Master Data Governance And Master Data Management Are Not The Same
However, your MDM can be an important tool to support your data governance strategy as it provides useful methods for data collection, classification, modeling, quality control, clerical review by data stewards as well as linking-and-merging capabilities for automated de-duplication.
As the diagram above shows, by connecting applications and unifying data, the MDM that harbors the customer data hub, effectively breaks down siloed data management, which is the single most important impediment for achieving data transparency and accountability.
Who Should Be Involved In Your Mdm Program
Now that you understand the what and why, lets talk about the who and really, there are a several different ways to think about who to involve in an MDM program. First, lets take a high-level look at three core roles:
Other MDM roles can include and vary by organization/project type:
Data Volumes And Complexity Set To Increase
The growing connectivity of devices in the Internet of Things provides a continuous stream of data that must be processed in the shortest time possible. Estimates suggest that a whopping 175 zettabytes of data will be available worldwide by 2025, which will only add to the complexity of data management.
Greater connectivity and complexity also means that even minor errors in master data can trigger unexpected chain reactions. Incorrect data therefore poses an even greater risk for companies.
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Master Data Governance Needs Definitions
Master data management cant work without governance. But to take one step further back, you cant implement a data governance framework without definitions.
Data collection, classification, and quality control must be applied before implementation of a data governance framework. You need to have clear definitions of things like acquisition and accessibility in order to govern data and these are essentials of MDM.
In that sense, your master data management and your data governance framework are mutually dependent.
What Is Master Data
Definition: Master data is reference data that is shared or exchanged across strategic applications, work processes, lines of business, or enterprises.
My definition has been personally field tested for thebetter part of 20 years and has served a number of Fortune 500 organizations very well. For me, the focus is on the sharing and leveraging of the reference data across the various organizational boundaries that master data can and must cross.
The definition is based on the goals and objectives my former employer was trying to achieve back in the late 80s ERP consolidation, moving from a decentralized, LOB/regional multi-national company to a global, top-down, centralized management, standardizing applications,infrastructure, business processes, roles and responsibilities, and reporting. To achieve that, we needed common reference data across our solution landscape. Does that sound familiar to you?
We were reducing the number of ERP systems, moving to four regional instances, based on SAP software . This required a broader view of master data than most are willing to adopt today. We wanted to ensure that our four SAP instances were configured identically. It was the start of our implementation of enterprise data governance, data standards, and standardized master data content. We knew we couldnt achieve one, without the other.
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Design A Data Governance Organization
This organization must be suitable for managing the standards you have defined. This includes the roles and responsibilities for those governing data, the internal governance processes that will be used to manage activities , and changes to any external process that affect the organizations ability to govern .
Determine The Current Status
To begin with, the status quo of data management in the company must be mapped. You should ask questions like: Who is the owner of the master data? Who manages the master data? And who has access rights? When it comes to material master data, the process of creating and maintaining data records must be examined with particular care.
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Data Maintenance Needs To Be Governed Too
Is data governance the same as data maintenance? The two are very closely linked through data quality but they are independent functions. Maintenance organizations tend to be aligned with specific IT systems or with specific lines of business within your organization whereas data governance is about a common set of rules that everyone should adhere to. The key to understanding this dichotomy is to understand the two parties relationship to standards.
As part of master data governance, you need to define a set of best practices or principles that will ensure that you create and maintain good quality in your data. Define these as your standards. It is the role of the data maintenance teams to comply with these standards, but it is the role of data governance to define the standards and to ensure that they are being met.
What Is Data Ownership
Data ownership is a very confusing term. For example, it is common in businesses to split data responsibilities geographically the UK sales force manage all customers and their data in the UK region whereas the US team take responsibility for those in the States. But then again, we are proposing a single data governance organization that is responsible for the data, and to add to the confusion, in many such governance organizations we see a role of data owner.
But actually, the data governance organizations role name data owner is a misnomer because in practice what they own is not the data, but the standards that guide the users in how to achieve good quality. So, while many departments may lay claim to the contents of the data it is the data governance organization that owns the structures and the quality rules.
