Data Governance using SAP MDM Part 2

Transcription

1 Data Governance using SAP MDM Part 2 Applies to: SAP MDM Summary Part 2 of the Data Governance using SAP MDM series elaborates on SAP MDM can be used to model master data administration, data quality and streamline business processes. Author: Ketan Phanse Company: Wipro Technologies Created on: 05 June 2008 Author Bio Ketan has been associated with Wipro Technologies and SAP MDM for more than a year and has completed his Master's in IT and MBA in Systems Management SAP AG 1

2 Table of Contents 1. Recap Data Administration Roles, Responsibilities & Users Change Tracking Data Quality Validations Assignments Matching & Merging Enrichment Policies & Procedures Workflows Summary...11 Related Content...12 Disclaimer and Liability Notice SAP AG 2

3 1. Recap The part-1 of the series Data Governance using SAP MDM discussed the different definitions of Data Governance and how we can model Data Governance using SAP MDM. The four layers of the pyramid of SAP MDM Data Governance model are shown below. Part 1 showed how high quality data standards can be achieved by modeling your MDM repository to best fit the required standards. Setting appropriate properties for different fields and using the different tables offered by SAP MDM, we enforce required criteria s which can help achieve the standards. This article will focus on the remaining three layers and how each of these can be achieved by SAP MDM SAP AG 3

4 2. Data Administration Who are people who will access the data? Who are responsible for creation, modification and deletion of records? What sensitive data needs to be tracked? These are some of the basic questions one needs to answer when creating a Data Governance framework. Data ownership and stewardship is key for successful Data Governance. While creating and managing master data, organizations do have roles such as Local Data Steward, Global Data Steward, Master Data Steward each of whom is responsible for maintaining a clean master data. SAP MDM allows imposing this using a User-Role relationship and gives numerous options to set up the responsibilities, functions and activities each role can perform Roles, Responsibilities & Users The first step in putting place a data administration model is to create Roles. These Roles can be for data managers like LDS, MDS and also for other user group like Marketing Sales etc. Once a Role is created, to set its responsibilities and security parameters, we can set different Functions for each role, example: Add records, Modify workflows, Import new records etc SAP AG 4

5 Also SAP MDM provides functionality to set security permission on each field and table level for a particular Role. These Roles can be assigned to multiple users thereby applying the above setting to actual people using it Change Tracking Audit reports and compliance also form an integral part of Data Administration and Governance. It is important to trace changes to key fields which can later be used for reporting purpose. SAP MDM provides a feature of Change Tracking where we can set the trace on fields for addition, modification and deletion. Besides Change Tracking, one can use the Stamp fields to trace changes which include Create Stamp, Time Stamp and User Stamp. Using combination of Roles, Users and applying proper authorizations one can develop a systematic data administration framework which would also enforce data ownership SAP AG 5

6 3. Data Quality Having clean, consistent and non-redundant data in your master data repository is of utmost importance. As the MDM system would act as your single source of contact for your master data having erroneous, inaccurate data will have a ripple effect throughout your IT landscape. In fact, one of the main objectives of MDM is to achieve high Data Quality standards. Data Quality is not a single activity or a separate entity, it is an ongoing process. There are multitude of data quality specialized tools available in the market, but it would add one more software to your complex landscape. SAP MDM provides effective data quality management using functionalities which are very closely aligned to your business. With a combination of Validations, Assignments and Matching Merging strategies we can develop strong rules to ensure high data quality. Assignments Validations Matching DATA QUALITY Enrichment Merging 2008 SAP AG 6

7 3.1. Validations Using Validations, rules and restrictions can be imposed on various fields. One can write complex expressions on different business scenarios in Data Manager. These validations can then be called from workflows to optimize data quality Assignments Similar to Validations, SAP MDM provides another feature called as Assignment which is a strong tool to maintain data quality. Using Assignments you can change/assign the values of fields based on certain business criteria s. The above example shows how a business requirement of setting the Material Type based on Minimum Remaining Shelf Life can be modeled in SAP MDM. Assignments & Validations can be used is tandem to design data integrity and quality standards SAP AG 7

8 3.3. Matching & Merging Apart from checking for data rules, another important aspect of data quality is de-duplication and removing redundant and erroneous data. De-duplication can be used at various stages of MDM, during data consolidation to check for duplicates amongst different remote systems, during Central Data Management for checking for duplicates during creation. SAP provides a separate Matching Mode for this very purpose. One can define Transformations to remove junk data, define Rules to assign scores and Strategies to merge multiple Rules. When a strategy is run, SAP MDM generates a report as shown below. This report can be used as a statistical tool to analyze the duplicates and generate reports. Converting business requirements into Validations and Assignments and using intelligent Matching and Merging strategies, one can achieve high levels of Data Quality standards SAP AG 8

