Background

While there is a lot of buzz in SAP world about SAP Datasphere, a few customers may be concerned about their past and future investments in SAP Data Warehouse and SAP Data Intelligence Cloud. According to SAP Datasphere FAQ, SAP Data Intelligence is going to stay until SAP Datasphere is enriched with all features of DATA Intelligence or may continue as separate product for customers who want a dedicated infrastructure per customer.

Technology vendors and consulting companies claim that data is the renewable fuel and currency, but data has officially become the nightmare for many customers as data moved from pen drives to metaverse as vendors offered a plethora of hybrid structured and semi/unstructured data cloud tools for customers.

While few customers going on an SAP Cloud Global template roll outs are using this as an opportunity to embed overall enterprise data model initiatives into the business case, many of the customers aren’t defining a business outcome driven end to end data strategy covering 7 pillars and describing the roles and responsibilities between multiple teams before they start on the SAP Cloud Transformation Journey.

 SAP Data Sphere – Connecting 10 data pillars

While SAP Data Sphere is more focused on sophisticated analytics currently, I think we can extend this product or provide plugins in future for end-to-end data life cycle orchestration as it already provides plug ins to Collibra, Data Robot, Confluent, Data bricks etc for enabling prescriptive analytics powered by hyper automation after transitioning to SAP S/4 HANA Landscape.  SAP Data sphere can bring both business and IT teams on same platform with predefined content to design and deploy end to end SAP Data Life Cycle Services to get data right during interim, target and after target states.

1. Data Discovery and cataloging
2. Data Security
3. Data Archiving
4. Data Retention and Storage
5. Data Cleansing and Quality
6. Data Migration and Reconciliation
7. Data Governance and Compliance
8. Data Federation, Streaming and Lake house structured and semi/un-structured Analytics
9. Data Integration leveraging blockchain, graph etc
10. Intelligent Data Operations powered by ChatGPT

Embedding Business Data Fabric on SAP S/4 HANA Transformation Journey

Data Lead is often always held hostage and held under the gun point for not delivering within time scale and budget. We have seen a lot of programmes that goes into deep red as customers don’t look at data from end-to-end lens as there are not many architects with broader data skills covering 10 data pillars and the teams work under silos without clearly defined roles and responsibilities deliverables and activities between Global and Local Business, SAP Functional, Legacy, SAP Technical teams. To add salt to the injury of starting SAP S/4 HANA transformation journey without an end-to-end data strategy, there are not a lot of resources who has end to end data skills and vendors are releasing new products but not training enough people every quarter.  I won’t be surprised if data architects who has broader skills across 10 data pillars across various sap and cloud products charge same salary as the prime minister of our country due to shortage of supply over demand in the future.

As SAP Datasphere matures, we hope that we will be able to embed SAP Datasphere business fabric on cloud transformation projects not just for analytics but also use it to bridge the 10 data pillars to build a data factory that brings end to end data life cycle together across geographies. The below approach and governance model below  provides a foundation view on how we need to bring the 10 data pillars and teams together to design and execute an end-to-end data strategy on S4 HANA journey. It will help customers to start looking at data holistically and creating collaboration and governance bridges between various data teams on projects like the SAP BW Data Bridge and Collibra in the SAP Datasphere.

3 Data Stories to build a business fabric of data

Lastly let’s gets 3 data stories that applies to all 10 data pillars in our hearts and minds:

1. Data is not an IT story; it is a business story and is driven by business. Senior Business Executives should own and take accountability for data story and be executive sponsors for data initiatives and governance boards.
2. There is no perfect data model, plan and data design and data quality. We iterate the plan after every mock load to rank, measure and improve data business priority and by profiling data early.
3. Data dies in silos, we need to look at data from an end to end perspective across 10 data pillars and tie it to a business outcome.

What is your thinking this in space? I like to hear your thoughts on this!