
The SAP Data and Analytics Advisory Methodology was introduced over a year ago (see blog). Today, I am happy to announce the new release of the methodology providing several enhancements and new content.
In this blog, I will provide an overview of the changes we are delivering with the new release.
The underlying approach has not changed and has been successfully applied in several customer engagements. We had some findings we reflected in the approach, especially in the following areas:
Phase III was adapted to streamline the architecture development process.
Existing gaps in phase IV we closed
Finally, we will use SAP Help to provide a detailed description of the procedures and tools used in the four phases of the methodology.
Updates in Phase III
We have adapted the architecture development process by introducing a “solution concept” from the SAP Enterprise Architecture Framework (SAP EAF) methodology. A solution concept diagram provides a high-level representation of the solution that is envisioned to meet the requirements of the architecture engagement. This helps to structure the target architecture in a generic way by using architecture building blocks. This conceptional architecture is the baseline for the following steps where each architecture building block is replaced by IT solutions to build the final solution architecture.
The following diagram provides an example of a solution concept.
Data & Analytics Maturity Assessment (Phase IV)
The methodology has a clear focus on developing a target architecture for data-driven business outcomes. Nevertheless, data governance and organizational aspects are equally important to be successful in becoming a data-driven company. Therefore, they need to be investigated at least at a high-level and the necessary actions incorporated into the roadmap created in the final step.
Therefore, we introduce a high-level data governance maturity assessment based on a far more comprehensive SAP Data Management Framework provided by SAP Lab. The aim is to identify data governance areas where maturity is insufficient and define appropriate actions that feed into the roadmap.
We have selected 15 focus topics from five dimensions (i.e., data strategy, data & KPI’s, data architecture, processes and organization) of the SAP Data Management Framework that are most relevant from a governance and organizational perspective. In our methodology, we propose to select and assess only those focus topics which are most relevant to the architecture context that was discussed in the previous phases.
Each selected focus topic is then discussed in terms of current and required maturity level. Five maturity levels exist ranging from “initial” to “data-driven”. Each level is described in the context of the focus topic.
Here is one example from the dimension “data strategy” covering the focus topics “data strategy management” and “data literacy”:
We will describe phase IV of the methodology in a subsequent blog, where we will provide more details on how to apply the maturity assessment and, as a final step, develop the roadmap to realize the target architecture and related actions.
It is important to mention at this stage that this maturity assessment is rather a generic investigation that is intended to provide a first perspective on data governance and organizational aspects. If the result of the assessment indicates that a deeper investigation is required, we refer to specialized frameworks from SAP or our partners.
New Data & Analytics Capability Model
We have reworked the Data & Analytics Capability Model to reflect state-of-the-art capabilities from the data & analytics practice. We also skipped a capability level to reduce complexity and make it more applicable in the methodology.
The capability model is organized around six data and analytics-relevant technology areas (blue areas) that contain the respective capabilities:
Data & Analytics capabilities in these areas can be mapped to IT solutions from SAP or other vendors and are the baseline for developing the target architecture as described in the previous blog.
We indicate other technology domains like AI, application development or process automation that can provide supplementary capabilities if required (grey areas).
The technical capabilities are complemented by organizational capabilities around data governance and architecture (orange area). The idea is to drive the discussion toward the capabilities required to successfully enable the value creation for which the target architecture is intended. This can be considered as part of the analysis of data governance and organizational aspects introduced in the previous section.
Update on Data & Analytics use case patterns and SAP BTP Reference Architectures
The methodology provides a “fast lane” in phase III to accelerate the process of developing a target architecture using SAP BTP Reference Architectures. This option exists if the customer use cases analyzed in phase II match the data & analytics use case patterns that are realized by SAP-centric reference architectures (see blog phase III).
We have reorganized the structure of those use case patterns that focus on data provisioning and call them “Data Foundation Patterns” (see bottom area of illustration below). We included the “Business Data Fabric” as an overarching pattern, a concept that was introduced with SAP Datasphere. It enables organizations to seamlessly access and interact with a semantically rich data layer regardless of data location or format. As it combines data management, integration and governance capabilities, the related SAP BTP Reference Architecture provides the complete picture.
The underlying use case patterns and reference architectures for Data Management, Data Integration and Data Governance) will be more detailed as they cover specific aspects of business data fabric.
I would like to point out that SAP now provides such Reference Architectures not only for Data & Analytics but for all relevant technology domains covered by SAP BTP, e.g.
• Artificial Intelligence
• Automation
• Integration
• App Development
SAP Reference Architectures are published in the SAP Discovery Centre. Here you will also find SAP BTP Reference Architectures that integrating Hyperscaler und partner solutions, confirming SAP’s goal to integrate into an Open Data Ecosystem.
Documentation of the methodology
The updated PDF presentation and PPT template of the methodology are available for download in our SAP Work Zone. For those who do not have access yet, please send an email to sap-data-analytics-methodology@sap.com. The methodology and related material can be used free of charge.
As with the other two SAP BTP methodologies, ISA-M and AEM, detailed documentation for the SAP Data & Analytics Advisory Methodology will be available in the SAP Help Portal soon. Here you will find a detailed description of the four phases with more examples and best practices.
Acknowledgements
I would like to take the opportunity to express my gratitude to all colleagues from SAP and our partners Camelot ITLab, Infomotion, PWC and IBsolution. Thank you very much for your support and for providing your expertise to us in the last months.
Special thanks go to:
Finally, I would like to thank PostFinance, the first customer that fully applied the methodology. They allowed us using some of their results as references.
Next up will be a blog on phase IV of the methodology where I will look into data governance & roadmap development in more detail.
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