SAP E-Bites - SAP Information Steward - Monitoring Data in Real Timevc

Don Loden

SAP®Information Steward: Monitoring Data in Real Time

~ Rheinwerk® Publishing Bonn • Boston

What You'll Learn Implement real-time information governance with SAP Information Steward with this E-Bite! Learn how to use three key SAP Information Steward modules: Data Insight, Metadata Management, and Metapedia. Build views and create scorecards with Data Insight. Discover data transparency, and collect and manage metadata with Metadata Management. Implement an enterprise data dictionary to apply business terminology to

data with Metapedia. Complimented with a comprehensive case study, you'll take what you've learned and apply real-world practice! 1

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Information Governance in the Real-Time Enterpri se . . . . . . . . . 1.1 Real-Time Enterprise..... . .. . ........... . .. . ..... 1.2 SAP Information Steward . . . . . . . . . . . . . . . . . . . . . . . . . . Real-Time Governa nce with Data Insight . . . . . . . . . . . . . . . . . . 2.1 SAP Information Steward Validation Rules and Scorecards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Connecting SAP Information Steward to an SAP ERP Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Building an SAP Information Steward Scorecard.. . . . . . . . Meta data Ma nagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Con necting to SAP Systems . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Setting Up and Runn ing Collection Tasks . . . . . . . . . . . . . . 3.3 Examin ing Results in SAP Information Steward. . . . . . . . . . Build a n Enterpri se Data Dictionary with Metapedia . . . . . . . . . 4.1 Setting Up Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Setting Up Categories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Exposing Metaped ia Content to Other Systems . . . . . . . . .

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Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Building Content in Data Insight. . . . . . . . . . . . . . 5.1 5.2 Extracting Metadata with Metadata Management. 5.3 Creating a Data Dictiionary w ith Metaped ia . . . . . What's Next?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Information Governance in the Real-Time Enterprise

Information governance is a core business process that ensures data is treated as a corporate asset and formally managed throughout an enterprise. When executed properly, this information governance paradigm ensures data can be trusted. Defined like this, it is obvious that information governance brings many benefits to an organization. Information governance has always been a puzzling topic for organizations and has gained more ground in recent years, with many organizations embracing information governan ce as a top strategic priority. Without information governance, issues such as data silos, repetitive work, and sacrificing long-term maintainability for short-sighted, short-term application and data solutions can arise. Although there are numerous other advantages to information governance, there is one looming reason why it is now more important than ever: The dawn of the real-time enterprise. Being able to see the effects of data generated in real time creates exciting new opportunities and obstacles that must be addressed. This section will discuss the need to govern information and data quality in a real-time enterprise and the effects of failing to consider these challenges.

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Real -Time Enterprise

A real-time enterprise focuses on the responsiveness of an organization,

bringing formerly discrete business systems into cohesive union by leveraging data in real-time. In this model, business systems are designed in such a way that greater transparency can be achieved by reducing redundant datasets, accomplished by finding data at the source to support either analytical or operational support decisions. Consider how a platform like SAP S/4HANA increases the capability to use one data-centric

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platform to not only control business processes, but also extract analytics from the same dataset. This capability supports many goals that a realtime enterprise sets out to fulfill, such as: » Reduced turnaround times for customers » Increased transparency straight to core data structures » Ability to share information from a central source across departments » Transactional reporting available at the time of record creation » Reducing overhead of systems and support staff There is tremendous potential for an organization that embraces this shift, as well as many new challenges. We will discuss both opportunities and challenges in the following sections. Opportunities

Real-time governance and attention to data quality is more important than ever when considering an in-memory technology like SAP HANA. With SAP HANA, data can be reported on directly from the source application, and this is especially true for operational reporting. This is only now possible because SAP HANA allows access to sources that traditionally would not lend themselves to direct reporting structures. This is especially true in cases in which a central SAP ERP system is the primary system of record. Figure 1 shows the simplicity possible with SAPS/ 4HANA in regard to operational reporting. This makes a legacy concept of a latency-driven external operational data store somewhat redundant, causing management and architects to wonder whether the additional overhead and complexity of an external operational data store is needed to support the operational reporting paradigm. Although there is no longer a need for redundant data, data quality is still an important issue.

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Figure 1 Conven t ial SAP ERP Reporting Compared with SAP S/4HANA Reporting

Challenges Figure 1 shows a simplified reporting method without the ETL transformational layer and ODS. However, you must consider data quality up front in an SAP ERP system. Without quality data, attention to data quality, and proper governance and monitoring, these appliances will only propel bad or incorrect data in reports. In the conventional SAP ERP model shown in Figure 1, an ODS would have provided cleansing and enhancements via an ETL process. However, in the real-time enterp rise,

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the fact that you are using transactional data for analytics makes a latencydriven process useless. This is important because organizations already have mechanisms for this in business intelligence (BI) constructs and the ODS layer. From this legacy perspective, data quality issues are corrected and data is prepared in the extract, transform, and load (ETL) layer. These correction mechanisms must be accounted for or at a minimum properly understood in this new "real-time enterprise" before an in-memory appliance can fill all of these gaps. This is where real-time governance comes into play. It may seem obvious, but real-time governance happens in real time!

Straightforward enough- but think of the challenges this introduces. Many master data management activities, like matching and cleansing, are based on transactional latency. To match and cleanse data, data must be collected and processed to be placed into cleansed containers with quality deduplicated data. That is not to say the matching and cleansing process goes away, but thes e types of things must be thought through. If you are going to report on transactional data in real time with SAP S/4HANA, you must deal with data quality in real time in the transactional system. Much thought and design must be placed into the real-time enterprise before bad data begins driving business processes poorly in real-time. This is not an overnight shift, so do not expect too much up front without proper consideration of going "straight to the source" for the data with an in-memory solution. Now, how can an organization begin this conversation about real-time governance? It begins by examining the real-time data quality monitoring and data quality socialization functionality of SAP Information Steward.

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SAP Information Steward

SAP Information Steward provides a single platform to discover, assess, define, monitor, and improve the quality of data. Users can do any of the following tasks:

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Information Govern ance in the Real-Time Enterprise

» Profile data with complex profiling options » Create and run validation rules embedded in scorecards » See relationships of metadata from sources all the way to fields used in reports » Manage a data dictionary » Create and develop complex data cleansing rules and dictionaries SAP Information Steward benefits from being part of the SAP product stack and sharing close API ties to the SAP ERP platform. SAP Information Steward can be directly connected to a production SAP system in a safe manner, allowing unprecedented access to an overarching view of data in SAP. A detailed view of how this is accomplished can be found in Section 2.1. SAP Information Steward allows the user to interact with data from a data validation standpoint that can be modeled by a master data domain or by a process flow for the organization. Having this organized, business-centric visibility is crucial to a successful post in, for example, an SAP HANAenabled SAP ERP platform, and is a requirement for governing data in a real-time enterprise. Tactically, governing data in real-time takes many forms in SAP Information Steward, and we will examine a few of these in the next section. SAP Information Steward allows you to create complex quality validation rules to check whether data complies with business constraints and requirements. These rules generate passing or failing scores that are configurable to a variety of situations and use cases. Rules are reusable and illustrate individual business scenarios and objectives. From these rules, a developer can create visualizations based on positive or negative results. These different rules can be used together to create a complex data quality scorecard. In this E-Bite, we will walk through these rules, along with the three primary modules in SAP Information Steward in each individual section:

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Real -Time Governance with Data Insight

Data Insight, Metadata Management, and Metapedia. Finally, in Section 5, we will take what we have learned and apply it to a comprehensive case study.

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Real-Time Governance with Data Insight

Many customers are aware of SAP Information Steward's capabilities within the Data Insight module to rapidly build complex data scorecards, but miss the fact that they can do so safely and effectively against a live production of SAP ERP or SAP CRM. In Section 1, we discussed the business case and needs for monitoring data quality in real time. Now, we will begin to see how SAP Information Steward enables such monitoring as an option for an enterprise. In this section, we will explore the scorecard and rule creation processes to create the foundation for governance in a real-time enterprise. We will also look at best practices for SAP application connectivity and the interaction between SAP Information Steward and SAP ERP to ensure both quality performance and safety for the SAP ERP transactional system.

2.1

SAP Information Steward Validation Rules and Scorecards

SAP Information Steward has many features, but one of the tool's greatest strengths is its ability to track the quality of data over time in a graphical and quantitative way. The heart of this functionality is the SAP Information Steward scorecard. The scorecard is a data socialization tool that validates data by using custom validation rules to ensure that data conforms to given corporate standards. The scorecard then tracks the effects of data conformity over time by plotting that information with a graphical trend line. The scorecard example shown in Figure 2 shows a relatively flat trend and scoring of data.

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This scorecard tracks customer data and shows a quality trend that is mostly flat, which would not be the case if either the data or the rul,e had changed considerably over time. 'fhe customer example scorecard reports a quality score of9.21, and this quality score is comprised of two quality dimensions: accuracy and completeness. There are many other data quality dimensions that may be utilized to group and order the presentation of data quality for socialization throughout the enterprise. Data quality dimensions are a useful tool for a high-level grouping or for the organization of rules and measurements; however, these lead quickly to what rules and measurements comprise accuracy. To see this level of detail, click the SHow DETAIL button on the main scorecard screen (see Figure 2) to show the details of the scorecard quality dimensions, as shown in Figure 3.

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On the DETAILS screen, we can see that under DATA QUALITY DIMENSIONS there are the AccuRACY and COMPLETENESS areas. These rules make up the core functionality of the scorecard. Each rule validates a particular business scenario and either passes or fails the data being evaluated. This evaluation creates a score for a given connection , which is illustrated as a rule binding. Individual rule binding scores for each connection that a rule has "rolled up" to an overall rule score. Each rule score rolls up to a composite score for its respective data quality dimensions. The data quality dimension composite score also rolls up to the key data domain score, which represents the highest-level score for the scorecard. Scoring is a crucial concept for a scorecard business scenario and can be adjusted to properly measure particular business conditions. We will examine this concept in greater detail later, in the section Assess and Set Scorecard Weights. Scorecards are essentially a presentation layer for the various validation rules in SAP Information Steward and a means to dial in their measures in

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order to tell a story of business relevance. It is important to note that validation rules can stand on their own as discrete operational measures, and they can range from very simple completeness checks to extensive and complex designs . To design and construct a rule, you might think that you would need to opt exclusively to code this rule in a scripting- or SQL-type interface. However, in SAP Information Steward this is not the case. This tool is designed with the business user in mind, so there is a graphical user interface (GUI) used to define and construct the validation rule. The Rule Editor in SAP Information Steward is the heart of Data Insight. These topics present an overview of how validation rules and scorecards work together to produce a cohesive business story in an SAP Information Steward Data Insight scorecard. We will fully explore the scorecard creation process in Section 2.3. Let's first examine ways to connect SAP Information Steward directly to tihe SAP ERP system. Only after this connection is established can a user achieve real-time predictive governance.

2.2

Connecting SAP Information Steward to an SAP ERP Application

The SAP ERP application is the solution that global enterprises rely on to run their businesses. However, seeing the data quality and relative relationships as SAP GUI screens can be challenging. This is especially true when a business user needs an overall picture of how data is intertwined to tell the full story of how a particular business process functions . SAP GUI's purpose is to provide input and lookup screens in order for members of an organization to do their respective jobs. This requires information to be compartmentalized and effectively spread throughout the application on a multitude of SAP GUI screens. There are many cases in which organizational constructs that depend on each other may be entered and maintained on separate transaction screens.

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What Is Required

SAP Information Steward uses functions that support the use of ABAP, BAPI, and !Doc interfaces on SAP servers. You will need some or all of these functions in the normal operation of the software in an SAP environment. Typically, you will focus on the function modules deployed with the SAP Information Steward software. These functions perform such operations as dynamically loading and executing ABAP programs from the application server, efficiently running preloaded ABAP programs, allowing for seamless metadata browsing and importing from SAP servers, and reporting the status ofrunningjobs. Some of these functions read data from SAP Business Warehouse (SAP BW) sources. It is important to note whether you need to load the functions onto your SAP application system or whether the f111nctions already exist. This is usually determined by what SAP application system version you have deployed in your environment. For SAP NetWeaver 6.0 and later, SAP functions for SAP Data Services and SAP Information Steward are included in three SAP components: PI_ BASIS , SAP _APPL , and SAP _B ~J. If your system does not contain these functions or you plan to upgrade SAP systems , see SAP Note 1919255 for information about SAP Data Services and SAP Information Steward transport functions. If your SAP NetWeaver system does not contain these functions, please contact SAP support and reference SAP Note 1919255. As noted, some SAP NetWeaver systems already contain these supplied functions from SAP and you should check your system. If these functions are not available, then you will need to install the transports. If they are available, then you are ready to begin a connection to SAP Information Steward. SAP Information Steward makes a connection to SAP NetWeaver and calls functions that exist in the SAP application functions construct. What SAP Information Steward can call and utilize depends on the security permissions and authorizations that are granted to the non-dialog user making the connection from the SAP Information Steward system. Note that

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although SAP application functions may not be required, depending on which version of the SAP NetWeaver stack you are running, SAP custom security will always be required as a custom setup option, which requires a design by either SAP Basis or the customer's security team. We will explore the setup and design of t hese components in the following section in much greater detail and also will outline the workflow of the setup process implementation. Implementation Workflow

The setup process for the SAP application server can be intimidating, but there is a relatively predictable process workflow that can be utilized to guide setup activities. This section will walk through each step in this workflow. Install SAP Application Transports if Required

The easiest way to survey your SAP system to see if you need to obtain and install the SAP transports is to search for the /SAPDS namespace on the SAP system. To do so, log on to SAP GUI and run Transaction SE37. The resulting SAP GUI screen allows you to search for the /SAPDS function group, as shown in Figure 4. Function Builder: Initial Screen

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If the function modules are not present, consult the readme documentation on the SAP Data Services server that you used to obtain the transports (see Figure 5).

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Real-Time Governance with Data Insight

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Here, there are basically two options: Use the provided transports to install on a legacy SAP NetWeaver 4.6 system, or obtain the transports from SAP to install using SAP Note 1919255. If you do have to install the transports, then please refer to the SAP Data Services supplement for SAP guide for your version of SAP Information Steward (visit http:// help.sap.com, and look in the DATA MANAGEMENT section). Design and Implement Security Structures

After the function modules and proper namespace are present on your SAP system, you will need to ensure that the proper authorizations exist so that SAP Information Steward connections can be established. Just as with transports on newer SAP systems, the basic roles may already exist. You should check your SAP system to see if the roles for SAP Information Steward are already present. Roles Your system may have SAP Information Steward roles SAP_ DS_ DEV and SAP_ DS_PROD if you operate a newer SAP NetWeaver system. If this is the case, then these roles can be used as templates for defining authorization profiles for SAP Data Services users working with SAP BASIS/SAP NetWeaver functionality. SAP _DS_DEV is a template for DEVUSER and SAP _DS_PROO is a template for TESTUSER/ PRODUSER.

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Real -Time Govern ance with Data I nsight

In any case, you will need to design security profiles for your service account that fit each stage of your SAP landscape. SAP Information Steward needs a variety of authorizations to create content in a development (dev) system that will not be ne.e ded in a production (prod) or quality assurance (QA) landscape. You must make sure that your design meets all of these needs to be successful. Table 1 details the authorizations that are needed at a high level. but if more information around the details of the functions accessed in the authorizations is required, please refer to the SAP Data Services supplement guide for your version of SAP Information Steward: http://help.sap.com/businessobject/product_guides/sbods42/en/ ds_42_sap_en.pdf. Service Account User

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It is important to note that the users described in Table 1 are not dialog users, or users that someone would use to access the SAP GUI. These

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users are strictly for SAP Information Steward to facilitate communication with the SAP application system. The user-facing security happens on the SAP Information Steward platform itself in the form of users and roles. These users and roles are out of scope for this section, as we are strictly discussing the SAP-related connections that are needed for SAP Information Steward to access the SAP system. It is important to note that SAP authorizations can be managed in two ways:

» Profile generation method When using the profile generation method, activity groups define and control all of the allowed authorizations. Each user is assigned to an activity group that has information about the allowed activities that each user can perform.

» Profiles assigned to users method The profiles assigned to users method relies on the administrator or security team first creating the authorizations needed to perform specific tasks, then creating a user profile to assign the specific authorizations on the SAP system. After the authorizations and the profile are both created and activated, the profile may be assigned to users. Either method is acceptable. The choice should be based on the organization and whether the chosen method will fit into existing user maintenance processes and protocols_ Test with SAP Basis and Security to Remediate Issues

The final task to complete before closing out the security work is to test both the SAP application function modules deployed on the system and the relative access to the system using the created authorizations to these function modules. This testing is typically performed by facilitati ng a session with an SAP Information Steward subject matter expert (SME) and both an SAP Basis resource and a security team resource.

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Real -Time Governance with Data I nsight

The SAP Basis resource is not required if the security team resource has both the ability and knowledge to run SAP application traces in order to illustrate distinct processing on the SAP system. The idea is that the SAP Information Steward SME runs various tests on the SAP Information Steward system, such as (but not limited to) importing SAP tables, viewing data on the tables, creating a view on the SAP tables, and running the view. Any one of these tasks may produce errors, which prompts the SAP Basis and/or security team member to review the errors using the SAP trace, to see where the system error was occurring and to troubleshoot the error during an interactive session. If the right people are in the room to troubleshoot any errors, then these sessions go quickly. It should be noted that any changes made during this process should amend the transports used to create both the imported function modules and the security authorization objects and user profiles. By doing so, you can successfully move all changes at one time throughout the landscape as a unit. This not only is best practice, but also assures that no valuable work or troubleshooting solutions are missed in any of the SAP landscapes. In the following section, we wil[ first create views of data on mUJitiple tables, then layer rules on top of these views, and finally place the rules into the scorecard to produce the end result: a finished SAP Information Steward scorecard.

2.3

Building an SAP Information Steward Scorecard

One cannot simply jump straight into developing the visualization of a scorecard. There are many foundational components that must be in place before this can occur. These building blocks of scorecard development start from the ground up. The first of these are the SAP Information Steward views. You must also create a validation rule, which needs to be bound to a view and scheduled. Only then are you ready to associate the

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rule with a scorecard. To begin this process, we will first explore how to build an SAP Information Steward view in the next section. Build the View

To have data for a rule to validate, at a minimum you need to have a table containing the desired data. However, in most highly normalized operational systems, a table usually will not suffice to provide a proper productive data set. The data in most operational systems typically is highly normalized, or split into segmented tables, and SAP is no different. Take for instance the material master table in SAP: table MARA. This table is extremely wide, with over 180 columns for most customers, and one would think that so many columns should cover most of the data needed for validation rule processing. However, when you need something as core to a material as a material description, you need to go to table MAKT to get it. This is a simple example, but all of SAP is like this. In order to get data that you need to model a proper business scenario, you must combine these tables together. The way to accomplish this in SAP Information Steward is to create a view. However, with SAP Information Steward, the decision of the user goes even further. Before creating the view, the SAP Information Steward developer must decide whether he needs to create a standard view or an ABAP-enabled application view. Standard Views

A standard view is a straightforward approach to creating views in SAP Information Steward. View creation occurs solely in the tool and does not involve any code creation or management of ABAP in SAP. An example of a standard view can be seen in Figure 6.

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In this view, there is only data sourced from one table, as you can see in Figure 7, but you do not need to stop at table KNAl (Customer Master) if more data is required. For instance, if you need to include mll!ltiple addresses used over the life of a customer, simply click the ADD button , as shown in Figure 7.

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The number of tables that you combine is limited only by the business scenarios you are trying measure with each rule. From that perspective,

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you really must let the business scenario dictate your design. Just remember that it is easy to add other tables and join them together. However, joining these tables also comes with a performance penalty in a standard view. Taking the standard view in Figure 7 as an example, you may be transforming the original data in table KNAl with an expression in the mapping of a field, or processing only active customers as a subset of table KNAl. The important thing to note is that data must first be extracted from SAP to the SAP Information Steward application server for processing. This does not mean that SAP Information Steward stores any of the customer data. The application server processes the data as it is streamed from SAP. This is something to particularly watch with standard views that contain multiple tables. For example, if you have two tables and your processing requires the entire data set from both tables, then both tables will be extracted from SAP, and the combination of the two data sets will occur entirely on the SAP Information Steward server. This is fine for master data tables like KNAl , as they do not contain nearly as much data as transactional tables. The data will fit into memory on the application server. There is a simplicity involved here. The SAP Information Steward developer just creates the view using the GUI, saves it, and then executes the view against SAP. There is no involvement with SAP Basis, a security team, or a team of ABAP programmers. This allows for rapid prototyping and development, and most importantly places all development control in the hands of the SAP Information Steward team. This is crucial for these efforts to have a rapid deployment time. However, as stated before, there is a limit to how much data you can fit into memory on the application server. When you run out of memory, the SAP Information Steward application server will start to page the data to the disk, and performance will all but grind to a halt. There is no danger to the SAP NetWeaver system, as the data is extracted only in tiny, 10,000 record segments and compiled on the SAP Information Steward server. The problem is that your processing in SAP Information Steward will essentially stop when

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the memory limit is reached. When you encounter problems like these, it is time to consider creating ABAP-enabled SAP application views. ABAP-Enabled SAP Application Views

ABAP-enabled SAP application views are exactly what they sound like . ABAP is created on the SAP NetWeaver system, and processing occurs entirely on the SAP system before the data set is streamed back to the SAP Information Steward application server. This is extremely important for combining very large transaction tables. Creating an ABAP-enabled view is simple. The only thing that the SAP Information Steward developer needs to do is to select ABAP from the DATA TRANSFER METHOD dropdown menu, as shown in Figure 8.

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Then, just select the processing option from DATA TRANSFER METHOD. However, this action only begins the process of managing the generated ABAP code through the SAP NetWeaver landscape. After generating the ABAP, it must be transported from the development system to the test system, and after testing it must be transported again to production. Both of these SAP transports must occur, because the generated code is now SAP NetWeaver code and must be managed just like any other SAP appli-

23


cation development. This is not only additional overhead to a proj ect; you also affect other teams with the introduction of the SAP Basis team at a minimum and sometimes the SAP Security team. These teams will have different priorities from the core SAP Information Steward development team and therefore can significantly slow the SAP Information Steward development process. The following four phases are typical for application development:

1. In the design phase, a high-security profile user designs the data and work flows and generates ABAP programs. The ABAP programs are pushed to the development SAP landscape and executed. 2. In the consolidation phase, developers upload generated ABAP programs into the test SAP system with predefined program names and preregistered connections. A user in this phase can run these programs with a lower security profile- TESTUSER, for example. 3. In the test phase, the SAP Information Steward views must be exported to test the SAP Information Steward landscape, and the generated ABAP programs must be transported to the SAP test system. This process is usually performed by an SAP Basis resource in the organization. 4 The production phase begins when no additional program modifica-

tions are required. The user involved in the move to the production phase has the lowest security profile or fewest permissions. Now that we have a good understanding of what is involved with view creation, we can move on to the second phase in building an SAP Information Steward scorecard: building a rule. Build a Rule

Rules provide the quantitative foundation to demonstrate whether your data conforms to your business scenarios. Rules can drive operational behavior and can fit together to tell a greater story in a scorecard. At their core, rules are pass/fail conditional objects. Failure records are reported

24

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in scores that display next to the columns to which they are bound, as shown in Figure 9, by the red and green rectangles holding blue numbers. When you click on the blue numbers, if there are failed data records they will be displayed in the FAILED DATA section shown at the bottom right of Figure 9. This is an operational view allowing a user to quickly see how various fields are performing according to business rules that are bound to the table. You can also quickly share this data with others in the organization by exporting to Microsoft Excel. Doing so helps drive data quality in the organization by quickly elevating and allowing access to the root of the problem so that users can correct the data quality issues back in the source system. w~ ...._•• o-

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In combination with the SAP application views discussed earlier, this complex and fully customizable data quality monitoring can occur in real time against the SAP system in a holistic manner. This is an important operational activity in the SAP S/4HANA world. If the SAP ERP system is on SAP HANA and reporting is now in real time, then it is more important than ever not only for the data in SAP to be of high quality in the source, but also that issues be remediated as quickly as possible.

25

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The operational view of the rules shown in Figure 9 are a result of the designed rules. To code and develop a rule, you must navigate to the Rule Editor, as shown in Figure 10. The Rule Editor is the GUI in which rule development occurs, to both design and compose the rule. Note the relative lack of a normal coding interface. After you give a rule a name and description, you need to set up its parameters. Parameters are required; they are the conduit for connecting, or binding, the rule to columns in views and tables. The rule on its own is just a set of instructions. Only when it is bound to a table or view does it take action and produce scores and failed data. Therefore, establishing parameters is the first step. X

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Figure 10 shows the rule ZB_G l 4_Cus t omers , which has an assigned parameter called $P _ l _ City in the PARAMETERS section. This parameter is merely a placeholder for the city data that will be met when you bind the rule to the table or view containing city data. The evaluation of the city

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data will occur based on what is specified in the EXPRESSION section. In this example, parameter $P_I_Ci t y is used to evaluate a NULL condition and not equal to blank values. Notice that the expressions are developed using the GUI screens and the foUowing "code" is generated into the section you see in Figure 10: BEGI N RETU RN $P_I_C ity I S NOT NUL L AND $P_I_C ity ! =

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oper. The development occurred using the tools in the GUI, which greatly enhances the opportunity for less technical resources to develop rules. There are times when rules will not be this simple or straightforward and traditional coding will be required, which is why SAP Information Steward offers the ADVANCED RULE EDITOR perspective shown in Figure 11. X

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In this perspective, the developer can use sophisticated templates for proper best practices code and GUI-driven functions or parameter value assignments . The functions are grouped into the following types in the Advanced Rule Editor:

27

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)} CONVERSION

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Functions structured around pattern validation. The Advanced Rule Editor shares many characteristics with conventional programming tools, so a seasoned developer can feel right at home. Another benefit is that the syntax of the scripting language in SAP Information Steward is actually the same as that of SAP Data Services. This is a great benefit for organizations that are choosing to standardize on the SAP toolset, because there is a great deal of overlap both in terms of functionality and syntax. The Advanced Rule Editor is a great platform to create even the most complex rules. However, until a rule is bound to a view or table, the rule is in a useless state. You have designed, developed, and created a rule, and now you must bind it. Bind a Rule

Binding a rule is a straightforward task - and incredibly important. Without a connection, the rule is useless. Now, let's bind a rule to a

dataset. To bind a rule to a view or table, click the

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After clicking the BIND button , the RuLE BINDING EDITOR screen opens, as shown in Figure 13. X

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The Rule Binding Editor is a straightforward screen used to map the physical City field from the ZB_G14_CUST_Gene ral Data_Add ress view. After that relationship is set, you can save the rule binding. You can also set a threshold under the THRESHOLD section. This will change the color of the display based on the settings. This way, the rule behavior and display can be altered based on the severity or impact of the business scenario. After the rule is bound to the view, you are ready to collect scores by running the rule task and setting up a schedule.

