Release of SAP Data and Analytics Advisory Methodology

Intro

These days, companies in all industries look for opportunities to use data to improve business operations or provide smart product and services to their customers. Still, the potential to gain value from data remains largely untapped as companies struggle to keep pace with simply accessing, storing and harmonizing the data in complex and unintegrated data landscapes. Many Business and IT leaders are looking for a structured repeatable approach to swiftly design and realise data-driven ideas.

SAP business applications create much of the transactional data our customers require for additional value creation. Also, with the announcement of SAP Datasphere, and its open data ecosystem, as the technology foundation that enables a business data fabric, SAP has underpinned the position as a leading provider of Data Management and Analytics solutions. Looking at the challenges our customers face in this area, SAP has recognized the opportunity to develop a methodology to guide our customers in this process.

Thus, we are pleased to announce the first version of the “SAP Data and Analytics Advisory Methodology” intended for our customers/partners. The purpose is to provide guidance in the design and validation of solution architectures for data-driven business innovations.

SAP Data and Analytics Advisory Methodology

The methodology represents an architecture approach based on standard TOGAF Architecture Development Method and is intended for enterprise and data architects to help execute their data strategy.

The structured process is accompanied by a

• data domain reference model to establish a common understanding of SAP data,
• a data & analytics capability model to better support the architecture definition and
• Data-to-value use case patterns and related reference architectures to accelerate solution architecture development.

The methodology also considers the organizational impact new data & analytics capabilities and solutions might have and how governance processes need to be adapted. Finally, it supports an Open Data Ecosystem consisting of related Third-party software products of Hyperscalers or SAP partners.

“SAP Data and Analytics Advisory Methodology” complements the already exiting SAP methodologies for the intelligent, sustainable enterprise:

• SAP Integration Solution Advisory Methodology with a focus on Integration and
• SAP Application Extensibility Methodology with a focus on Extensibility.

SAP methodologies for the intelligent, sustainable enterprise

All three methodologies aim to support our customers and partners and are provided free of charge. They follow a structured and step-wise approach for analyzing requirements and selecting appropriate solutions based on well-established and documented best practice.

The „Data Product“ as central architecture building block

The development of data-to-value solutions is based on a common framework of architecture building blocks that guides the definition of a tailored data architectures.

Common framework of building blocks for the composition of data architecture

In the center of this framework is a “Data Product”, a controlled dataset provided by a data domain that is composed of data, metadata and standard APIs to access it. Any data-to-value business scenario is based on this fundamental concept to provide the right data in the right quality and format, easily accessible for data consumers.

Customer use cases that focus on this data provisioning scenarios are called “Technical Use Cases” while those focusing on creating value out of data are referred to as “Business Use Cases”. The methodology is providing standardized use case patterns that are organized in categories that share architecture patterns or technical capabilities.

Overview of use case categories and patterns for data and analytics

The use case patterns are mapped to reference architectures that help to develop the right solution architecture quicker. The following example represents an SAP BTP-based reference architecture for the use case pattern “Analytical Apps”.

BTP reference architecture for analytical app

You can find the BTP reference architecturese in this GitHub repository

In this repository well publish the BTP reference architecture for our methodologies as well as other BTP based architectures:

Overview of architecture development phases

Our new methodology provides a structured process to develop a tailored solution architecture for data-driven business outcomes and is composed of four main phases:

Data and Analytics Advisory Methodology

In Phase I the objectives and scope of the investigation is defined and the as-is situation is analysed to identify data-to-value opportunities or improvement potential.

Phase II and III is executed for each business outcome that describes the measurable result. Phase II focusses on analysing the use cases related to the business outcome and define requirements for the data product and the solution architecture.

In Phase III, the consolidated solution requirements are mapped to the technical capabilities provided by the Data & Analytics Capability Model and aligned with potential software solutions. Also, the use case categories & patterns should be reviewed to check if related reference architectures, especially SAP BTP reference architectures fit. The results of these activities could be architecture options that need to be assessed and evaluated. The preferred architecture option could certainly be validated by a proof-of-concept to ensure feasibility.

Finally, Phase IV deals with the impact to organizational skills and data governance processes that might be affected. In a last step a timeline for implementation of the target architecture and organizational changes is created in the form of a roadmap (high level timeline) or a detailed project plan.

The methodology is rather comprehensive and needs to be tailored to the scope of the investigation. For example, if business outcomes are clear the focus should be on Phases II and III while further analysis described in Phase I is not necessary. On the other hand, if the focus is on the execution of a data strategy that encompasses a large data landscape and several functional areas then Phases I and IV should be managed comprehensively. Also, the adopter of the methodology can adapt the approach to fit the preferred project methodology (agile vs. classic).

What’s next

In case you are interested please reach out to sap-data-analytics-methodology@sap.com to get invited to our SAP Build Work Zone workspace to get access to the presentation and further assets like templates.

Please check also the detailed blog from Alexander Bange about: