Individuals can quickly accumulate a lot of information on their computers—credit card and banking information, tax data, legal names, birthdates, mortgage information, etc.
This information can become disorganized and difficult to find, especially when old. This is why data cleansing is so important.
What is Data Cleansing?
As companies acquire gigabytes and terabytes of data, they need a way to parse it and store it. They also need a way to analyze the data to uncover new insights and business opportunities. Thankfully, SAP offers multiple solutions to meet these needs.
The company’s SAP Data Services platform is one of these tools that can help with the data management process. An SAP data management guide allows users to organize information and create a more effective structure, so it’s easier to work with. It can also be used to connect information from different company applications, which can improve visibility and collaboration.
Data governance is another important aspect of SAP’s data management system. It helps standardize definitions and business rules and automate governance tasks with collaborative workflows and notifications. It can also be used to improve master data governance across a federated network of systems. Hence, users can access the core attributes they need while maintaining local systems.
The solution also supports dynamic tiering, which relocates data from the memory layer to disk-based extended storage when it’s not accessed. This reduces database usage and processing times. The platform can also detect duplicates and relationships between different records, saving time when importing data into the SAP environment. It can also detect errors in the data and notify the appropriate department so they can correct it.
What is Data Integration?
Data is often stored in different systems, and bringing all the information together is difficult. This is why data integration is important. It helps to connect disparate data sources and make them accessible to users. It also provides a way to streamline the data management process and reduce duplication.
Data integration includes several tools, including SAP HANA Smart Data Integration (SDI) and SAP Smart Data Quality (SDQ). SDI loads data in batch mode or real-time into SAP HANA based on multiple source systems. It supports both structured and unstructured data.
It can transform and enrich the data using prebuilt transformations such as address cleansing and geospatial data enrichment. SDQ performs various data quality processes such as text processing, parsing, and standardization. It can also integrate and cleanse data from a wide range of on-premises and cloud systems, including database management systems, big data structures, file systems, messaging systems, web services, and proprietary systems.
SAP Master Data Integration is SAP’s strategy for the integrated intelligent enterprise. It aims to provide a shared, harmonized view of master data across business applications in the SAP suite and beyond. This is facilitated by a single domain model, which can be leveraged to share and expose information as a unified API in a hybrid or multi-cloud environment.
What is Data Warehousing?
A data warehouse is a centralized analytics repository that combines data from multiple sources and provides users with a single source of truth for business intelligence. Firms may build a single SAP data warehouse as the central platform for all analytics or multiple diversified data warehouses that serve specific use cases.
Either way, the data in a data warehouse must be parsed and cleaned before it can be used for analytics and reporting. This is done via a process known as data provisioning. Each solution is designed to connect to data from both SAP and non-SAP systems, including big data repositories like data lakes and Hadoop clusters.
These solutions provide a range of data modeling and processing capabilities, such as agile data preparation, dynamic tiering to move data from memory to disk, and data aging to systematically stale and delete older records.
These solutions are connected by a common data architecture, a foundational piece of the SAP Data Management landscape. Moreover, all of these solutions can connect to a common data lake and enable the combining and querying data across multiple SAP data warehouses.
This is all made possible through the SAP Datasphere cloud, a managed and persona-driven data warehouse-as-a-service that offers reduced deployment complexity, flexible pricing, and integration with other SAP applications.
What is Data Go?
The goal of data goes to provide users with a structured way to store large sets of raw and unstructured data. The data process consists of different components, such as SAP’s smart data access technology, which lets users call third-party SAP and non-SAP sources without copying it to the SAP HANA system (thereby keeping data transfer volume low), and SAP’s master data governance solution.
These solutions allow you to sift through gigabytes and terabytes of unstructured data to parse it for use in models, reports, and other business processes. It also includes automated systems that run rules on data based on predefined criteria. These include SAP-enabled Excel or business-friendly web forms from Precisely Automate, which help you improve data quality and streamline controller data processes.
SAP’s new Datasphere platform provides a unified experience for data integration, cataloging, semantic modeling, and warehousing across the entire data landscape. It is a secure, cloud-native solution allowing you to quickly distribute mission-critical data while keeping business context and logic intact.
