Enterprise Data Analytics & Data Management: A reference architecture

Before explaining how a specific SAP analytics or data management product can address a dedicated problem, it is quite important to understand the overall problem using a product agnostic approach. A reference architecture typically helps in the identification of areas and business pain points that need to be addressed. Together with a world wide team of SAP domain experts for analytics, data warehousing and data management we’ve created the below content in 2021.

Target Audience

Data Management & Data Analytics architects from customers and/or services partners that are responsible for technical environments used for enterprise wide analytics & data management solutions.

The below content provides a summary of the content that was created. Detailed documents with descriptions of all capabilities, the use cases and their relations exists. We are happy to exchange and discuss them with you in a 1:1 conversation!

If you think about Enterprise Data Analytics & Data Management as a DOMAIN, this can be broken down in the following 5 SUB-DOMAINS.

Reference Architecture Block Diagram

Data & Analytics

The Analytics sub-domain is the process of discovering, interpreting, and communicating significant patterns in data.  Quite simply, analytics helps us see insights and meaningful data that we might not otherwise detect.

The Data Warehousing sub-domain is the storage layer for any analytics solution. It is designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse combines data in an aggregate, summary form suitable for enterprise wide data analysis and reporting for predefined business needs.​

Data Management

The Metadata Management sub-domain provides an end-to-end process and governance framework ​for creating, controlling, enhancing, attributing, defining and managing ​a metadata schema, model or other structured aggregation system, either independently or within a repository ​and the associated supporting processes

The Data Quality Management sub-domain contains capabilities related to Data Quality Dashboards, Data Analytics, Data Processing, Data Governance and Data Quality Services. Any data quality solution is comprised of one or more capabilities from this list.

The Data Integration sub-domain comprises the practices, architectural techniques and tools for achieving the consistent access and delivery of data across the spectrum of data subject areas and data structure types ​in the enterprise to meet the data consumption requirements of all applications and business processes.

Analytics Sub-Domain AREAS

Any analytics solution is comprised of capabilities that can be grouped into one of the 8 areas outlined here. A detailed Analytics Reference Architecture document describes all areas and capabilities in detail.

Data Warehouse Sub-Domain AREAS

Any data warehouse solution is comprised of capabilities that can be grouped into one of the 6 areas outlined here. A detailed Data Warehouse Reference Architecture document describes all areas and capabilities in detail.

Metadata Management Sub-Domain AREAS

Any meta data management solution is comprised of capabilities that can be grouped into one of the 5 areas outlined here. A detailed Metadata Management Reference Architecture document describes all areas and capabilities in detail.

Data Quality Sub-Domain AREAS

Any data quality solution is comprised of capabilities that can be grouped into one of the 6 areas outlined here. A detailed Data Quality Management Reference Architecture document describes all areas and capabilities in detail.

Data Integration Sub-Domain AREAS

Any data integration solution is comprised of capabilities that can be grouped into one of the 6 areas outlined here. A detailed Data Integration Reference Architecture document describes all areas and capabilities in detail.

Reference architectures are usually something monolithic and static and it is hard for users to understand what this is all good for. A set of sample use cases help and show how the different building blocks and capabilities work together to support specific user roles.

Below is an overview of use cases which we’ve identified as “most relevant”. Each use case is documented in detail including user roles and flow diagrams.

16 (most relevant) Analytics & Data Warehousing Use Cases

Based on the Analytics Reference Architecture and Data Warehouse Reference Architecture a number of use case have been outlined and defined in detail. Each use case also contains a mapping to relevant SAP products & solutions.

17 (most relevant) Data Management Use Cases

Based on the Data Management Reference Architectures a number of use case have been outlined and defined in detail. Each use case also contains a mapping to relevant SAP products & solutions.

The aim of this set of documents is to provide common definitions and a common notation for all capabilities that are relevant for data analytics and data management solutions. It is for sure a working set of documents that may change over time.

Call-to-Action

Please provide feedback about your view and experience related to reference architectures for analytics and data management! Either contact me directly or post or comment below.

Additional Assets

Additional assets with regards to data architectures, data management, big data, and analytics can be found here:

Quick briefing decks for new database and data management trends

A future-proof Big Data Architecture

Does Your Organization Have a Data Strategy?

Digital Transformation and the Data Warehouse

Master Data Quality Management with SAP Master Data Governance on SAP S/4HANA 1909