Make Efficient and High-Quality Master Data Management a Reality with Data Attribute Recommendation
People tend to generate a lot of data each day and businesses highly depend on this data. Companies that use data for business analytics and draw conclusions from it can react to changes in real-time, innovate faster, and ultimately make better business decisions based on the gathered data. But in many companies, there is an uncontrolled growth of data and businesses are often not able to gain control over these vast data amounts, ultimately hindering them in drawing valid conclusions and business decisions out of the available data. With the help of Machine Learning and Artificial Intelligence an intelligent form of Data Management is now possible
Businesses these days face many challenges. With the help of SAP AI Business Services, services that provide strategic machine learning capabilities that automate and optimize processes, those challenges, including master data management challenges can be easily automated now.
- Incomplete master data prevents profound decision making
- Wrongly maintained and incomplete master data creates high manual efforts and inefficiencies
- Lack of enterprise-wide, consistent framework for object data maintenance (master and transactional data) often leading to inconsistent information and huge efforts for manual processing
The process of matching information for the inconsistent master data is currently being done manually by the master data team. The employees spent most of their time searching for the right categories, descriptions, missing master fields etc. This is often tedious and time consuming. The AI Business Service – Data Attribute Recommendation can help automate this process by recommending categories and sub-categories (or other customer specific information) to the master data team.
What is Data Attribute Recommendation?
The SAP AI Business Service “Data Attribute Recommendation” is an SAP Cloud Platform service that automates master data management tasks 24/7. Data Attribute Recommendation applies machine learning and artificial intelligence technologies to match and classify data records, making more efficient and high-quality data management a reality. It helps customers classify, compare and recommend data entities such as product data or user data into multiple classes. It also provides possibilities for a variety of customer specific tightly integrated scenarios with SAP S/4HANA and SAP S/4 HANA Cloud.
Bob, part of the master data management team of a Material Provider, has a very manual and repetitive job. All day long he is looking into material tables and searching for categories and subcategories of given products. This data is strategically very important for the comparison of products on a market. Bob usually needs a lot of time to categorize and match data with the received information given by the data providers.
As shown below, filling in a material table can be incredibly monotonous and time-consuming, due to a wide and fragmented range of business cases. In addition, filling in the material table may be required when adding new attributes or categorizing new data.
Scenario 2: Commodity Code Prediction
It is quite a common scenario that trucks / ships arrive at the international border or port of entry of another country and their loaded products do not have a commodity code at all, or the codes are incorrect. This often leads to rejection of assignment goods by customs or long waiting times at the border – in turn costing the organization heavily. Data Attribute Recommendation can predict the correct values by learning the logic behind complex master data fields and the associated dependencies to multiple data attributes.
Data Attribute Recommendation offers the following benefits:
- Reduces manual efforts in commodity code assignment
- Increases the validity of assigned commodity codes
- Decreases cost incurred by manual assignment
Scenario 3: Suggestion of Material Classes and Characteristics
Master Data Management is an extensive task in most organizations. Also, in organizations dealing with materials, Master Data Management is a tough job – the creation and updating of new materials, missing information in material master data, or choosing the right material classes. Many requests are coming back due to incorrectly chosen material classes or missing object information. Data Attribute Recommendation can be used for a prediction performance: bulk and individual classifications of materials and its characteristics are done online. It also enables intelligent master data governance in companies struggling with the creation of new material requests.
Data Attribute Recommendation offers the following benefits:
- Acceleration of the master data creation process
- Reduction of effort for the creation of new master/transactional data
- Automation of repetitive work and utilization of labor hours on other high-value tasks
The following diagram provides architecture details of the Data Attribute Recommendation Service along with the associated integration possibilities with SAP and non-SAP applications. The service is running on SAP Cloud Platform and is being offered as a re-usable service for SAP Cloud Platform customers.
All functionalities are delivered via web services over the HTTPS protocol and the communication with the service is secured by the OAuth 2.0 protocol. Moreover, the standard user authentication and authorization mechanisms provided by SAP Cloud Platform for Cloud Foundry is used. Customers can create an instance of the service and generate credentials to communicate with the service instance.
As visible in the illustration below, the service consumer, which could be an SAP or non-SAP application, would call the service via the HTTPS-based API which is secured by the OAuth 2.0 protocol. The functionalities of the services (e.g. classify and categorize entities such as products, correct values by learning the logic behind the master/transactional data fields) are available as RESTful APIs with respective endpoints and HTTP methods (especially GET, POST, DELETE). The data is provided back to the service consumer in the JSON format.
- Ready To Use
Data Attribute Recommendation is generally available on SAP Cloud Platform as part of SAP AI Business Services. Commercially the services can be consumed via the Cloud Platform Enterprise Agreement (CPEA). For more information, please visit the Pricing and Packaging for SAP Cloud Platform.
- Ready to test
Data Attribute Recommendation is available for testing via SAP Cloud Platform Trial. You can easily activate the service, test it on your own data sets and build a proof-of-concept around it. A trial activation tutorial is available on the SAP Developer Center under Trial account.
How to Engage with SAP
Especially, the SAP Customer Engagement Initiative enables you to get early insights into SAP’s product developments and directly work with the developers to define and shape future product directions. Three times a year, a list of new projects is offered. In a one-month-registration phase, you can then register for a first informative virtual session and decide afterwards, if you wish to participate in the project and influence SAP.
- Visit our SAP AI Business Services Topic Page with links to other resources, SAP Help Portal Links, Customer References, Videos and relevant blog posts
- For SAP Internal Use Only – Find more information about SAP AI Business Services and our Data Attribute Recommendation service on JAM
Read all blog posts of the SAP AI Business Services introductory–, and product portfolio series:
- Part 1: SAP AI Business Services – Exploit the Full Potential of Intelligent Technologies to Optimise your Business Processes
- Part 2: Artificial Intelligence – What it is and why you can’t turn a blind eye
- Part 3: Simplify your Procurement Process and Automate Accounts Payable with Invoice Object Recommendation”
- Part 4: Simplify Business Document Processing with SAP AI Business Services
- Part 5: Make Efficient and High-Quality Master Data Management a Reality with Data Attribute Recommendation (this blog post)