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These Nine Key Components Of Sap Mdg Help Ensure Regulatory Legal Environmental And Financial Compliance Of Your Master Data And Improve Operational Efficiency
- Jawad Akhtar,Logistics and SCM expert
Master data is the central nervous system of an ERP system, yet its importance is not always understood, even when an organization makes a concerted effort to cleanse, correct and consolidate its data through an enterprise information management initiative. Companies struggling to harmonize and centrally maintain the master data streaming in from disparate systems can implement SAP Master Data Governance, an add-on tool available for SAP ERP Central Component, Business Suite on HANA and the next-generation ERP, S/4HANA.
Let’s take a look at nine key components of SAP MDG.
Roles and work centers
Standard roles in the form of templates are available to align applications with the roles and responsibilities in the organization. These domain- and task-specific roles include data specialist and read-only, among others.
The SAP Master Data Governance UI for central governance is based primarily on Web Dynpro for Advanced Business Application Programming, SAP’s standard UI for web applications developed in ABAP. However, the data consolidation UIs are based on SAP Fiori. Additionally, Fiori apps are available for request and approval processes of central governance and for analytics applications. Fiori apps are mobile-friendly and have the same look and feel on both desktop and mobile devices.
Data that is in the change request is stored in a staging area until the workflow is completed.
Formalize Your Data Governance
People in your organization are already doing data governance as part of their regular job. For example, the accountants are probably ensuring that postings are made to the correct ledger codes, your accounts payable department is ensuring that invoices are sent and matching payments are received. Much of your operational data is already part of an active management process but to a large extent their interest is in quantities and values.
The areas that get less quality checking are the master data that drive many of your business processes. Master data governance aims to put in place formal management responsibilities for the overall quality and reliability of this data.
One of the changes in attitude that is driven by data governance is to move away from a reactive approach to quality into a more proactive approach. Often poor data quality is only found when a business process fails when a delivery cant be made or when your IT system stops working, which is hardly the best way to find problems. It is also common when disasters occur through poor data quality that nobody can be found to take responsibility. Data governance ensures that somebody is clearly responsible not just for fixing the disasters but also for reducing the likelihood of one occurring.
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Master Data Management What Why How & Who
Master data management arose out of the necessity for businesses to improve the consistency and quality of their key data assets, such as product data, asset data, customer data, location data, etc.
Many businesses today, especially global enterprises have hundreds of separate applications and systems where data that crosses organizational departments or divisions can easily become fragmented, duplicated and most commonly out of date. When this occurs, answering even the most basic, but critical questions about any type of performance metric or KPI for a business accurately becomes a pain.
Getting answers to basic questions such as who are our most profitable customers?, what product have the best margins? or in some cases, how many employees do we have? become tough to answer or at least with any degree of accuracy.
Basically, the need for accurate, timely information is acute and as sources of data increase, managing it consistently and keeping data definitions up to date so all parts of a business use the same information is a never ending challenge.
To meet this challenges, businesses turn to master data management .
What youll learn from this article:
This article explains what MDM is, why it is important, how to manage it and who should be involved, while identifying some key MDM management patterns and best practices. Specifically, it covers:
Lets get started!
How To Get Started With Master Data Governance
As stated above, there is no one-size-fits-all, yet as a minimum, you need to consider the following steps towards a data governance program:
- Make someone accountable for the program, e.g., a CDO
- Make it central to all data management disciplines
- Assess where you are you can use a maturity model then plot your journey
- Define roles and responsibilities
- Measure progress by setting KPIs
When digging a little deeper into the program, the next level could consist of these six blocks:
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What Is Sap Master Data Governance All About
The following videos provide quick insight into the concept and benefits of SAP Master Data Governance.Watch the videos introducing SAP Master Data Governance from a business perspective, it takes no more than 3 minutes .
Video 1: Introducing SAP Master Data Governance
The first video describes the impact of poor master data across the enterprise and outlines how SAP Master Data Governance can help to get and keep your customer, supplier, material, financial and technical asset data ready-to-use for excellent business operation and analytics. And this is valid also in hybrid landscapes consisting of on-premise and cloud systems. Check the SAP Master Data Governance introduction video out on SAP.com:
Video 2: Introducing Consolidation with SAP Master Data Governance
The second video focuses on the consolidation capability of SAP Master Data Governance which has first come with SAP MDG release 8.0, and substantially enhanced with the current release MDG 9.0. With this capability, SAP MDG tackles the challenge of consolidating master data that is spread across heterogeneous system landscapes. As of release SAP MDG 9.0, consolidation is available for business partner , material data and custom objects. More details can be found in this presentation.