29


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Run the Rule Task and Schedule

Now that you have bound a rule, you need to calculate its scores. This is required to move forward in the scorecard process and to activate the rule and promote it into a useful state. For a rule to be useful and measure anything, you need it to derive a score. The first step in this process is to run a rule task in an interactive mode to provide scores to review in SAP Information Steward. After review, the final step will be to schedule the rule tasks in the SAP Information Platform Services Central Management Console (CMC). This way, scores are collected at regular intervals to provide checkpoints of quantitative rule and data performance that drive governance activities and underpin the SAP Information Steward scorecard. Let's examine what tasks look like in SAP Information Steward. Figure 14 shows a series of tasks, including both task history and an actively running task.

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Although task history is visible on the TASKS tab, the tasks are created by calculating the score for a particular table or view. This is done on the RuLE RESULTS tab by selecting the table(s) or view(s) you wish to calculate and clicking CALCULATE SCORE, as shown in Figure 15. After you click CALCULATE SCORE, the DEFINE TASK: RULE screen appears, as shown in Figure 16.

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Fi gure 16 Define Task: Rule Window to Specify View(s) or Table(s) to Process

However many tables or views you selected from the RULE RESULTS screen will display on the DEFINE TASK: RULE screen. From this screen, click SAVE AND RuN Now. This will create all of the scoring that appears in the RULE RESULTS screens. This is one example of running the rules interactively against the source system. This process is fine from an operational standpoint, but to make the rules available to underpin a scorecard as a foundation for governance you 31


want to schedule task processing so that scores can be calculated at recurring intervals. The recurrence will be crucial to produce a scorecard with proper and timely checkpoints for data quality. Scheduling the rule task is something that would seem likely to be accomplished in the SAP Information Steward application, but this is not the case. Scheduling instead occurs in the CMC, which is an SAP BusinessObjects legacy component that controls both security and scheduling. Access the CMC by using the URL http://:8080/BOE/CMC. From the initial CMC screen, nog in with your user name and password. You can use a variety of methods to administer security for SAP Information Steward, but to keep the examples simple for this publication we have chosen to use out-of-the-box Enterprise CMC authentication. All user interaction for the following examples is performed with the Administrator account; this is something that would need to change for a production-ready scorecard or project. After you log in to CMC, you will see the CMC home screen. To get to SAP Information Steward-relevant content, click the INFORMATION STEWARD link. as shown in Figure 17. Central Management Con1-ole Welcome: Ad"*'ktrator I Pref«tnees IH.-, • I Log Off

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After clicking the INFORMATION STEWARD link, navigate down the INFORMATION STEWARD tree to DATA INSIGHT and select the PROJECTS folder, as shown in Figure 18. 9 1'9 ht1P:/ IocallooMe.faces>servoce=' :.:J ~~

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Then, browse the PROJECTS folder and navigate to the IS_RDS_MATERIAL project, as shown in Figure 19. This project was shown earlier in SAP Information Steward and is where all of the rule and view work is stored. You will notice that the rule task CUST_ Remai nder _ Rules shown in Figure 19 is the same rule task that was run interactively earlier in the section. Right-dick CUST_REMAINDER_ RULES and select SCHEDULE from the popup menu, as shown in Figure 20.

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After clicking SCHEDULE, the SCHEDULE: CUST_REMINDER_RULES dialog box opens, as shown in Figure 21. Schedule: aJST Remainder Rules Hide Navigation

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This screen displays the available recurrence options to create the S·chedule for the rule task. Select DAILY from the RUN OBJECT dropdown menu to run this rule daily per your business rules (see Figure 22). ?ClX

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This adds the START DATE/TIME and END DATE/TIME fields to specify when you wish to start and end the schedule (see Figure 23). Clicking ScHEDULE will instigate the task and start it running in the CMC, as shown in Figure 24.

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The task will immediately run, but more importantly, a recurring task is established to run the rule at the specified interval (in this case, daily). Scheduling any other rule task is accomplished in the same manner and must be repeated whenever schedules are needed. After the schedule completes, you will need to log back into SAP Information Steward to view the score results on the connection, as shown in Figure 25. It is important to note that the resource responsible for scheduling the rules and scorecard tasks detailed in the section Schedule Scorecard Application Component may not be the same resource that creates the rules and views in SAP Information Steward. We find that typically scheduling- and CMC-related work is relegated to an IT resource rather than a business-focused, technical resource that creates SAP Information Steward content.

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Associate Rules with a Scorecard

Compared with rule development, associating rules with a scorecard is easy and straightforward. Scorecards only consume content already created, so they are much like a reporting presentation layer. You will see as we step through the examples in this section that very little of the scorecard is created as part of this process; it simply repackages existing content. The scorecard is assembled in the ScoRECARD SETUP screen shown in Figure 26. ........._ ~

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Click ADD in the KEY DATA DOMAIN top-left section, and the KEY DATA DOMAIN DEFINITION screen will popup, as shown in Figure 27. Kev Data Domain Definition

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Figure 27 Define the Key Data Domain w ith a Name and Description

Define the key data domain by first providing a name-in this example, "ERP Customer"- and then providing a description for the name that you chose. It is important to note that the key data domain will beco me the graphic that most users will associate with the scorecard, so it is very important that both the name and the description be both concise and accurate. Now that you have the key data domain defined, click ADD under the QUALITY DIMENSIONS top-middle section (see Figure 28). This will open the popup screen ASSOCIATE DIMENSIONS FOR: ERP CUSTOMER, shown in Figure 29 .

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For this example, select the ACCURACY, COMPLETENESS, and CONFORMITY quality dimensions from the ASSOCIATE DIMENSIONS FOR: ERP CUSTOMER popup screen. After clicking SAVE , you will see the quality dimensions in the SCORECARD SETUP screen (see Figure 30).

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Fi gure 30 Add Ru les to t he Scorecard by Clicking Add

With both a key data domain and quality dimensions in place, you are ready to add rules to the scorecard. To select rules, click ADD in the RULES top-right pane. The ASSOCIATE RULES FOR: ERP CUSTOMER popup window will open (see Figure 31).

39

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Figure 31 Select Rule(s)

With rules added to the scorecard, you can select the rule binding by clicking ADD in the bottom RULE BINDINGS screen (Figure 32). Then , select the view(s) or table(s) you wish to bind, as shown in Figure 33.

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40

2

I


2

Repeat the previous steps until all rule warnings go away and green validation checkboxes appear, as shown in Figure 34. It is important to remember to not only repeat the previous steps, but also to work in the same left-to-right and top-down manner to add the rest of the rules and bindings. You will end up with the completed scorecard shown in Figure 35. SAP-. SAP

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41


After the scorecard development in the SCORECARD SETUP tab is complete, you can browse to the SCORECARD link on the SAP Information Steward homepage to see the scorecardl results (Figure 36). Then, you can explore the detail of the scorecard (Figure 37). This way, you will ensure that you have the scorecard properly designed before you assess the weights that you will assign if needed.

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42

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Having the scorecard designed and processed is very important and the end result for scorecard creation, but you are most likely not ready to move to production until you assess whether or not you need to be concerned about assessing the weights of the key data domain or rules. Weighting a scorecard is a powerful way to tell a business story with your data. In the next section, we will discuss weighting and show examples of how to apply it to a scorecard. Assess and Set Scorecard Weights

We used all of the default weights on the scorecard created in this section, but now we will explore how to change those weights and what it means to do so, both from a logical and a technical perspective. First, Figure 38 shows your finished scorecard. Use the

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section to alter the weight of the rule ZB_G14_CUST_ CITYEXISTS. Set it as a fixed value of 70 percent. The effect of this change is shown in Figure 39. RULES

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Now that you have declared that the weight of this particular rule should be 70 percent, the other respective rule, ZB_G14_CUSTNAME0FADDRESSExisrs, automatically shifts to 30 percent. This is a nice time-saving feature, and the results cascade to the roll up of the scorecard. This weighting activity is important from a logical perspective, because we are illustrating that having a city value is much more important from a business context than whether or not you have the name of address values. This is a

44


simple example, but an important concept. The weight of a measure drives business relevance. Rule results are very important, but they will never break out of an operational perspective. Only through scorecard weighting can you tell an effective business story of data quality compliance. After assembling and organizing the weights for business content, the scorecard development is complete and ready to be scheduled. We will illustrate scheduling activities in the next section. Schedule Scorecard Application Component

Scheduling the scorecard is much like scheduling rules, as covered in the section Run the Rule Task and Schedule, but with a couple of important differences. First, the scorecard will only ever have one task. Rules are custom-developed objects and can have multiple connections to schedule. You have the option of whether to roll all scheduled collections into one task or to schedule multiple tasks at various intervals. With scheduling a scorecard, you do not have that option. The other important distinction is that a scorecard calculation will only run once to calculate the entire scorecard. Because this calculation rolls up the entire rule result scores at one time, it makes sense that all key data domains for all rules process at once. This is true whether you have one key data domain with a few rules, as in the current example, or hundreds of rules with ten key data domains. The process works the same way with one calculation regardless. We will now examine the scheduling process in detail by working through the necessary steps. First, log in to CMC. Then, click the APPLICATIONS link, as shown in Figure 40.

