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Making SAP Information Steward a Key Part of Your Data Governance
Strategy
Part 3 – SAP Information Steward Metadata Management and Metapedia
Part 1 in our series on Data Governance defined the concept of Data Governance and gave suggestions on how to go about implementing an initial program at a corporate level. Part 2 provided an overview of how SAP Information Steward can help you get started with a Data Governance program and detailed the Data Insight module of the tool. In Part 3, we will now turn towards the Metadata Management and Metapedia modules of Information Steward to show how they can help in other areas of Data Governance.
Common Concerns
The following questions and comments coming from within an organization are ones that we hear often:
How were the values on this report calculated?
Where is this data being sourced from?
I can’t trust this report; some values look right but others seem way off base
What definition of Customer do you mean here? We define it differently
We view this set of materials as Finished Goods, but some other plants view them as semi-finished. We sell these but that other plant is responsible for putting these materials into a larger assembly
Mostly these conversations boil down to two main problems: 1. Business users are completely blind to how the data they see in a report has been processed.
They don’t know where it came from or how it was calculated, and therefore they don’t know if it can be trusted
2. Common terms are being lost in translation across the enterprise. One group defines a term one way, and the rest of the company defines it another. As a result, communication has become challenging as conversations devolve into how to properly define certain terms, rather than solve the actual business problem that has come up.
Metadata Management
Metadata Management is the answer to the first problem. It is the Impact & Lineage Analysis tool available within SAP Information Steward that allows users the capability to trace the lineage of a piece of data from report output back to source systems. Alternatively, one can also check the impact of any data source on all downstream reports. In addition, it will identify all data objects in between those two endpoints and determine if any transformations or calculations have occurred, giving your organization a powerful lens into all of your data movements and manipulations. Metadata Management works by effectively “crawling” through your different source systems to gather metadata objects from within them. The list of systems that can be connected is quite extensive and includes SAP BusinessObjects Enterprise, SAP HANA, SAP ECC, SAP BW, SAP Data Services, most common relational database management systems, and even the modeling tool SAP PowerDesigner. Its capabilities can be taken even further if a license is obtained for the Meta Integration Model Bridge
(MIMB) tool, which allows for metadata integration of dozens of 3rd party tools. A full list of the tools supported by MIMB can be found here. If all else fails, it is even possible to manually import metadata contained in an Excel spreadsheet. Once connections have been created and metadata integrator tasks have been run on those connections, SAP Information Steward displays the systems in the Metadata Management module within the web application.
Figure 1 Metadata Management Directory Home Page. Notice the different system types
Notice that Information Steward breaks down the connections by type, including Business Intelligence, Data Integration, and Data Insight Connections. Business Intelligence contains systems such as SAP BusinessObjects, SAP HANA, SAP ECC, and SAP BW. Data Integration includes any SAP Data Services repositories that have been connected. And finally, the Data Insight Connections include any connection that has been used in the Enterprise Project of Data Insight. These are automatically added to Metadata Management, allowing for even further analysis of those data sources.
Lineage & Impact
The real power of the Metadata Management module is its capability to perform impact and lineage analysis. What this means is that as metadata integrators crawl through your different source systems and gathers the metadata objects within, Information Steward automatically determines how those objects relate to others within that system and also to objects in other source systems. This is incredibly powerful because the tool can then build visualizations to show relationships between metadata objects and how data moves through your different systems. For example, the screenshot
below shows how an SAP BusinessObjects report is sourced from columns in an SAP HANA analytical view.
Figure 2 The Online Sales Report is sourced from many columns in the Internet Sales universe which ultimately sits on top of an analytical view in a HANA database
Alternatively, you can also view the impact that a data object has on other objects and other systems. In the screenshot below, the SALE_HEADER table is used as a source for other tables in my SAP Data Services ETL environment, and this has a further downstream effect on the FACT_INTERNET_SALES table in SAP HANA and the analytical views that are built on top of it.
