Data Capabilities in SAP

As you are already reading this, I would like to thank you for your time. In next 15 mins of reading, we will deep dive into data related capabilities of SAP.

Target audience is anyone who wants to understand and is looking for solutions to manage different aspects of data in SAP.

I have tried to keep the language as simple as possible. So, let’s gets started.

Let us begin with understanding the topic itself.

What is Data?

Data is like Identity/Information/Intelligence. It can be information like facts, name, numbers, alphabets, statistics, symbols or anything which is able to describe a particular thing. Collection of different data enable us to identify a particular thing. It helps us to understand, learn and apply learning in different situations.

For example: If we consider object Car.

All information about it like its red in color, has four wheels is data about it.

SAP (System Application and Products in Data processing)

SAP is widely used Enterprise Resource Planning (ERP). ERP means it helps to create a central system in which all the departments in an organization has access and sharing common data. It has well designed business processes example Procure to Pay or Order to Cash and it even allows customers to do customization as per their own needs. All different business processes are integrated and share the common data.

When we talk about data in SAP, it can be divided into 2 types as below.

Master Data – This is the reference data that is needed to execute business transactions. Example Material Master, Vendor Master, Customer Master, or Business Partners.

Transactional Data – This is the actual business transactions like Purchase Orders, Sales Orders and Open Inventory etc.

In any SAP/Non-SAP project, customers are always looking solutions for below data related areas.

Let’s deep dive into this to see how SAP helps to address this topic.

What is Master Data Management?

Master data management is the process of creating and maintaining a single master record or single source of truth for each person, place, and thing in a business.

Why Master Data Management?

In business there are mostly lot of systems involved like ERP, CRM, HCM. There are many people involve creating, changing data all the time across different departments. This creates data related issues like duplication, siloes, out of date and in consistency. Bad data further results in bad data driven business decisions, delays and financial losses.

Benefits of Master Data Management is as follows.

SAP addresses this master data management and governance using SAP Master Data Governance (MDG).

What is SAP Master Data Governance?

SAP Master Data Governance is an application which allows you to combine data from multiple sources, manage it and even replicate to different systems. It acts as a central hub for the core master data objects like Material Master, Business Partners and FI related data objects like Cost Centre, Profit Centre etc. It also allows to create custom processes and govern it. It also provides the options to co-deploy on existing SAP system.

SAP Master Data Governance application can be divided into below 4 areas.

Consolidation: Create a single source of truth by uniting SAP and Non-SAP data sources and mass processing additional bulk updates on large volumes of data. Below diagram shows the consolidation process in SAP MDG.

Data Load: Import Data from the file.

Initial Check: View loaded data and check data quality.

Standardize: Enrich data for example postal code is of a particular format.

Match: Find duplicates based on defined rules.

Best Record: Create best record out of multiple.

Validate: Validate best record against customizing or using central governance.

Activate: Activate consolidate master data for operations.

Governance: Allow various teams to own unique master data attributes and enforce validated values for specific data points through collaborative workflow routing and notification.

Below diagram from SAP shows the process of governance on high level.

From system perspective central governance is achieved by designing following steps.

Data Modelling: It is basically collection of attributes and entity. Attributes are generally fields to govern, and entity is collection of those fields. It allows to define relationship between different entities.

User Interface (UI) Modelling: This is to define how the UI screen will look.

Process Modelling: This is to define the process whether its creation or change etc. It also groups together data, UI and process.

Workflows: It has rule-based workflow to setup some kind of approval process. It helps to keep control of the process.

Data validations: It helps to set rules to validate data.

Data derivations: It helps to derive or default data based on defined rules.

Data Quality: It helps to improve the quality of data. Define, validate, and monitor established business rules to confirm master data readiness and analyze master data management performance.

Below diagram from SAP shows process of Data Quality.

Before we see how SAP uses rule mining capability which uses concept of AI and machine learning to identify data patterns and suggest quality rules to improve the overall quality of data.

Let us deep dive into concept of Artificial Intelligence.

What is Artificial Intelligence?