Video 3: Master Data Governance for Technical Assets
Video 4: Master Data Governance for Retail and Fashion Management
Avoid Errors And Follow
We would like to explain the damage that unclean data can cause using the example of material master data. It forms the basis for many essential processes in production and logistics.
To create material master data records correctly in SAP, up to 600 fields must be filled in. Creating and maintaining these data records involves numerous departments and even external stakeholders such as customers and suppliers. Coordinating all the parties involved poses a genuine challenge.
Without a transparent process for creating and maintaining master data, most errors remain undetected, which can lead to serious consequences. For example, if the wrong material is ordered, completion dates can be delayed. This can cause significant economic damage for organizations.
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The Importance Of Company
Companies are processing ever increasing amounts of data from a wide variety of sources . The most important type is master data, which contains basic information about customers, suppliers, employees and products. It forms the basis for the digitalization and automation of business processes. Used correctly, businesses can leverage data to better respond to customer requirements and changes in the market.
However, incorrect data can lead to wrong decisions and cause considerable damage. If personal data is not handled in a legally compliant manner, companies could even find themselves in legal difficulties.
This is why organizations today need a strategy for the correct management and processing of master data. Data governance provides the necessary framework for this. In this blog post, you will find an introduction to the topic and learn why no company can afford to do without data governance in times of digital transformation.
Establish Data Governance Processes
The next step is to define processes that describe how your data should be backed up, protected, stored and archived. This includes guidelines on how certain data may be used and which persons or departments are authorized to perform which actions. Furthermore, control processes are required to ensure the continuous monitoring of compliance and adherence to legal requirements.
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Work In A Legally Compliant And Secure Manner
Special attention should be paid to personal data, such as customer and employee master data. The General Data Protection Regulation , which lays down the principles for processing personal data, applies within the European Union. Companies must prove compliance with this regulation. If these principles are not implemented in a legally compliant manner, companies can expect penalties of up to 4% of the total annual turnover generated worldwide.
Why To Use Sap Mdg
SAP MDG allows the Company to benefit from the following facilities:
- SAP MDG maintains supplier, material, financial, customer and other data Centrally to ensure stability throughout the enterprise.
- The benefit of a demonstrable audit trail that identifies why, by whom and when the data is modified.
- Applying, integrating and reusing available business logic and infrastructure for data authentication.
- Use pre-built user interfaces as well as a workflow on the verified data models.
- Duplicate master data in the non-SAP and SAP systems, allowing the creation and maintenance of master data governance.
- SAP MDG enables a unique solution that can provide both the central governance and the consolidation, shortens management and reduces the Total Cost of Ownership.
- Provides a unique and reliable view of data and responds to Enterprise Information Management and Master Data Management challenges.
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Harmonize It And Business Concerns
The goal of integrated master data management is to harmonize IT and business concerns. This creates a foundation for future projects, improves data quality and, in the long term enhances your decision-making basis and thereby achieves indirect cost-saving benefits.
This approach not only serves to automate individual process steps or business processes the right end2end strategy also lets you combine master data management with effective process management.
The SAP Master Data Governance solution was created specifically with this purpose in mind, and it is highly integrated with the SAP Business Suite. SAP MDG can be combined with process management components including rule-based workflow management using the Business Rule Framework .
% Of All S/4hana Projects Struggle Due To Data Issues
In the current business climate, many organizations find their IT initiatives, like S/4HANA, being pushed out or placed on hold. You need to find creative ways to prepare your organization for the move to S/4HANA, de-risk the project, and demonstrate immediate business value with underlying data readiness.
Utopia enables the acceleration of business value prior to and during your S/4HANA project by understanding and immediately improving your data. Most organizations concentrate on the capabilities and cool stuff in their new S/4 system while neglecting the underlying data. Understanding, cleansing, and ultimately migrating data is often the make-or-break factor for the success of any S/4 program.
Even if you havent begun your S/4HANA transformation program or have been forced to hit pause, Utopia can help you improve your data now, hands-free. Better data will help your organization today and prepare you for a successful S/4 program when the time comes. Understanding and improving your data now, and deploying a small MDG instance is a No Regrets Step that you can begin today.
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