Data + AI - How does the emergence of Unified Data Platforms help SAP customers shift from operational to intelligent ways of working?

By: Mihir R Gor, UKI SAP CTO, IBM and Allan Coulter, Global SAP CTO, IBM

Date: 30 July 2020

5 min read

Today we are inundated with statements like "Data as the new Oil", "Data-Driven Transformation", and the emergence of Big Data. We’ve always had data, so what’s different now and why is it so fundamental to the strategies from the likes of IBM and SAP, as we talk about the Intelligent and Cognitive Enterprises? 

At IBM, we help our clients understand the value of data in the context of SAP and beyond SAP. We can show how Data unlocks new offerings, new trading relationships, new client-centric outcomes as well as the shift to intelligent processes, embedding automation to shift work from humans to machines. 

We start by enabling our clients to define their Business Architecture (the Need), a clear and coherent Data Strategy (the What) which shows Data Value Maps with IBM Component Models, Industry Data Models and Data KPI’s outlining the value of data as a key resource in defining and delivering the business strategy and then providing the core Data Capabilities (the How) starting with Technical Architecture and Data Reference Architectures. 

Ultimately, the approach is to turn “Data into Information” into “Insight into Action”. This fundamentally redefines the ways of working (experience) for the consumer, be it an employee or a customer.

Data is everywhere but how do use it effectively within the context of the Intelligent Enterprise? As we see clients move from their traditional ERP into a digital core with SAP S/4HANA, the way in which they can start to consume data both within their enterprise and beyond goes through a major step-change.   

Yes – you can drive AI insights from classical SAP ECC6 (on AnyDB or even Business Suite on HANA) with, for example, a corresponding SAP BW (on AnyDB or BW on HANA) instance as the traditional model of separating OLAP and OLTP processing for performance and data management reasons. However, with SAP S/4HANA, with either embedded analytics or complementary SAP BW/4HANA or even SAP Data Intelligence and SAP Analytics Cloud, customers have more elaborate tools with richer capabilities to derive insights faster, deeper, and at scale than ever before. One word of caution: it is important to understand that poor data quality and human-powered processing can be rate-limiting factors hence the need to invest in real data quality initiatives supported by AI-powered by data to augment or indeed shift to Intelligent Process Automation to fundamentally remove these areas of friction.

When we talk about capabilities, we need to have solutions for the 5 steps outlined above:

• Data Value – where we align Data with the Business Process Capabilities and new Business Models e.g. Data Monetization, defining Data Value Maps, and defining the sources of data that are needed to provide the value-based outcomes.
• Data Quality – where we assess, profile, and clean Data to enable better quality Master Data that underpins our systems of record and insights. With Data orchestration, we will understand where data needs to move from/to using data Migration and Transformation tools, while also applying Data Governance and Security policies on top. 
• Unified Data Platform – where we understand how to ingest data into target applications, using the right mix of Data Replication and Virtualization technologies. Data Storage solutions need to be in place alongside metadata management to ensure data models are understood. Data needs to be qualified with data curation rules. Data needs to be cataloged as part of the Data model. New sources of data need Pipeline Management and transformation needs data Engineering scenarios.  Finally, Data Technologies underpin the tooling for doing all of these tasks before you can consume data through the Data Analytics
• Data Analytics solutions include SAP Analytics cloud for advanced analytics, SAP BW4HANA for operational reporting, and SAP Digital Boardroom for data visualization.
• Data-driven experience – this is where we use technologies to train the Data with Data Science, apply Machine Learning Algorithms so that Artificial Intelligence can drive Data Insights, augment Human intelligence to drive Intelligent Workflows through rich data consumption experience. 

Looking forward, we see how the establishment of Industry 4.0, Edge Computing, 5G, and IoT embedded in products connecting users to producers from the plant floor, interconnected networks of distributed devices, and even wearable technologies, enterprises can really start to consume streaming data analysis in near real-time to enable them to assess, optimize, visualize, predict business outcomes using the combined power of high-performance transactional ERP systems (such as SAP S/4HANA) and analytics applications (such as SAP BW/4HANA, SAP Analytics Cloud and SAP Data Intelligence) operating on large data lakes that hold vast amounts of heterogeneous data. 

We can already see senior leadership in these Enterprises being able to visualise business outcomes, for example by using SAP Digital Boardroom capabilities, to drill down to item level – with fine data granularity – a lot size of one – with increasing levels of precision that drives insight, creating new data relationships and business model opportunities. 

What this does is to provide a superior User experience from operational insights to advanced planning and predictive capabilities. This means customer relationships can evolve from a transactional model into one based on deeper insights, engagement, and trust, where AI and Machine Learning is embedded into the many business processes – this is what we call Intelligent Workflows.

Since Data is emerging as a fundamental asset in driving shifts in business strategies and ways of working for our clients, where New Board Level positions like the Chief Data Officer, are being created to own how data is used and turned into Intelligence. 

So, let us see what is different now for the CDO. If we take three on-demand data sets – let's say, from IBP, Manufacturing Plants/Throughput, and Machine Sensor Information, then we would expect to see large volumes of data.  By joining up the relationship between IBP, Manufacturing Plants, and Sensors, we can turn data into information. Now, when we combine these data sets together, then start using AI, we are able to turn this information into Insight, and this helps us understand the optionality of changing Supply Chain plans based on, say, a reduction in Manufacturing Plants Capacity because sensors may be highlighting errors in production.  This insight can be visualized in real-time enabling decisions and action to be taken. This “Insight into Action” based on the precision of AI doesn’t just highlight the issue, but also recommends the course of “next best action”, and indeed higher precision means that this can be an autonomous process, as the recommendation can be automatically enabled and implemented. 

This is one example of the innovation we bring to Data in the context of the SAP ecosystem founded on S/4HANA.  We have many more offerings in the space, so for more information, reach out to me on LinkedIn or [email protected] and IBM Global SAP CTO, Allan Coulter [email protected]