Friday, December 2, 2022

Sap Data Governance Best Practices

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Our Data Governance Scoping Assessment identifies your needs and establishes a strategy, roadmap and implementation steps based on your organizations objectives for governance. One of the important prerequisites that determine the success of an MDG implementation is clean data that adheres to business rules. Cleansing and preparing data manually is an error-prone and time-intensive task. We offer Fast-Track, prototype-based implementations for data quality monitoring and remediation alongside MDG. And we can utilize cleansed and harmonized MDG master data to simplify and accelerate your movement to SAP S/4HANA.

Grc Tuesdays: Governance Risk And Compliance And The Data Debt A Conundrum That Can Be Solved

I was recently exchanging thoughts and ideas on Governance, Risk, and Compliance topics with one of my previous colleagues from SAP GRC Solution Management and a good friend , when she mentioned a terminology that I wasnt familiar with: Data Debt. But I am familiar with the concept like most of you I am sure.

In essence, data debt is a form of technical debt that is usually driven by short term focus, and it consists in the additional effort required by leveraging an easier trade-off compared to a potentially more complex one that would initially have taken more effort or resources.

If you have been part of an initiative to automate any business process, then chances are that you have been confronted to this issue!

Do You Need Sap Master Data Management Software

The short answer is: yes, you do. As your company grows, you are bound to run into many data-related challenges. Though you might use a lot of resources and tools to prepare data and insights, any inaccuracy in it will drain down all the efforts.

But it does not end there!

Some other crucial SAP master data management challenges can impede your business processes, too. Well have a look at those here and also determine how you can overcome them by using SAP master data management software.

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The Beginning Of The Analytics For Everyone Era

Its not unusual for an employee to pull up a list of overdue accounts, an accounts receivable statement, and a sales performance view. But those capabilities are not analytics by todays standards. Instead, they are aging glimpses into a past that will eventually become irrelevant.

Today, the realm of decision-making needs to extend beyond solely reporting static operational data evolving to include real-time, intelligent analysis of next steps and future possibilities. In fact, surveyed organizations shared with Forrester that the top benefits of augmented analytics include detecting anomalies, automating data quality, using natural language to answer questions and explain results, and identifying, predicting, and forecasting trends.

With cloud-based, predictive analytics such as the SAP Analytics Cloud solution, part of SAP Business Technology Platform many companies are empowering people of all ranks and skill sets to make decisions that lead to consequential outcomes for their business and community.

Bumble Bee Foods LLC, for instance, has improved its brand equity with retail buyers and consumers through greater transparency and facilitated a market premium for fair-trade products. The company analyzes fish buying trends and data to identify historical performance and improve current-day catches in fishing villages together with nongovernmental organizations. This predictive approach is improving the livelihoods of local fishing communities.

Why Bother With Managing Master Data

Sap mdg(master data governance) online training

Because master data is used by multiple applications, an error in the data in one place can cause errors in all the applications that use it.

For example:

An incorrect address in the customer master might mean orders, bills and marketing literature are all sent to the wrong address. Similarly, an incorrect price on an item master can be a marketing disaster and an incorrect account number in an account master can lead to huge fines or even jail time for the CEOa career-limiting move for the person who made the mistake.

Real Life Master Data Example: Why You Need Master Data

A Typical Master Data Horror Story

A credit card customer moves from 2847 North 9th St. to 1001 11th St. North. The customer changed his billing address immediately but did not receive a bill for several months. One day, the customer received a threatening phone call from the credit card billing department asking why the bill has not been paid. The customer verifies that they have the new address and the billing department verifies that the address on file is 1001 11th St. North. The customer asks for a copy of the bill to settle the account.

This would not be bad if you could just union the new master data with the current master data, but unless the company acquired is in a completely different business in a faraway country, theres a very good chance that some customers and products will appear in both sets of master datausually with different formats and different database keys.

In Summary

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How Do You Create A Master List

Whether you buy a MDM tool or decide to build your own, there are two basic steps to creating master data:

  • Cleaning and standardizing the data
  • Matching data from all the sources to consolidate duplicates.
  • Cleaning and Standardizing Master Data

    Before you can start cleaning and normalizing your data, you must understand the data model for the master data. As part of the modeling process, you should have defined the contents of each attribute and defined a mapping from each source system to the master data model. Now, you can use this information to define the transformations necessary to clean your source data.

    Cleaning the data and transforming it into the master data model is very similar to the Extract, Transform and Load processes used to populate a data warehouse. If you already have ETL tools and transformation defined, it might be easier just to modify these as required for the master data instead of learning a new tool. Here are some typical data cleansing functions:

    • Normalize data formats: Make all the phone numbers look the same, transform addresses and so on to a common format.
    • Replace missing values: Insert defaults, look up ZIP codes from the address, look up the Dun & Bradstreet Number.
    • Standardize values: Convert all measurements to metric, convert prices to a common currency, change part numbers to an industry standard.
    • Map attributes: Parse the first name and last name out of a contact name field, move Part# and partno to the PartNumber field.