45

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Next, click the INFORMATION STEWARD APPLICATION link from the APPLICATIONS section of the CMC. It is important to note that this is different from the main INFORMATION STEWARD link shown earlier. The link location is shown in Figure 41.

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The U TILITY CONFIGURATIONS page will open, and you will see CALCULATE SCORECARD, as shown in Figure 420 When you right-click CALCULATE ScoRECARD, you will see the popup menu shown in Figure 43 0 ? 0 X

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47

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Figure 43 Scorecard Schedule Popup Menu

Select DAILY for the frequency from the RUN OBJECT dropdown menu, and click SCHEDULE (Figure 44) to establish the schedule and the recurring task. Both the recurring and executing task are shown as a result (Figure 45). schedule: calculate SCx>recard Hide Novigotion

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Fi gure 45 Calculate Scorecard Task in Recurring and Running Form

This completes the development of the scorecard with Data Insight. In the next section, we will look at organizing metadata with the Metadata Management module.

3

Metadata Management

Metadata Management is a powerful tool that allows organizations to

bring insight and governance to an enterprise, but unlike Data Insight, it is not development-centric. Metadata Management brings transparency to the relative path of data for an organization and helps organizations untangle their respective webs of data to support information governance for an enterprise. This module is built to compile information about the various systems in an enterprise and expose the interrelationships among them. In this section, we will looks at how Metadata Management collects data and exposes relationships with other SAP systems. We'll look at two specific examples: connecting with SAP HANA and SAP BusinessObjects Business Intelligence (BI). From there, we will set up and run the metadata collections tasks for these two connections. Finally, we will examine the results of this collection using SAP Information Steward.

49

I

3

Metadata Management

I

3

Connecting to SAP Systems

3.1

Metadata Management capitalizes on the development work performed on other platforms to depict the relationships of different development environments on one platform. This shows what can happen if a development change is made without oversight and how that change may impact other platforms and development teams. For example, Figure 46 shows how Metadata Management collects the relative metadata of an SAP HANA system. -----------------------------------------------------------------------SAP

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Fi gure 46 SAP HANA Metadata Collection in SAP Information Steward

Alone, this information is powerful , presenting an organized catalog of available SAP HANA objects. However, this is only a portion of the story. Let's look at another example of this module collecting metadata relationships from a reporting system such as SAP BusinessObjects Business

50

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Intelligence (SAP BusinessObjects BI), which consumes data from SAP HANA (see Figure 47). SAP

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Again, this is useful information, but only a piece of the story. A user can access some of this information inside SAP BusinessObjects BI , but the real value here is the ability to consider such systems together with Metadata Management. Seeing the impact of which reporting objects in SAP BusinessObj ects BI will be affected by a change to a field in SAP HANA empowers the development teams of both platforms to see what will break before any break occurs. This insight will help teams make better decisions with access to real data.

51

•

Metadata Management

I

3

Consider the consequences of not knowing the impact of changes to a source field such as Di sc oun ta mount in SAP HANA and the effects it could have on both the SAP BusinessObjects BI universe and report definitions of fields stemming from SAP HANA objects. Even before SAP BusinessObjects BI is affected, you want to see the SAP HANA impact as well, as shown in Figure 48 for the Di sc ou nt amoun t field. X

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It is apparent why this type of information provides a great deal of value to an organization. It is vital to note that SAP HANA and SAP BusinessObjects BI are not the only systems from which data can be collected via Metadata Management. Table 2 presents a full list of systems supported for metadata collection.

52

Metadata Management

Data Source

Version

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Table 2 Supported Systems for SAP Information Steward Metadata Collection

A number of organizations use SAP HANA together with SAP BusinessObjects BI for a fully capable enterprise-reporting solution. This by no means is to say that your organization should stop or pause using at this solution. Ideally, you would capture metadata lineage and impact information from any of the systems listed in Table 2. In this section, we will explain how to configure connections to both SAP HANA and SAP BusinessObjects 81. Connection to SAP HANA

Connections to external applications and systems from SAP Information Steward are made through the SAP BusinessObj ects BI platform that

53

I

3

Metadata Management

I

3

underlies SAP Information Steward. Most organizations roll out SAP Information Platform Services (IPS) for the BI platform component per SAP best practices. In IPS, many administration tasks are performed, such as security, scheduling, and connections to external systems. To configure a connection to SAP HANA for metadata collection in SAP Information Steward , you must first login to CMC on the IPS system. You can log on to CMC in a variety of ways. We are using Windows Active Directory (Windows AD) as the login mechanism, but if Windows AD is not configured in your environment, you may need to choose ENTERPRISE under the AUTHENTICATION field and use your enterprise login. Enterprise logins are the out of the box method of communication with SAP Information Steward. Regardless of how you log in to CMC, you will see the CMC home screen. From here, click the INFORMATION STEWARD link. Clicking INFORMATION STEWARD will take you to the SAP Information Steward configuration screen (see Figure 49). Central Management Console

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Here, you need to navigate to INTEGRATOR SoURCE, located under the METADATA MANAGEMENT node. Right-click on INTEGRATOR SOURCE and select NEw · INTEGRATOR SouRCE from the popup menu, as shown in Figure 49. This will bring you to the INTEGRATOR SOURCE PROPERTIES screen shown in Figure 50. Here, you will configure the connection for SAP HANA.

54

Metadata Management

I

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Figure so Integrator Source Properties for SAP HANA Configuration

Configuring this connection is relatively straightforward. Table 3 provides the fields and reference values to use as a guide. Field

Value

INTEGRATOR TYPE

SAP HANA

NAME

Name of your connection in Information St eward

DESCRIPTION

Mean ingful description here if desired

COMPUTER NAME

SAP HANA host

DATABASE PORT NUMBER

SAP HANA port for connection

DATABASE USER

User used for metadata collection

DATABASE PASSWORD

Password

TABLE SCHEMA

Not required; only supply the schema name t o restrict col lection if needed

PACKAGES

Not required; only supply package names to rest rict col lection if needed

Table 3 Reference Values for SAP HANA Integrator Source Connection

Click SAVE to complete the configuration of the SAP HANA system for metadata collection. The metadata integrator source connection in SAP Information Steward for SAP HANA is complete and ready for metadata collection.

55

C¥cell

Metadata Marnagement

I

3

Before you collect metadata from SAP HANA, you need to set up a similar connection to SAP BusinessObjects BI, which we will discuss in the following section. Connection to SAP BusinessObjects 81

Connecting to SAP BusinessObjects BI is similar to connecting to SAP HANA. Therefore, please refer to Connection to SAP HANA for detailed information, but for reference the basic steps are as follows: 1 Log in to CMC.

2. Click INFORMATION STEWARD on the main CMC screen. 3. Click INTEGRATOR SOURCE from the popup menu. After clicking INTEGRATOR SOURCE from the popup menu, select SAP BUSINESS0BJECTS ENTERPRISE in the INTEGRATOR TYPE field. This will bring you to the INTEGRATOR SOURCE PROPERTIES configuration, as shown in Figure 51. ?O X

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Figure 51 Integrator Source Properties for SAP BusinessObjects Enterprise Configuration

•

Metadata Management

On the INTEGRATOR SOURCE PROPERTIES screen shown in Figure 51, use the values shown in Table 4 as guide]ines for configuration. Field

Value

INTEGRATOR TYPE

SAP BusinessObjects Enterprise

NAME

Name of your connection in SAP Informat ion Steward

DESCRIPTION

Meaningful description here if desired

CMS SERVER NAME

SAP BusinessObjects Bl CMS to collect metadata from

USER NAME

User used for metadata collection

PASSWORD

Password

AUTHENTICATION METHOD

Method to use for logon authentication

Bl LAUNCH PAD INTEGRATION USER

Use for Bl Launch Pad authentication; not required

PASSWORD

Not required; password if you use Bl Launch Pad authentication

Table 4 Reference Values for SAP BusinessObjects Bl Integrator Source Connection

Click SAVE to complete the configuration of the SAP BusinessObjects BI system for metadata collection. Now that you have connections in place, you need to set up and run the metadata collection tasks for the SAP BusinessObjects BI connection and the SAP HANA connection to gather the metadata for the SAP Information Steward system. We will diiscuss how to do so in the next section.

