SAP’s Data Management solutions offer customers a unified platform to improve data integration, management, governance and security. They allow enterprises to create a centralized repository for their data to optimize reporting and analytics operations.
SAP Master Data Governance (MDG) offers organizations a single trusted view of data to address digital, operational and analytical challenges directly. It helps increase data accuracy and reduce the total cost of ownership.
Data Integration
Data integration transfers data from various sources to a target system, whether an ERP system like SAP or another enterprise application. It also includes transforming, enriching and loading the data to the desired destination.
Data from various systems, such as e-commerce, file storage and social media, can be integrated with SAP using different integration methods. Some of these methods include SAP data management solutions, which provide a set of predefined interfaces to transfer data between SAP and other systems.
Traditionally, these systems were siloed. It made it difficult to extract, transform and load for new use cases.
Today, new product lines and data sets are constantly being introduced, causing many data teams to rebuild their in-house data pipelines.
To solve this problem, many organizations use ETL tools to extract, transform and load data into analytical systems for analysis. These data pipelines can get complex, and keeping up with the ever-changing landscape of data sources, connectivity issues and other challenges takes a lot of work.
Data Warehousing
Data warehousing is collecting, managing and analyzing data to provide business insights. It is a critical component of a business intelligence system that provides valuable business insights to decision-makers and helps them make better business decisions.
Data warehouses store current and historical data from many sources in one place, enabling decision-makers to get their queries answered quickly without relying on limited data or intuition. It improves decision-making processes and increases efficiency for a business, mainly when applied to market segmentation, inventory management, sales, and financial management.
Data from various transactional, historical, or external systems, applications, and sources are combined in a data warehouse serving as a single repository. Its architecture is separate from the operational procedures, and it’s designed for decision support, analytical reporting, ad-hoc queries and data mining.
Master Data Management
Master Data Management is maintaining a single source of truth for data about a business. It includes customer information, product data, supplier/vendor data, and other fundamental elements essential to an organization’s operation.
Master data management removes data redundancy and ensures that the entire pool of master data is available to all users within the organization, regardless of where they are located. It also ensures that the integrity of data is maintained.
A key feature of master data management is matching and merging duplicates into a single, accurate record. This capability enables a business to make correct decisions.
It is essential if an organization holds data in multiple systems with different definitions of the same data. For example, suppose an organization uses a different product hierarchy to manage inventory than it uses to support marketing efforts or pay sales reps. In that case, the various versions of the same master data can be challenging to reconcile.
Establishing a single version of the truth for master data requires employing people, processes, and technology to address these problems. It is achieved through a common set of standards that can be implemented across the organization.
Data Lifecycle Management
Data Lifecycle Management (DLM) ensures all your business data is available to users when needed. It is essential in today’s fast-paced global corporate world, where disruption to critical processes and workflows can have serious consequences.
DLM also helps ensure that your data is secure and doesn’t fall into the wrong hands or get corrupted by malware or other infections. It is accomplished by establishing protocols for managing data throughout its life cycle.
For example, once data is deemed useless for processing or use, it should be deleted. This process can be automated via a deletion policy or manually performed by an information owner.
A robust information lifecycle management solution helps to meet data protection and retention regulations in a consistent manner, which can save organizations time and money. It also provides a secure solution for moving data from active IT systems to archived formats, saving further costs in the long run.
DLM also enables organizations to automate data archiving, which can lower storage costs and improve the performance of IT systems. It mainly benefits organizations that comply with the General Data Protection Regulation (GDPR).
Data Governance
Data is one of the most critical assets for any organization. It enables companies to make decisions, improve their performance and boost their reputation with customers. However, if the data they use needs to be corrected or updated, it will lead to mistakes in their business practices.
For this reason, organizations need to develop a data governance program to ensure they use clean and accurate data. They must also create policies communicating vital processes and responsibilities for governing data.
Effective data governance can help organizations identify and prioritize essential data assets, ensure they are of high quality, improve their value to the business, and enable better interactions with customers. It can also help them improve data integration, storage, and use and streamline data stewardship and compliance monitoring.
Data governance can also help organizations avoid the common pitfalls of data breaches, such as security issues and unauthorized access. It can also help organizations comply with privacy laws in multiple jurisdictions. It can also provide a single view of the organization’s data, allowing for more accessible data analysis and planning.
