SAP Analytics and AI Assistants

SAP Analytics and AI Assistants

Organizations continue to re-invent the wheel when it comes to integrating and modeling Enterprise Applications data within their preferred data platform. Furthermore, the underlying metadata continues to be under utilized.

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Approach and Alternatives

Source Systems

SAP continues to remain a popular primary ERP system used by many medium to large sized businesses. The standardization of business operations and their integration into a single ERP system need large investments, as is the case with most ERP systems.

Methods and Offerings

BW Extractors, CDS (Core Data Services) Views, and Embedded Analytics are tools from SAP popularly used for migrations, as well as SAP's own analytical offerings such as Datasphere and B/4 Hana.

Modern Alternatives

Due to the disruption of legacy SAP BW environment and the rising need for an integrated offering of SAP ERP and additional analytical data sources, organizations must decide how to position themselves for their future analytical platform.

Common Pitfalls

SAP's analytical solutions are renowned for their ability to turn data into insights that drive informed decisions. However, to make the most of these powerful tools, you need more than just software – you need domain expertise, especially if the data from the SAP systems is to be integrated within a modernized data platform such as Snowflake or Databricks for Reporting, Analytical, and AI capabilities.

Pursuing SAP's analytical offerings can be complex, and challenges can arise in various areas. Click on each to identify your specific ones.

Building SAP analytical solutions can be costly starting with replicating and integrating SAP data near-real time into the data platform.

To reduce licensing costs, organizations might opt for lower-tier editions or limit the number of users, resulting in missed opportunities to leverage advanced features like real-time data integration or predictive analytics.

To minimize development time and costs, organizations may excessively use out-of-the-box queries and reports instead of creating tailored solutions. This can lead to inflexible reporting that doesn't fully address specific business needs or provide competitive insights.

Solely relying on standard SAP extractors without customizing or optimizing data extraction processes may result in incomplete data sets, longer processing times, and missed opportunities for real-time analytics.

Modeling the data for a particular business use case is far from simple

Especially given that the business logic is dispersed across hundreds of thousands of underlying tables, making the data integration process challenging.

While cost-effective, exclusively using pre-built SAP extractors can lead to incomplete or inflexible data models that don't capture all relevant business processes or custom configurations, resulting in gaps in analytical capabilities.

Failing to properly harmonize data from diverse SAP and "non-SAP sources" can result in inconsistent definitions, metrics, and hierarchies across the analytical landscape, leading to conflicting reports and eroding trust in the data.

Building and maintaining SAP analytics expertise in-house can be a significant challenge, considering multiple SAP products required to build the ecosystem such as SAP BI.

Lack of understanding of SAP's intricate table structures and their interdependencies (e.g., header-item relationships, configuration tables, and customizing tables) can lead to incomplete or inaccurate data extraction. This can result in analytical solutions that miss critical business context or produce misleading insights, particularly in areas like financial reporting or supply chain analysis.

Insufficient expertise in optimizing queries due to incorrect replication of SAP standard functions and text translations can result in poorly performing reports, excessive data retrieval, and unnecessary system load, impacting overall solution performance.

The sheer number of SAP options for both operational and analytical reporting can be confusing and quite expensive.

SAP's diverse array of tools for data extraction alone such as BW Extractors, CDS Views, Embedded analytics requires high reliance on SAP experts to interpret data throughout the migration process. Additionally, many SAP analytical tools such as Datasphere and BW/4 Hana have overlapping functionalities which may cause confusion in tool selection, leading to suboptimal choices for specific use cases or unnecessary licensing costs for redundant capabilities.

The need to master various SAP analytical tools can overwhelm users and IT teams, resulting in a longer time to value, reduced user adoption, and increased training costs. This complexity can also lead to over-reliance on a few expert users, creating bottlenecks in report creation and maintenance.

Managing data flows and integrations between multiple SAP analytical tools (e.g., from SAP BW to SAP Analytics Cloud) can be challenging.

Migrating from SAP S/4HANA to a modern data platform introduces a number of unique challenges due to differences in data structure, performance models, and overall architecture and can be attributed to one or more of the below.

Complex Data Extraction and Schema Differences.

Data Type and Format Discrepancies.

Performance and Data Volume Management.

Integration of Business Logic and Data Modeling

Data Quality, Governance, and Security.

Testing and Validation Complexities.

Rework and Iteration in Migration Process.

Managing Migration Costs arising due to infrastructure and know-how.

indigoChart's framework for developing an
SAP Knowledge Engine that power AI Analytical Assistants

Our framework simulates an SME's "Real Intelligence" by automating the process of enriching SAP Base tables and Meta data, by generating Re-usable base business models and Multi-level Hierarchies, and by creating SAP Standard Codes to fully harness the value of this SAP data.

From this point on, analytical engineers and data analysts that have a working knowledge of business processes may take advantage of these automations, and model and transform data into meaningful data assets within a modern data platform such as Snowflake and Databricks.

An important characteristic of the framework that promotes the idea of building, aggregating, and storing the generated SAP metadata, is in the form of a Meta data Knowledge Repository, the most prominent use of which is for developing Generative AI Analytical Assistants that aid analytical engineers and data analysts.

Enriched Tables

SAP base tables with enriched dimensions which can be re-used for downstream reporting

SAP Standard Codes Conversion

SAP standard codes such as SAP ABAP -Function Modules, Views, Classes, Currency Conversion, and T-Codes

Metadata Knowledge Base Repository

Convert metadata from all the above reusable components into a rich Enterprise Knowledge Repository that feeds Generative AI models that in turn power Analytical Assistants

Multi-Level Hierarchies

Data aggregation on various groups based on business defined Hierarchies such as Profit Center, Cost Center etc., with multi-level generation supported by the automation

Pre-built Re-usable Base Models

Analytical models characterized by joining enriched base tables, dimensions, and business friendly metadata, such as AP, AR, and GL from the FI (Finance) module

Let's see this in Action!

Accelerate the process of deriving insights from your SAP data

Apply Change Data Capture methods to land the SAP data into your landing zone and leverage the potential of indigoChart's automated solutions to generate reusable components that will empower your analysts to create robust data models for developing analytical data products.

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