Every time businesses consider SAP Data Migration and Automation using external modern data platforms and building datasets and models using this data, familiar concerns surface:

How can we control complexity and cost?

How do we minimize our dependency on subject matter experts?

How do we ensure consistency in the data models when these get developed outside the source system.

These concerns are valid as they can drastically slow down progress. In this post, we tackle four of the most common obstacles that data teams and decision makers face when building SAP analytical models externally and we’ll show you exactly how indigoChart offers automation solutions to address these.

Concern 1: Speed up SAP Analytical Solutions Development

Current methods for SAP Data Migration and Automation often require significant SME (Subject Matter Expert) involvement. This dependency extends project timelines and increases costs. Finding a way to reduce this reliance while speeding up solution delivery is crucial.

How can you shorten the time it takes to produce SAP analytical solutions within a cloud native data platform such as Snowflake or Databricks while minimizing reliance on SAP Subject Matter Experts?

indigoChart automates the entire process of cleansing and enriching raw SAP tables, allowing data engineers to free up their time interacting with the SMEs. This dramatically reduces the time needed to produce analytical solutions and cuts costs by minimizing the reliance on expensive expertise.

Solution and Key Differentiators:

indigoChart automates generation of Enriched Base Tables along with Enriched Dimensions, creating reusable datasets that eliminate the need for manual transformations and improve data quality.

1. Enriched Base Tables: Enriched tables meaning conversion of “code to textual information”, by including the “text column” as an additional column in the enriched table, wherever applicable and all handled via automation.

Example

CSKS is Cost Center Master Data in SAP with some sample fields as below

CSKS:

MANDT                Client

KOKRS                 Controlling Area

KOSTL                  Cost Center        

DATBI                  Valid To Date

DATAB                 Valid-From Date

BKZKP                  Lock Indicator for Actual Primary Postings

PKZKP                  Lock Indicator for Plan Primary Costs

BUKRS                 Company Code

……

2. Enriched Dimensions by Module (ex. Finance, Quality Management, Materials Management etc.): These Dimensions conform to business definitions and are developed by combining multiple underlying “enriched” tables mentioned above. Additionally, there is textual representation via “Comments” that defines these Dimensions that also becomes metadata feeding into Knowledge Base.

   
   

   For example, Cost Center is one such Dimension that is comprised of enriched tables – CSKB (Cost Elements) and CSKU (Cost Elements Text).

Speeding up development is only one part of the equation. To fully maximize your resources, it’s critical to ensure your data engineers aren’t spending their valuable time on unnecessary complexities.

Concern 2: Failing to Unlock Your Data Engineers' Full Potential

SAP systems are complex, and engineers often spend too much time learning the intricacies rather than focusing on delivering value through analytics. This challenge is especially relevant when data engineers lack SAP knowledge.

How can you make the most of data and analytical engineers' time and skills? And how can you reduce the learning curve for them?

With indigoChart’s solution, you don’t need to worry about manually extracting business logic from the underlying SAP native modules. It handles this for you—extracting and embedding easy-to-understand business metadata, so your engineers can focus on operationalizing the data pipelines and not get bogged down by the complexities of SAP data.

Solution and Key Differentiators:

Re-usable analytical base Models by SAP domains (ex. Finance, Quality Management, Materials Management etc.) are developed from enriched tables and dimensions from the previous step.

Additionally, there is textual representation via “Comments” for these Dimensions that also becomes metadata feeding into Knowledge Base.

1. Reusable Analytical Base Models: These models integrate enriched base tables with relevant metadata, allowing data engineers to quickly work with SAP data without needing extensive SAP-specific expertise. See diagram below.

2. Metadata-Driven Framework: Automatically curated metadata simplifies the learning process, reducing the time engineers need to spend on understanding SAP structures.

For example, Accounts Payable is one such pre-built Financial Analytical Model that is comprised of enriched tables – BKPF (Accounting Header) and BSEG (Accounting Lines).

Unlocking data engineering teams' potential and optimizing their time alone isn’t enough. You also need to make sure your core business logic remains intact during modernization.

Concern 3: Keep Your Business Logic Intact — When Moving to External Modern Data Platforms

When moving SAP data to external systems like Snowflake and Databricks, many organizations worry about losing critical business logic embedded within SAP. This concern often leads to hesitation in modernization efforts that include source system data migration and model development.

How can you ensure that business logic remains intact when building data models outside the source systems?

indigoChart ensures the integrity of business logic by leveraging automated metadata driven logic development. These features maintain consistency across models, ensuring that key business rules and logic are preserved even in external environments.

Solution and Key Differentiators:

1. Analytical Models: Complex models, such as Vendor Aging or Custom Aging, are preserved and enhanced within the framework. The automation also supports custom configurations, ensuring accuracy and flexibility across diverse business environments.

2. Multi-level Hierarchies: The system supports data aggregation across Profit Centers, Cost Centers, and other business-defined categories, preserving reporting structures and the integrity of business logic.

3. Data Lineage: Automatically generated lineage maps the relationships between tables, providing transparency for impact analysis and change management, all while preserving the original business logic.

4. SAP Standard Codes: SAP standard codes such as SAP ABAP -Function Modules, Views, Classes, Currency Conversion, and T Codes that are embedded within the source system business logic is migrated as well to external data platforms, to ensure consistency in business logic. 

Once you’ve preserved your business logic, the next step is to ensure both IT and business teams can effectively access and use your data and metadata.

Concern 4: Build a Unified Knowledge Base with Metadata

Metadata is often overlooked, yet it holds immense potential for fostering collaboration between business and IT teams. The challenge is in creating a repository that can be accessed and used by both sides to improve decision-making and analytics.

How can you use metadata to create knowledge repositories that are useful for both business and IT?

indigoChart’s Knowledge Base Repository centralizes metadata, making it a dynamic, continuously evolving resource for both business and IT users.

This repository powers Generative AI assistants that automate SQL query generation, improving data accessibility and reducing manual effort.

Solution and Key Differentiators:

1. Knowledge Base Repository: By aggregating metadata into a centralized, evolving resource, indigoChart enables cross-team collaboration, helping both IT and business users access and understand the data they need.

2. Generative AI Assistants: These AI-powered assistants use the Knowledge Base to automatically generate SQL queries, allowing data engineers to respond to business needs quickly and efficiently.

3. Cross-Functional Value: The Knowledge Base bridges the gap between IT and business, empowering both teams to collaborate effectively while maintaining data integrity and usability.

Embark on Future-Ready SAP Data Migration and Metadata driven Analytics Strategy

SAP data migration to external modern data platforms isn’t about solving today’s challenges—it’s about creating a framework that will support future growth and scalability. indigoChart offers more than a quick fix; it provides a sustainable, long-term solution that evolves with your organization.

By

• Automating data preparation

• Minimizing reliance on SMEs

• Preserving business logic and

• Leveraging metadata for knowledge-driven analytics,

indigoChart helps organizations adopt a modern and forward-looking migration approach ensures that your data infrastructure can grow and evolve with your business, setting you up for long-term success.

If these challenges sound familiar, let us help you take the next step towards a sustainable SAP modernization strategy. Explore our SAP solutions right here.

Visit us at www.indigoChart.com or drop us a line at hello@indigochart.com