    For example:

    Missing & Incorrect Cross

    Incorrect relationships across Master Data domains leads to fragmented, ineffective data that, in turn, hurts an organizations growth. Master Data should be linked across domains. Linking this data allows you to

    Impacts of missing & incorrect cross-domain relationships

    • Missed opportunities where special offers may not be offered to Customers of the Product being cleared
    • Reduces your negotiation power as purchases from a vendor cannot be clubbed
    • Missing Product & Customers links lead to dead & unsold stock blocking liquidity for a company

    Cross-domain Master Data Management Best Practices

    • Regularly check for duplication of data
    • Maintain hierarchical relationships of your customers entities within SAP
    • Keep your cross-domain relationships in order with a smart Master Data Management solution will help you .

    This is where the Discus Master Data Management add-on for SAP shines. It offers a unified platform where you can create a holistic view of your enterprise-wide information. Right from administrative to governance functions across the domains, MDM enables you to handle it all from a single user interface.

    Furthermore, it eliminates the guesswork and uncertainty thus, paving the way for powerful insights and informed business decisions.

  • Data Manipulation & Embezzlement
  • Person-level Dependency
  • Lack of Effective Data Governance
  • Thus, in the absence of data governance, your MDM implementation may suffer.

  • Lagging Analytics & Business Intelligence
  • Auto-generated Master Data
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    Who Benefits From Using Sap Master Data Governance

    The platform is suitable for companies of all sizes. With a wide range of data governance and management features in a single software product, SAP Master Data Governance lets enterprises control their data-related activity without the need for multiple platforms. This helps reduce confusion regarding data asset locations and internal management practices. SAP Master Data Governance also integrates with other existing SAP platforms.

    Quick Starting Point For Critical Analytics Scenarios

    SAP Insider Webinar Best Practices for Value Monetization of Master Data Management and Governance

    Another aspect of SAP Analytics Cloud that most organizations enjoy is access to pre-configured, best-practice, industry, and functional business content. Free access to a library of end-to-end scenarios for specific industries and lines of business lets SAP customers quickly develop their analytics systems and tailor applications with packages and templatized content from SAP and our partners.

    Pre-configured, best-practice offerings, content, and technical objects available through SAP Analytics Cloud help organizations jump-start their implementation and adoption of individual analytics scenarios. The open-innovation technology behind the cloud-based analytics solution and ready-to-use analytical applications and content from SAP partners can be leveraged immediately and tailored as needed.

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    Check Data / Validate

    Any field-based properties that are set will be checked when a user runs a Check Data. This is essentially an offline validation of the rules that have been set for the data. If anything violates the rules, the inconsistencies will be returned to the user. Check Data is also run at the beginning of any validation. Validations ensure that data available in the spreadsheet will be accepted by SAP as having valid data for all the upload fields. You could also force users to validate data before doing a run by setting an IF statement around the commit function code in the script to only run if the validation column has a Success message. Without a successful validation, users will be prompted to validate data first before they are allowed to run it in SAP. The message presented to the user can be customized by doing a transform of the error message thats returned by trying a run prior to validation.

    You can always use Studio to help clean up bad data. You can harness the power of Studio to help you ensure your data is good before it ever gets into SAP. Prevention is the best medicine!

    What Is Master Data Governance

    Before getting into master data governance, lets understand what the master data is. The master data is the core information within an enterprise that describes how business in conducted. Master data can include pieces of information on customers, products, locations, etc.

    While theres a high volume of available data, you dont need all pieces of information to continue doing business. Its up to the company to decide which data are reusable and most valuable to maintain.

    Because the data crosses various divisions, it can become duplicated, fragmented, and out-of-date. The decrease in quality of business information eventually leads to the need for accurate and consistent data.

    Thats where Master Data Governance comes in. MDG refers to the management or handling of data within a system that are shared across organizational departments.

    To improve data quality, MDG standardizes definitions of data terms and usage, eliminate duplicate information, and update fragmented data to provide a single view of truth across all departments.

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    Best Practices For Master Data Mapping In Central Finance

    Master Data Governance and mapping are critical to any Central Finance project. In this session we will walk you through the MDG Foundation mapping in Central Finance and show you firsthand how it can be leveraged to redesign and harmonize your master data in S/4HANA. Attend this session to:

    Discuss Key, Value, and Cost Object mapping in MDG Foundation and how they can each be leveraged throughout your Central Finance deploymentTake a deep-dive into the functionalities and capabilities of Value Mapping and learn how you can leverage context-based mappings for similar master dataUnderstand key S/4HANA points of configuration and how they drive the master data strategy for both current and future phases of Central FinanceKnow the importance of building a rock-solid master data strategy in Central Finance and how missteps can potentially result in errors from missing master data dependencies and mappings

    This content is available to Premium Members.