3.2

Setting Up and Running Collection Tasks

Setting up and running metadata collection tasks in SAP Information Steward is performed in CMC. Metadata collection tasks and scheduling for both the SAP HANA integrator source and the SAP BusinessObjects BI integrator source work exactly the same way. Therefore, we will use SAP

57

I

3

Metadata Management

I

3

HANA for the example in this section to set up the tasks; the tasks for SAP BusinessObjects BI are the same, except that you will select a different connection for the SAP BusinessObjects BI integrator source. To set up, run, and schedule the collection task for SAP HANA, first log in to CMC, just as you did to set up the connection. Then, click the INFORMATION STEWARD link to take you to the SAP Information Steward home screen, as shown in Figure 52. Central Ma_na,gement Console welcome: don .Ioden I Preferences I Hetl •

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Fi gure 52 Click Schedule on the Popup Menu to Schedule Collection Task

At the SAP Information Steward main screen, click the INTEGRATOR SOURCE link under the M ETADATA MANAGEMENT node (see the left side of the screen shown in Figure 52) and select the integrator source. For this example, select the DFT HANA integrator source. Right-dick the integrator source and select the SCHEDULE option to show the SCHEDULE: DFT HANA screen (see Figure 53). Then, select DAILY under RuN OBJECT on the schedule for collection. The finalized screen is shown in Figure 54. When you are done, click ScHEDULE to implement the changes.

58

Metadata Management

? Cl X

Schedule: OFT HANA Hide NavigatiOn

.

Recurrence

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lltlmber o f retries allOwed: 0 Retry nterval in seconds: 1800

Figure 54 Completed Daily Schedule Screen Ready for Collection

With this step accomplished, and once the schedules run, you will now collect metadata to review in SAP Information Steward for both integrator sources that have produced data!

3.3

Examining Results in SAP Information Steward

Examining the results of the metadata collection in SAP Information Steward is a decidedly non-development activity. When examining the results, each collection type is broken down by objects. For instance,

59

I

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Metadata Management

I

3

with SAP HANA, you have collections of tables, views, schemas, and so on. For SAP BusinessObjects BI, you have universes, reports, connections, and so on. Although this is not a development activity, the user of the system must have enough knowledge of the platforms he/she is exploring to successfully navigate the metadata and relationships collected in SAP Information Steward. This is an important point, and for the casual business user with only functional knowledge of the data, it can be a bit daunting. However, with the proper technical knowledge, the ability to examine results can be incredibly powerful. Consider an example (see Figure 55): table FACT_ INTERNET SA LES in the BOOK_USER schema in SAP HANA. ~ SAP irlorf"'\Zloon Stew
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Fi gure 55 Menu for Navigating Table BOOK_USER.FACT_I NTERNET_SALES

The bread crumbs at the top of Figure 55 show how we navigated to the table DIRECTORY • BUSINESS INTELLIGENCE • DFT HANA (an SAP HANA metadata collection we ran) • SCHEMA$ • BOOK_ USER • TABLES. Here, right-click FACT_INTERNET_SALES to produce the popup menu shown in Figure 55.

60

!.. !..

"

Metadata Management

From the popup menu, select IMPACT to show the relative impact of a change made to the source table FACT_INTERNET_SALES , as shown in Figure 56. X

FACT_IHTERNET_SALES (Rdadonal_Table•

SAME AS

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IMPACT

UNEAOE

USER OEFINEO

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a o

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Figure 56 Impact ofTable FACT_INTERNET_SALES

This impact screen depicts all of the dependent obj ects downstream that will be affected if a change is made to the table. The object to the far left is table FACT _INTERNET_SALES, and the column of objects to its right with the orange icons are analytic views in SAP HANA that use the table. As you move to the third column, many of these objects are aggregations in SAP HANA. The fourth column shows the SAP BusinessObjects BI universe information that depends on one of the analytic views as a data source. Figure 56 highlights a particular field in the universe called CUSTOMER_STATECODE, which will be impacted. Hover over any of the icons shown in the columns in Figure 56 for a summary of details. This powerful information provides a complete picture of what wiill be affected by a change to table FACT_ I NTERN ET_SAL ES. You can also dive into the table to the field level to see the direct, fieldlevel impact. This is the level of granularity that most organizations use to see the finite impacts of development choices. The field-level detail will

61

I

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Metadata Management

I

3

also surface formulas or transformations of data by revealing them in popup windows when transformations occur. This is the case whether you are in SAP HANA or SAP BusinessObjects Bl. If you set up a connection for SAP Data Services and wanted to see the lineage of where the data came from for table FAC T_ I NTERNET_SALE S, you could do so by setting up a connection to SAP Data Services and running a similar collection task for either SAP HANA or SAP BusinessObjects Bl. If this metadata integrator source connection was collected for SAP Data Services, you would see the lineage information from the ETL job that made table FACT_ INTE RNE T_SALE S (see Figure 57). f ACI _INTI::RNET_SALt'S (Rtlatio:Ml_Table) SAME AS

RELATED TO

IMPACT

X LINEAGE

USER DEFINED

Figure 57 Lineage ofTable FACT_INTERNET_SALES

This linage information direcdy from the source system shows exactly where the table data came from and can be examined at the field level as well. With this level of clarity, you can see the following objects from source to final destination: » Source tables » ETL transformations » SAP HANA tables » SAP HANA analytic views » Universe that consumes the analytic view )} Universe field

62

Build an Enterprise Data Dictionary with Metapedia

This completes the technical view of the Metadata Management module. As previously mentioned, this data is quite technical in nature. To take some of the technical edge off, you can use the Metapedia module to create a business-term-driven data dictionary and relate such technical objects to business-centric terms in order to communicate with a wider audience. We will explore this process next.

4

Build an Enterprise Data Dictionary

with Metapedia Complete business terminology is something many organizations struggle to compile. Even with a complete list of terms, companies encounter challenges as to where and how they will store such information. Metapedia allows you to create a compilation of data definitions and terms in a format that can be both governed and shared as a hub to expose them to various systems. Metapedia provides a full history of any term or category changes, and it offers the ability to differentiate who can make changes and how. Finally, Metapedia provides a secure platform to guard against any loss of information via deletion. As you can see, there are numerom reasons that Metapedia is an ideal option to manage the term and category creation process. In this section, we will now explore how to create terms and categories within Metapedia.

4.1

Setting Up Terms

The first step of creating content in SAP Information Steward is to set up terms. You can see an overview of the Metapedia module in Figure 58; this is where you will create the terms.

I

4

Build an Enterprise Data Dictionary with Metaped ia

C'

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Fi gure 58 General Metapedia View of Terms in SAP Information Steward

In the sections that follow, you will learn how to create, approve, and associate terms and how to show term activity history. Create Terms

To create both terms and categories in the Metapedia module, you use the GUI. Unlike the Data Insight module, there is no coding or special knowl-

edge required here. To begin, dick the NEW button (see Figure 59).

-

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Figure 59 Click New to Create a New Metapedia Term

The METAPEDIA TERM dialog box will open (see Figure 60). Here, you will enter all of the term's details.

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4


Propcrtiol!s

Custom Att:ributH Status:

Hew

Description • Data cap~ and post!ontd aret.nd corporate rntemtt sales. Data h this table ccnsists of

menses tM; ¥t uptu'ed dally .Y'Id related ~o items sdd by o.astetners bv day. Internet sales....,. be fromal arus of the~ ard not JUSt rmt~ to fqlh A.tnerica.J

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Safes, h ternet

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Figure 6o Metapedia Term Dialog Box to Edit and Create Terms

In the PROPERTIES tab, enter the term name in the NAME field. Use a functional name that is not technical and that will make sense to an end user. The purpose of a Metapedia term is to provide a business context and description for technical content, therefore a Metapedia term must be business-centric. After you enter a term name (in our example, the term is called "Internet Sales Data") , add a description for the term and an APPROVER (see Figure 60). This information forms the business description of the Metapedia term, with as many details as necessary to fully qualify the term from a business perspective. The approver concept is the first step of the approval workflow that we will discuss in the next section. After you fin ish entering the term details, click the SAVE button, and the term will be created. Approve Terms

The next step in the term workflow process is to approve the term for use in SAP Information Steward. To do so, click the SUBMIT FOR APPROVAL button.

J

4


A popup screen will open so that you can submit your comments and specify a due date for the approval (see Figure 61). Subm1t for Approval · Comments

Due Date:

9{9/ 20 15

liiil

Comments:

1

Please approve my term for Internet Sales Data.

OK

Concel

Figure 61 Submit for Approval Popup Screen to Enter Comments

To complete this step, click OK. After completion, the Metapedia administrator will approve the term if it is appropriate, and your term then can be associated with other metadata that has been collected in SAP Information Steward. It is important that the term is approved; otherwise you will not be able to associate the term. We will step through the process of associating an approved term in the next section. Associate Terms

To associate a term in Metapedia, you "match" the term to a metadata object that has been collected in Metadata Management. Much like creating the term and approving the term, there is no coding required when associating a term. This process is performed at a high level by finding the metadata object you wish to associate and using the Metapedia GUI to associate the Metapedia term with the object. Use the same M ETAPEDIA TERM screen that you used to create the term and submit the term for approval. To associate your term, open the ACTIONS dropdown menu and select ASSOCIATE WITH OBJECTS (see Figure 62). After selecting ASSOCIATE WITH OBJECTS, you will see the ASSOCIATE OBJECTS (INTERNET SALES DATA) screen appear (see Figure 63).