9 3.4. Enrichment External enrichment services are provided either remotely by a service provider or by locally installed software. They typically offer very specialized and sophisticated enrichment functionality for certain kinds of master data. Consider the example of enriching customer address data. We have third party service providers like Dun & Bradstreet (DnB) which provide industry standard, accurate information on global entities. Given a small set of information like DUNS number, Name, City, Country etc DnB replies with a plethora of information like the Worldbase Package containing the hierarchy of organization, its specific details like , phone, web site and also the address details. Using SAP MDM and the Enrichment Controller provided by SAP, we can send the request as a web service to DnB and then enrich our repository records with information returned by DnB. This increases the data quality enormously! Errors such as typos, spelling, incomplete information etc do not take place as the data provided by DnB is standard and accurate. Similarly other web services can be invoked from SAP MDM like VAT number, Latitude& Longitude etc. Use of such enrichment architecture can ensure high quality master data SAP AG 9

10 4. Policies & Procedures Business and IT must go hand-in-hand for success of any MDM project. Implementing MDM can sometime lead to change in way people are used to doing things. Their roles, functions change, and so does the overall process. Business process optimization, compliance adherence, process streamlining etc are other underlying needs for Data Governance. MDM involves consolidation and communication of multiple master data remote systems and hence as a result new processes must be designed in such a way that they do not completely disrupt the existing processes. The MDM team must have joint sessions with IT as well as Business teams to clearly articulate the benefits of new process. To design and model workflows SAP has provided MS Visio as a designing tool and provides with multiple stencil options which talk with the MDM repository Workflows Using MS Visio, one can create dynamic workflows which cater to business requirements. Visio being a very user-friendly tool, modeling of complex scenarios can be done using simple drag-drop options. Each step in Visio is closely integrated with MDM. All the above governance points like Roles & Users, Validations, Assignments, Matching strategy etc can be used here. This integrates all the layers of the pyramid into one. The above MDM workflow shows a typical business scenario where a new request for creating a master data record is initiated like creating a new material. The request then follows a systematic path to different departments, Review of data and Notifications before being created in MDM. At each stage, data standards & administrative rights are maintained by allocating appropriate Roles & Users to each step. Data Quality is maintained by calling the Validations & de-duplication strategies. Also data ownership is brought by sending regular notifications to concerned user group SAP AG 10

11 5. Summary As I mentioned in the beginning, The Data Won t Govern itself!! Three basic principles for structuring a governance function are: 1. Governance is more than standards, reporting and prioritization of projects 2. A strong governance organization ties business and technology into a tightly integrated operating entity manages and integrates ongoing investments in technology and data increases the speed of organizational response by driving accountability and decisionmaking into the organization 3. Governance is not static; it must evolve over time The true function of governance is to actively link integrated business and technology teams with corporate and strategic initiatives. Within this context, governance becomes an integral part of enterprise line management. Executed properly, the governance function can actively and effectively reallocate business, technology, reporting and analytic resources to align with rapidly changing market demands. SAP MDM supports most of the Data Governance principles, but it is more important for organizations to realize the significance of having a strong data governance framework along with their MDM initiatives. Substantial effort and time must be put in analyzing, designing and building a data governance framework. Also, assessing the functionalities of SAP MDM and how it would best fit the organizations requirement would also be helpful. One of the key objectives of this article was to showcase SAP MDM s capabilities for data governance and how it can be actually implemented from a business as well as technical perspective. Hope you can get some takeaways and develop better stronger governance framework SAP AG 11

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13 Disclaimer and Liability Notice This document may discuss sample coding or other information that does not include SAP official interfaces and therefore is not supported by SAP. Changes made based on this information are not supported and can be overwritten during an upgrade. SAP will not be held liable for any damages caused by using or misusing the information, code or methods suggested in this document, and anyone using these methods does so at his/her own risk. SAP offers no guarantees and assumes no responsibility or liability of any type with respect to the content of this technical article or code sample, including any liability resulting from incompatibility between the content within this document and the materials and services offered by SAP. You agree that you will not hold, or seek to hold, SAP responsible or liable with respect to the content of this document SAP AG 13