    Getting Started With Your Mdm Program

    Five components of Data Governance

    Once you secure buy-in for your MDM program, its time to get started. While MDM is most effective when applied to all the master data in an organization, in many cases the risk and expense of an enterprise-wide effort are difficult to justify.

    PRO TIP: It is often easier to start with a few key sources of master data and expand the effort once success has been demonstrated and lessons have been learned.

    If you do start small, you should include an analysis of all the master data that you might eventually want to include in your program so that you do not make design decisions or tool choices that will force you to start over when you try to incorporate a new data source. For example, if youre initial customer master implementation only includes the 10,000 customers your direct sales force deals with, you dont want to make design decisions that will preclude adding your 10,000,000 web customers later.

    Your MDM project plan will be influenced by requirements, priorities, resource availability, time frame and the size of the problem. Most MDM projects include at least these phases:

    As stated earlier, any MDM implementation must incorporate tools, processes and people to maintain the quality of the data. All data must have a data steward who is responsible for ensuring the quality of the master data.

    The rest of this article will cover the details of the technology and processes for creating and maintaining master data.

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    What Is Data Governance Strategy

    A data governance strategy defines how data is named, stored, processed, and shared. Instead of data being a byproduct of your applications, it becomes a vital company asset. The strategy defines how data will be used efficiently in an organization.

    In this regards, the company will set up processes and structures to clean, store and share this data. This process eliminates duplication of resources while enabling access to vital data by those who need it in an organization. When you have an effective data governance strategy you can tell where data originates from, where it is stored, and who can access it. Data strategy precedes regulatory compliance. Actually, it boosts compliance.

    Master Data Management Best Practices

    A best practice master data management approach seeks to maximize business value and minimize risks and uncertainty. An understanding of people, processes and technology, are considered best practices for a master data management approach.

  • Define mission, objectives and business value for project in order to justify budget, motivate the organization, provide focus and measure progress.
  • Agree on an up front organizational governance model before implementing any new IT solutions. This will make ongoing issues easier to resolve and provide clear direction for business analysts configuring new validation and taxonomy solutions.
  • Business users must take full ownership of a master data project in conjunction with the IT group for technology support.
  • Organizational change and knowledge transfer are the biggest master data challenges. A change management team, with a well thought out plan suitable for the client culture is critical. This team provides the leadership and fosters communication to resolve issues throughout the organization.
  • The recommended IT strategy will require use of appropriate real-time governance and data quality tools. These tools must be capable of data cleansing, validation, integration, and enrichment suitable for the client.
  • Business user workshops and easy to understand graphical modeling tools is strongly advised to improve communication during the analysis phase of a MDM project.
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    Designing A Cloud Environment With The Customer In Mind

    At SAP, we take great pride in enabling this new era of analytics for everyone and helping people of all roles and skill sets drive impactful outcomes for their business, their community, and the entire world. This mindset is what led to the development and continuous evolution of SAP Analytics Cloud.

    From the boardroom and office desk to a customer meeting, users can trust their business logic and formerly hidden insights can become well-advised actions that enhance corporate outcomes. More importantly, every person can be a part of the data-to-value chain evaluating enterprise data, asking the right questions, getting real-time answers, and moving forward to help the business succeed and even improve humankind.

    Find out how your organization can track carbon targets and meet sustainability goals with SAP Analytics Cloud.

    To learn more about how SAP partners are helping businesses succeed with a unified technology platform, read the previous articles in this series: part one, part two, and part three. If youre interested in software partnership opportunities with SAP, check out SAP PartnerEdge, Build. If youre already a partner, check out these exciting initiatives: Hack2Build, SAP Analytics Cloud business content wave, and .

    Jagdish Sahasrabudhe is CTO of the Global Partner Organization at SAP.

    Data Governance Tools And Technology

    Product Lifecycle Management with SAP MDG for Materials

    Creation of the data governance framework does not require any additional tools. However, technologies can help collect, manage, and secure the data. Consider these:

    • Information steward applications assist in data profiling and monitoring the performance of the enterprises data governance policy. It facilitates executing information governance initiatives across the business units, enforcing quality standards with data validation, and measuring the improvement of data quality processes.
    • Metadata management solutions, often referred to as EMM , categorize and consistently organize an enterprises information assets and has become increasing important in the era of Big Data. Information of the data asset that is maintained include type, tags, source, and dates.
    • Information lifecycle and content management technologies control data volumes and manage risk with automated information archive, retention, and destruction policies. Content management-specific capabilities can also streamline business processes by digitizing documents and integrating relevant content with transactions and workflows.
    • , or augmented data integration, enhances existing enterprise data with information attained using new technologies such as AI and machine learning. The goal is to improve decision making and help some applications in becoming more self-tuned.

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