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Figure 63 Associate Objects Screen in Metapedia

Use the AssociATE OBJECTS screen to search for metadata objects that you have collected in SAP Information Steward (as described in Section 2). Continuing with the Internet Sales Data term example, find all of the "fact Internet sales" related tables. The results will tell you that the following tables relate to Internet sales:

--


I

4

» FACT_SALES (from the context of the SQL Server database connection) » FACT_SAL ES (from the context of the data services development repository; DS_RA_DEMO is the source object in this case)

» FACT_SALES (from the context of the data services development repository; DS_ RA_ DEMO is the target object in this case) As shown in Figure 63, select all returned entries and click AssociATE WITH TERM to perform the association. You have now provided a business context for the raw metadata by implementing a relationship in Metapedia. Figure 64 shows the resulting relationship information. )(

HdaPt:diahrm

Properties

Lastmoclfltdby Ac:triiistrat« on 9/9/2015 l0:37:SSAM

Custom Attrib\ltes Status:

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De$c:ripli0n • 06t6eap~ed-positiontdarou:ldCIOI'POI'ate internet sales. Data in

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Admiristrator

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Figure 64 Resulting Relationship Information in the Metapedia Term Screen

This completes the association process. As you might expect, there is quite a great deal of churn that can occur in this process, so it may be helpful to view the workflow history captured in the tool. We will discuss how to do so in the next section.

68

Build an Ent erprise Data Dictionary with Metapedia

Show History of Term Activity

Business activities often evolve and change, which can affect an organization's terms list. Terms often mean different things to different groups within an organization. For example, a "product sale" might be very different for a merchandizing group than it is for an operational controller. For such reasons, there will be a reasonable amount of churn (activity) in the creation and maintenance of terms in Metapedia. Therefore, it is important to see the history of a term's evolution in order to put changes into context both from date and approval perspectives. Fortunately, SAP Information Steward tracks all of changes to an evolving term. Figu re 65 shows the history detail of our Internet Sales Data term. Intemet Sales Data - History

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9/9/ 2015 10:37 AM

Internet Sales Data

Editing

9/9/2015 !0:34AM

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Corrments

9/9/ 2015 10:37 AM

Close

Fi gure 65 Internet Sales Data Term History in Metapedia

This detailed history is a useful tool and includes the following information: » Term origin » Who approved the term » When the term was approved » When the term was updated » Rich comments at each level » Term current status In addition, the term history tool displays history of all terms created and maintained, which safeguards the workflow process and the history of what occurred at the term level.

I

4


Now that you have set up your terms, next you need to set up categories to organize and catalog terms into groupings.

4.2

Setting Up Categories

Terms grouped together in Metapedia are called categories. Categories are quite flexible, but they have one drawback: There is only one level of hierarchy within a category. For instance, you can have a Customer category and place customer terms (such as Customer First Name and Customer Last Name) within it. However, you cannot have a secondary level of grouping under the Customer category. You may wish to segregate customer address data from customer name data, but you can only do so with separate categories currently (as of SAP Information Steward 4.2). You cannot have a Customer category with subcategories of Customer Address and Person. Therefore, you must be specific and deliberate when choosing category assignments in Metapedia. In this section, we will look at creating and associating categories in Metapedia. Create Categories

To create a category in Metapedia, click NEw CATEGORY, as shown in Figure 66.

Figure 66 Create a Category in Metapedia

Next, the METAPEDIA CATEGORYscreen will appear, as shown in Figure 67.

70

J

4


4

J

Here, you will enter the category name, category author, category description, and any keywords you wish to use to search for this category later. All of these configurations and field entries are shown in Figure 67. Httapedia Category Properties

X

Custom Atlributes

Fi gure 67 Metapedia Category Screen

Associate Categories

To use the created category with a Metapedia term, open the Internet Sales Data term created in Create Terms and click ACTIONS. Instead of associating the Internet Sales Data term with a metadata object as you did previously, you will now add the term to the sales category that you just created. Therefore, choose ADD TO CATEGORY from the ACTIONS d ropdown (see Figure 68). X

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The ADD TO

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4

popup screen will open (see Figure 69).

Add to Category

X

- ct a category to assodate -""'the term(s)

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Figure 69 Add the Metapedia Term to t he Sales Category

This screen currently only shows one category. If there were more categories, they would show up as part of a potentially lengthy list in this dialog box. It is best practice to consider your category naming conventions carefully to avoid unwieldy lists. After you select the appropriate checkboxes for the category or categories you wish to associate, click OK to complete the process. The completed Metapedia term is shown in Figure 70. -

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Figure 70 Finished Internet Sales Data Term in M etapedia

4-3

Exposing Metapedia Content to Other Systems

After you have created content in Metapedia, you can expose this information to other environments. In this way, all terms and categories created can be leveraged by other applications. This process is accomplished through SAP Information Steward hosted web services, which can be deployed and configured in the SAP Information Steward system. This process requires additional configuration in SAP Information Steward,

72

Case Study

which we will not cover in detail here. The purpose of this section is to discuss the option to deploy such services, and why doing so can be desirable for end users. After utilizing SAP Information Steward's web services, the workflow to share a data dictionary is as follows: 1. Metapedia terms and categories are created, maintained, and governed in SAP Information Steward. 2. The SAP Information Steward workflow controls rights, processes, updates, and activities. 3. External applications use SAP Information Steward deployed web services to query the repository of Metapedia terms and categories. In this way, SAP Information Steward becomes the system of record for business glossary terms, and other applications merely consume the content. Next, the final section in this E-Bite will round out what you have learned thus far with a comprehensive case study using all three SAP Information Steward modules.

5

Case

Study

This section will take the knowledge we have gathered thus far about the different SAP Information Steward modules and apply it to a real-world scenario. In this case study, we will examine the AdventureWorks Cycle Company, which has recently implemented a new BI platform based on SAP HANA, SAP Data Services, and SAP BusinessObjects BI. Through SAP Data Services, the company's BI resources were able to successfully extract, translate, and load the sU!pporting Internet sales dimensions and fact tables into an SAP HANA schema using a batch approach. AdventureWorks will use SAP Information Steward to manage and govern master

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data by first building content in the Data Insight module and then extracting metadata through Metadata Management. Finally, the company will create a data dictionary with Metapedia.

5.1

Building Content in Data Insight

AdventureWorks will use Data Insight to mine the customer master data for potential rules and to perform some discovery on the customer master data by using the Data Cleansing Advisor (DCA) functionality. Using the DCA tool, the AdventureWorks staff can see what they need to measure and examine some sample rules that can be implemented with SAP Information Steward. Syst em The examples in this section use a Microsoft SQL Server CRM system. Many SAP customers wou ld connect directly to SAP, as we discussed previously, but in this example, AdventureWorks uses SQL Server to host its in -house CRM system. Therefore, we will use SQL Server as our connection for customer data profiling.

Import Tables and Build a View

The first task for AdventureWorks in SAP Information Steward is to import tables into Data Insight. To do so, browse to the import screen shown in Figure 71 and select the tables you wish to import. Next, the AdventureWorks staff need to examine contact address information in the CRM system for a closer look at any data quality issues. They select the PERSON.ADDRESS, PERSON.CONTACT, and PERSON.STATEPROVINCE fields in order to gather tables to construct a complex view (see Figure 71). Then, they import the SALES.CUSTOMER, SALES .CUSTOMERADDRESS, and SALES.INDIVIDUAL fields in the same manner until they have all of the table metadata definitions needed to support the CRM view. The completed tables are shown in Figure 72.

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Case St udy

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llil StateProvlnce

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Street address Information few customers, en'C)Ioyees, and vendors. Types r:l addresses stored in the Address table.

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Figure 71 Import Tables into SAP Information Steward

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Figure 72 Imported Tables in Advent ureWorks' Data Insight Connect ion

Now they begin to build a view. AdventureWorks staff use the TOR

VIEW EDI-

to construct the V_ADVWRKS_CUSTOMER_ADDRESS_UI view, as shown in

Figure 73.

75

5

Case

Study

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Fi gure 73 Build View in Data Insight to Prepare for Profiling

The tables for the view are found under SOURCES on the left sidle of the screen shown in Figure 73, and they are joined in the VIEW EDITOR. The Adventure Works staff have selected the output columns under COLUMN NAME on the right-hand table shown in Figure 73. There is now a dataset to examine in Data Insight. The next step is to profile the data for more information on the rules that should be enacted. Profile View

Profiling data in Data Insight is an easy task that provides great visibility for data quality challenges and exposes rules that the AdventureWorks staff should consider based on the data profile. To start this process, click V_ADVWRKS_CUSTOMER_ADDRESS_UI (the view built in the section Import Tables and Build a View) to select it. Then, click PROFILE to expose the dropdown list. From the dropdown list, select COLUMNS to set up the column profile task, and then select CONTENT TYPE to expose the completed profile tasks shown in Figure 74.

Case Study

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The profiling tasks are now complete, and SAP Information Steward shows results for the columns (see Figure 75).

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As shown in Figure 75, there is a great deal of information exposed in this window. There are metrics concerning NULL values, patterns of data, and completeness of entries. All blue icons are clickable, to provide sample

77

'i

Case Study

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5

data for the end user. From here, you can click the gear icon highlighted in Figure 75. Doing so allows you to examine the suggested validation rules for data metrics. We will examine these rules in the next section. Examine Suggested Rules

After you click the gear icon shown in Figure 75, you will see the screen shown in Figure 76. This is the DATA VALIDATION ADVISOR, in which you can examine rules the tool suggests . - ..... 1 Data validation Advisor

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Figure 76 Suggested Rules Based on Column Profiling Data

After examining the DATA VALIDATION ADVISOR a little closer, you will see validation rules that can be accepted or rejected in the AcTIONS column at the top right of Figure 76. If you select the rule for PosTAL CODE, you will see that the distribution of postal codes is wrong for AdventureWorks' business requirements. AdventureWorks does business by shipping the majority of its products. Therefore, zip codes should be able to be entered as nine-digit codes rather than only as five-digit codes. Here, you want to accept the validation rule. Click YES to see the rule in SAP Information Steward (see Figure 77).

Case Study

I

5

Rule Editor

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Figure 77 Ru le Editor

Note that this is exactly the same RULE EDITOR screen as used in Section 2 for creating rules to support scorecard development. The DCA process to generate these rules is much faster when the rule is generated to specified requirements. Using this feature will allow Adventure Works to jumpstart the rules monitoring process. The company can still take the rule and use it in a scorecard for trending and any other purposes. They are still rules like any other SAP Information Steward rules. This process is just a faster way to get to the developed rule. At this point, AdventureWorks needs to see a greater level of detail for both potential data cleansing and matching opportunities. To do so, the company will use DCA on the table. Data Cleansing Advisor

To start DCA, click the broom icon shown in Figure 78.

79

Close

Case Study

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Figure 78 Broom Icon Starts Data Cleansing Advisor

The DCA Wizard will open, as shown in Figure 79. X

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Specify Data Cleansing Solution Oetaitls

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select Coi.Jmns

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Set uP Matct't"JQ

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Fi gure 79 DCA W izard Step 1: Name the Solution

8o

I

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Case Study

The AdventureWorks staff can now begin to create the data cleansing solution. After providing a name and business description, click NEXT to get to the screen shown in Figure 80. X


1

Name the SOlution

Select Relevant Data Quality Columns

Include columns that are relevant to cleansino and matctw'IQ in this data cleansing soUion. You c.v. Rlxle cohsms representinQ ooe person~ one address, ooe fi m, and other data.

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Fi gure So DCA W izard Step 2: Select Columns for Cleansing

Next, select columns for cleansing. Include the fi rst and last names, because you need to see how bad the relative qualities of the name data fields are within the CRM system. However, you are primarily concerned with the address fields, which you know are bad based on the basic column profiling and rule suggestions in the previous sections. Click NEXT to move to the third screen (see Figure 81). This is screen is important from a matching perspective, because it determines what you will be matching on. Because you are focusing on deansing the person and address columns , try to see if there are person and address duplicates using the matching functionality within DCA; select the PERSON AND ADDRESS checkbox. Click NEXT to move to the final configuration screen (Figure 82).

81

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Case St udy

X


1

Name the SOlutiOn

2

Select Cwmns

4

Set uP Tr;nsformatms

Select Match Theme and Rules

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Figure 81 DCA Wizard Step 3: Set Up M atching


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Name the SOlution

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Select C
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If your d.Ma needs simple text de~, such as ~rch and rcpl8ce, Of removino SJ)8ces or QUOtation marks., add transformations below. This step is optional.

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Figure 82 DCA W izard Step 4: Set Up Transformati ons

Here, the AdventureWorks staff has the option to transform the data if required in the data cleansing solution. We typically recommend not doing this, and instead taking the output given with the tool, because transformations are very limited and can be problematic when using data from w ithin SAP Information Steward. This is especially important when you are going to use the DCA output to generate code for SAP Data Services. For these reasons, AdventureWorks staff choose to keep the options set to their defaults and simple. After this screen, click FINISH to

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close out the DCA Wizard and run the solution. The process completes the execution, and you will see the high level output shown in Figure 83. SAP " SAP InformabonStE-A.Yd

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Figure 83 High Level DCA Resu lts

A more detailed representation can be achieved by clicking on the DATA CLEANSING tab (broom icon), as shown in Figure 84. Looking at Figure 83, you immediately can see that duplicates are not an issue by examining the graph on the right side of the screen; the tool does not detect any duplicates. Therefore, this is not something AdventureWorks needs to deal with for matching purposes. However, the cleansing results are more interesting and would lead AdventureWorks to look at the details in Figure 84, which show that almost all of the postal codes were corrected from the rule suggestion. The details also illustrate a great deal of problematic street data. This result necessitates further investigation of the data before and after address correction (see Figure 85).

LooOff

Case Study

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"

Case Study

This valuable view allows you to see exactly what the tool did to the data. In this example, we can see that the "W" for "West" was added to the street address to make it correct for postal delivery.

5.2

Extracting Metadata with Metadata Management

After using the Data Insight module, AdventureWorks has greater visibility into predictive data quality monitoring. The next step to support information governance will be to extract metadata using Metadata Management. We will now discuss a high-level approach for this step. At AdventureWorks , IT uses SAP BusinessObjects BI for reporting against CRM data. Using the Metadata Management module, AdventureWorks can gain greater insight into all of the relationships of the BI reports and into the lineage of where the data is sourced from CRM. To accomplish this, the AdventureWorks staff will essentially perform all of the steps described in Section 3 of this E-Bite. First, they will establish a connection to the SAP BusinessObject BI system in SAP Information Steward. Then, they will run a metadata collection task to collect all of the metadata on the SAP Information Steward server. The AdventureWorks analysts will use the resulting information compiled from the SAP BusinessObject BI system to answer questions such as the following: » Where did the customer name data come from in the customer sales report? » Was the customer name data transformed in any way? » How many reports in SAP BusinessObjects BI use the customer name field? These are simple questions, but they offer insight into completely different aspects of the CRM data on top of the powerful information already compiled by the Data Insight module. Next, to complete our governance story, we will set up a data dictionary in Metapedia.

I

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Case Study

5.3

Creating a Data Dictionary with Metapedia

In the previous section, Metadata Management rounded out the insight to ensure that you know where data is coming from and how it is being transformed. Now, it is time to bring business relevance to technical items by creating a data dictionary in Metapedia. The AdventureWorks governance team members need to quickly provide business relevance for the technical term names they have defined. Luckily, AdventureWorks currently has a data dictionary maintained in Excel, so team members do not have to key in individual terms. This provides a much faster route for getting started, as there is no need for approvals. In the data dictionary, AdventureWorks has terms that have been approved and are used currently against production data. In this case, it is much better to perform a mass upload that includes all of the Metapedia terms. Then, the company can begin to govern additional terms or edits to terms through the Metapedia GUI one record at a time, as discussed in Section 4. To see this import in action, first log into Metapedia and click the button (see Figure 86).

IMPORT

Fi gure 86 Import Data Dictionary File from Microsoft Excel

Then, browse to and select the sample import XLS file (see Figure 87).

86

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5

Case Study

I

5

Met apedia Import

II

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X

Import XLS file path: Metapedia_20 13100ZT 131207.xls

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Term Mapping Import Options:

Citegory Mapping

~ ~rt categories

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Ovem
IIJ

Deta.Jit Author:

Adrriristra tor

Default Approver:

Metodoto Ma - t Dato Steward •

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Automaticoly approve ~ted terms.

Import Excel Sche ma:

Information Steward Format •

Figure 87 Import Process for M etapedia: Automatic Term Creation

Finally, review the information imported into Metapedia in the application (see Figure 88). ----------------------------------------------------------------------------------"'"*"-' ......._....,.

SAP.,. SAP lnlofmrtton Strward

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Figure 88 Completed Terms Import

This concludes our SAP Information Steward journey. This last section rounded out our discussion of the three modules in a comprehensive case study. You should now be able to move forward and monitor your data in real time with SAP Information Steward.

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6

What's Next?

So you've monitored, cleaned, validated, and defined your data using SAP Information Steward's three pr imary modules. Now what? SAP Information Steward is just a piece of the Enterprise Information Management (ElM) puzzle. Therefore, it's important to know all the tools at your disposal to ensure optimal data quality. From bigger solutions like SAP Data Services to tools such as SAP Data Quality Management .and SAP Master Data Governance, ElM provides a plethora of functionality to manage the entire lifecycle of your data. Recommendation from Our Editors Data quality and monit oring is a critical business task in an w-r::;:,v.M~ enterprise. Study up on t he latest tools of t he t rade: Learn ~--..--_, more about using ElM with SAP and further information on

SAP Information Steward by visiting www.sap-press.com/ 3666 and checking out the SAP PRESS book, Enterprise Information Management with SAP!

In addition to this book, our editors picked a few other SAP PRESS publications that you might also be interested in. Check out the next page to learn more!

88

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