Figure 3 Impact relationships of the SALE_HEADER table as it moves through my Data Services ETL and into my HANA environment
You can also change settings to tighten or loosen the logic that SAP Information Steward uses to determine if two objects are related to each other. If changing the settings does not produce the relationship that is desired, there is also the option for users to create a user-defined relationship between objects. This allows data stewards to manually input a relationship between objects to define them as being equivalent or as one having an impact on the other. Finally, the impact and lineage relationships of an object can also be displayed in a tabular format for an easy export out to an Excel document.
Figure 4 Tabular view of the impact diagram of the FACT_INTERNET_SALES table. The Export to Excel button is highlighted in the upper right
Metapedia
Metapedia is the answer to the second common problem from above. It is a corporate data dictionary application with a built-in approval workflow that serves as a tool for data stewards to define common business terms and submit those terms for approval from higher-ups. Once approved, users can link those terms to other previously defined terms as well as to objects over in Metadata Management. With plenty of use, the population of terms will become a strong corporate resource that can be used throughout the enterprise. Furthermore, Metapedia terms that are linked to BusinessObjects reports can be accessed directly through the SAP BusinessObjects InfoView portal when opening that report. And finally, Metapedia terms are exposed via a WSDL that allows web developers access to the terms and definitions through a web services call.
Figure 5 The Metapedia home screen. Notice the Category hierarchy on the left and the Terms on the right
Terms are created through the All Terms area on the left side. They can then be added to one or multiple categories, depending on what makes sense for that Term. It is important to note that although Terms can be added to multiple Categories, a Category can only be added to one other Category. For those organizations that already have a data dictionary or wiki in place, it is possible to embed URLs directly into Metapedia terms. In fact, Metapedia definitions are formatted in HTML, so the text can be enriched beyond simple plain-text format. Let’s take the term “City” as an example:
Figure 6 Metapedia Term editor. Notice the embedded hyperlink, text color options, and other text formatting features
Terms can be further enriched by adding synonyms and keywords. These enhance a Term by causing this term to appear in search results if any of those synonyms or keywords are ever searched for through the Metapedia search function. The search functionality is also accessible through the WSDL mentioned earlier. As an example, if I search for “Locality”, City will now appear in my search results.
Figure 7 City appears in my list of search results for "locality"
Figure 8 Terms that have been approved (green check mark) can be related to other terms and to metadata objects
Finally, notice also that Terms that have been approved have a green check mark to the left of them, and they are then able to be linked to other terms and metadata objects (Error! Reference source not found.). Clicking on a related term or associated objects brings up that linked term (Figure 9) or object (Figure 10) directly.
Figure 9 Linked term "Customer" accessed by clicking on the link through the Business Partner term
Figure 10 Associated object "BP: Partner 1" in the Business Partner term. Notice that clicking on the link took us over into the Metadata Management area of Information Steward
Conclusion In conclusion, Metadata Management and Metapedia can be powerful tools to use when building or expanding your Data Governance program. Metadata Management gives business users visibility into how data moves through the organizational landscape, which includes illustrating the source systems that the data in a report comes from. And Metapedia provides a corporate data dictionary for business users and web developers to use to determine the correct definition of terms that are used in your organization. Utilized together, they become a powerful application for giving business users the ability to better determine where data comes from and how corporate definitions relate to the metadata that they care about In future posts regarding Information Steward, I will highlight client-specific success stories that span a variety of industries. I’ll also show how these different modules can be used independently or collectively to help you meet your data governance program goals.
Rich Hauser, Manager
Decision First Technologies, A Protiviti Enterprise
Rich is a Manager in the Data & Analytics practice of Protiviti, specializing in Enterprise Information Management. He has delivered customized Data Governance and SAP BusinessObjects solutions for customers of all sizes across a variety of industries. With Protiviti, Rich utilizes SAP Data Services and SAP Information Steward.