Artificial Intelligence is like replication of human Intelligence. It means a machine or robot can do things which we humans do. In order words machine will think like us.

For example, assistant in our phone like Siri or Alexa are usage of AI.

What is Machine Learning?

Machine Learning from the name itself is the process in which a machine is trained. It is like teaching child different shapes by showing how different shape looks. It can be based on the algorithms too.

Best example here is Self-driving car.

What is Deep Learning?

Deep learning also sometimes refers to as artificial neural network is replica of human brain. How human brains can link different clues and identify a pattern.

For example, how we solve puzzle. There are various parts we visualize it, understand it by its color or shape and try to put every piece together. Deep learning does the same thing. Best example would be Face recognition.

What is Generative AI?

This term Generative AI is very frequently use everywhere but what it means? In simple language it is creative AI which creates on its own without been told how it needs to be created.

?For example: I asked it to create new human man robot angel. Below is the generated AI image.

If I want to summarize all the concepts of AI together than it picturize below diagram.

As we now understand basics of AI and what it means let us move back to actual topic of Rule Mining in SAP MDG. SAP uses AI and Machine learning to identify data patterns and propose rules automatically to improve the Data Quality.

Below Diagram shows how rule mining helps in SAP MDG Data Quality functionality.

Goal: A detailed explanation of this mining run’s purpose, or the rules you expect from the data.

Tables:? A list of tables to be mined at the same time. Under each table, you define the focus area and fields you want to use for the mining run.

Focus Area: The data set you want to use for mining. For example, in this case we want to perform product master rule mining and choose Product Type = Finished Goods (FERT). These areas are carried to the mined rules later.

Fields: A further drilldown of the selected focus areas on field level. The system examines the values of the selected fields to find potential rules.

Focus area then becomes the scope when Data Quality rules are defined, and fields becomes to condition criteria.

Data Migrations

Data Migrations is movement of data from legacy system to SAP. In S/4 HANA, SAP has provided Migration Cockpit which is used to migrate data from legacy to SAP. Migration Cockpit has predefined set of programs and its ready-made template in .xml format, which is used for migrations.

In Migration Cockpit there are two ways of migration.

·?????? Migrate data directly from SAP system.

·?????? Migration data using Staging tables.

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Migrate data directly from SAP System: In this approach SAP ECC system is connected to SAP S/4 HANA system using RFC connections and data is migrated.

Migrate data using Staging tables: In this approach staging tables are directly tables from local or remote database or .xml file template is filled with data and uploaded to system.

Below is the overview of Migration Cockpit steps from SAP.

In SAP ECC, tools like Legacy System Migration Workbench (LSMW) are used. There are 4 methods of upload as shown in below figure.

Direct Input- SAP has given standard programs which can be directly use for upload. It directly does table entry.

Batch Input Recording – Recording of the screen is done and then its use to upload records.

Business Object Method (BAPI)- Standard BAPI given for uploads.

IDOC – Very similar to BAPI method. Standard message types can be used for uploads.

One important part of migration from ECC to S/4 HANA is Business Partner concept. Customer/Vendors needs to be converted to Business Partners.

There are two approaches of conversion.

·?????? Greenfield approach where you decide on high level mapping and syncing of number ranges account groups and BP roles and then you do data migration of Business Partners.

·?????? Brownfield approach where you do system conversion converting Customers/Vendors to Business Partners in ECC itself before the technical upgrade.

Below diagram from SAP shows high level approach Customer Vendor Integration (CVI) for system conversion approach.

Integrations

Integrations are important part when dealing with the data. Data needs to be transferred to and from different systems. SAP Cloud Platform Integration (CPI) on SAP Business Technology Platform (BTP) is quite flexible in this case. Below diagram illustrates the Integration flow of Integration suite in CPI.

Data archiving is used to extract data from the database, write it to archive files and delete the data from the database tables. It helps to clean up database tables which in turn helps to improve performance of the system. Transaction code SARA gives guided approach for archiving a particular data object. Data archiving is quite sensitive topic so it should be done after the discussion and agreement with all the stake holders.