine the Data from those multiple systems. Often, the Data contradicts and overlaps. Therefore, the Data has to be conformed in some way [2]. Conformity is not required by dimensional models. They represent only one modeling method that allows Data to be aligned and produced as information for business. In some cases, the Master Data is delivered in a cube, while in other cases it may be delivered in flat and wide denormalized table struc-tures (otherwise known as first normal form). The notion of conformity is truly what Master Data cares about. This chapter discusses how to use Master Data and MDM as an enabler for business users to take control of Data warehousing, getting them closer to true managed self-service BI (a concept discussed in Chapter 2). It also shows how to set up a Database in Microsoft Master Data Services (MDS), an MDM solution included in Microsoft SQL Server 2014. Finally, it will show how to integrate Master Data from MDS with the Data Vault. Because MDS as a feature is often not very well known to users of Microsoft SQL Server, we have dedicated a lot of space to MDS in this book. 9.1 DEFINITIONS The next sections provide introductory definitions of the core components of MDM to better distin-guish between the terms used throughout this chapter and the rest of the book. 9.1.1 Master Data The definition of Master Data is the business’s answer to the chaos of reference Data in source systems. Master Data describes the business entities, which are part of the business processes implemented in operational systems of the organization. While Master Data, at first glance, look very similar to Data warehouse dimensions, they should be much closer to operational systems. However, Master Data be-come a source for Data warehouse dimensions and can provide a great source of such Data if the Master Data have been implemented well [2]. Master Data is commonly distinguished by operational Master Data and Analytical Master Data.

  • Master Data Management
    Data Vault 2.0, 2015
    Co-Authors: Dan Linstedt, Michael Olschimke

    Abstract:

    An important aspect of Data warehousing projects is the definition of Master Data. This chapter introduces basic architectures for Master Data management and shows how to deal with Master Data using Microsoft Master Data Services (MDS), a product included in Microsoft SQL Server. The chapter provides an overview of the goals in Master Data management in the context of Data warehousing, its drivers and compares operational and Analytical Master Data. It also explains how to use MDM as an enabler for managed self-service business intelligence and total quality management. The authors demonstrate the definition of entities with their accompanying attributes within MDS models, the definition of business rules to ensure Data quality. and how to stage Master Data from operational systems and then load the Data into the Data warehouse.

Dan Linstedt – 2nd expert on this subject based on the ideXlab platform

  • Chapter 9 – Master Data Management
    Building a Scalable Data Warehouse with Data Vault 2.0, 2015
    Co-Authors: Dan Linstedt, Michael Olschimke

    Abstract:

    Master Data Management (MDM) enables organizations to create and use a ” single version of the truth ” [1]. This is especially helpful when using conformed dimensions, as discussed in Chapter 7, Dimensional Modeling, which require a defined set of attribute values. In some organizations, the source for such conformed dimensions comes from a leading source system, but in many other cases, the Data comes from multiple systems. It is not only required to combine the Data from those multiple systems. Often, the Data contradicts and overlaps. Therefore, the Data has to be conformed in some way [2]. Conformity is not required by dimensional models. They represent only one modeling method that allows Data to be aligned and produced as information for business. In some cases, the Master Data is delivered in a cube, while in other cases it may be delivered in flat and wide denormalized table struc-tures (otherwise known as first normal form). The notion of conformity is truly what Master Data cares about. This chapter discusses how to use Master Data and MDM as an enabler for business users to take control of Data warehousing, getting them closer to true managed self-service BI (a concept discussed in Chapter 2). It also shows how to set up a Database in Microsoft Master Data Services (MDS), an MDM solution included in Microsoft SQL Server 2014. Finally, it will show how to integrate Master Data from MDS with the Data Vault. Because MDS as a feature is often not very well known to users of Microsoft SQL Server, we have dedicated a lot of space to MDS in this book. 9.1 DEFINITIONS The next sections provide introductory definitions of the core components of MDM to better distin-guish between the terms used throughout this chapter and the rest of the book. 9.1.1 Master Data The definition of Master Data is the business’s answer to the chaos of reference Data in source systems. Master Data describes the business entities, which are part of the business processes implemented in operational systems of the organization. While Master Data, at first glance, look very similar to Data warehouse dimensions, they should be much closer to operational systems. However, Master Data be-come a source for Data warehouse dimensions and can provide a great source of such Data if the Master Data have been implemented well [2]. Master Data is commonly distinguished by operational Master Data and Analytical Master Data.

  • Master Data Management
    Data Vault 2.0, 2015
    Co-Authors: Dan Linstedt, Michael Olschimke

    Abstract:

    An important aspect of Data warehousing projects is the definition of Master Data. This chapter introduces basic architectures for Master Data management and shows how to deal with Master Data using Microsoft Master Data Services (MDS), a product included in Microsoft SQL Server. The chapter provides an overview of the goals in Master Data management in the context of Data warehousing, its drivers and compares operational and Analytical Master Data. It also explains how to use MDM as an enabler for managed self-service business intelligence and total quality management. The authors demonstrate the definition of entities with their accompanying attributes within MDS models, the definition of business rules to ensure Data quality. and how to stage Master Data from operational systems and then load the Data into the Data warehouse.