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Last Update – April 26: Further Details (e.g. business case links) added.

Second SAP Data Intelligence Content Sprint Concluded

The SAP Data Intelligence product management team recently led a Global Program that focused on business scenarios and building content for SAP Data Intelligence. Moreover, the SAP Data Intelligence product management team and other SAP teams provided mentors for each partner to ensure close collaboration between SAP and our key partners. This Global Program was connected to the APJ One Packathon that focused on Business Content for SAP Data Warehouse Cloud and SAP Data Intelligence.

All APJ partners used SAP Data Intelligence systems provided by partners Dell and Red Hat.


Across the globe, our 10 involved partners brought us innovative business cases and built ready-to-use content for SAP Data Intelligence.


Business Content for SAP Data Intelligence is an important enabler for the following reasons:

  • It focuses on a specific scenario, industry, or line-of-business

  • It provides an easy starting point, giving customers a quick start so they don’t have to start from scratch

  • It can easily be adapted for specific customer requirements

Business Content on SAP Data Intelligence may include artifacts such as data pipelines, machine learning algorithms, operators, docker files, just to name a few content types.


The goal of the global initiative was to provide ready-to-use custom Business Content packages that customers can implement quickly, reducing the time required to develop and implement a new use case on SAP Data Intelligence. These and other business cases can be discovered at:


The partners provided the scenario based on their industry and line-of business expertise, developed all related pipelines, connection templates, machine learning algorithms, operator configurations, and any other specific content required to implement their use case.

Each partner solution can be deployed on SAP Data Intelligence. In most cases the use case will use multiple systems, for example SAP Data Warehouse Cloud, SAP HANA, SAP ERP, SAP S/4HANA, SAP Cloud for Service, SAP Business Warehouse, etc.

Each partner can work with you on adjusting the content to your specific endpoints and any customizations required for usage in your environment.

We would like to thank our partners for their active contribution in organizing and delivering  stellar and innovative business content packages.  Below is a list of all partner scenarios with links to blogs, business cases, podcasts, and more.


Partner Details

The content below is in alphabetical order by partner. Select the hyperlink for each scenario to learn more and connect directly with the partner.


Partner: Accenture

Scenario Description: Intelligent Manufacturing Safety Management

Industry: Manufacturing

In the Manufacturing industry, factories are equipped with large-sized critical machinery for daily operations.  The release of hazardous gases and electric damage incidents can cause smoke and fire. This makes worker safety an important concern for manufacturers.​ Current video analytics technologies are having less accuracy and can cause false alarms/warnings.

This solution combines SAP Data Intelligence with AI, deep learning, and  computer vision to enhance the accuracy of video analytics and will ensure the safety of workers.


Partner: Camelot ITLab

Scenario Description: Supply Chain Resilience Cockpit

Industry: Supply Chain

Business Case:  Supply Chain Resilience Cockpit

In today’s fast and volatile world,  it is in competitive supply chains’ very nature to be global and complex. For decades now supply chains are caught in a crossfire of minor to major disruptions. The Camelot Supply Chain Resilience Cockpit application analyses how external factors are impacting our supply chains and offers guided decisions to the users based on historical and current data points. Functionalities: ​

  • Regional route and sentiment analysis considering current, historical and external data points​

  • Risk analysis and classification of suppliers​

  • Integration to customer system landscape such as S/4 system ​

  • Guided decision making for orders considering historical and actual supply chain data taking new influential factors into account​


Partner: DataXStream

Blog - Scenario Description:  Intelligent Automation for purchase order processing

Video:  IA by DataXstream featuring iOC (intelligent Order Creation)

Video:  DataXstream OMS+ia

Industry: Wholesale Distribution

Podcast Let’s Talk Data Episode 46: Building DataXstream's Intelligent Automation with SAP Data Intelligence

Business Case:  Robotic Process Automation

Distributor receives purchase orders for materials from customers in files with different layouts and formats. The material descriptions used by customers often differ from what exists in the vendor’s system. Distributor manually processes each order request through manual data entry and material searching. On average, manual processing of each line item on an order takes a minimum of 30 seconds for an experienced sales representative, and up to 2 minutes for the less experienced. On average, a 10-item order would take anywhere from 5 minutes to 20 minutes.​

​The solution will reduce the amount of time to process orders of non-standardized formats by 80% with a chain of models that parse and predict text that is critical for creating orders, including customer and material information.


Partner:  Deloitte

Scenario Description: Intelligent Data Reconciliation

Industry: Cross-Industry

SAP S/4HANA implementations require project teams to spend excessive amounts of time and manual effort to oversee the ETL and reconciliation process of migrated financial data.  There is a mandatory reconciliation requirement of 100%-dollar value match for migrated data for successful go-live as well as to meet the compliance and auditory obligations. Manual effort leads to late detection of issues and inefficient use of resources processes elongating the cutover period with no automation.​

​This solution uses SAP Data Intelligence  to integrate data from multiple sources (legacy and target) and inject intelligence and automation to build a financial reconciliation tool and instantly generate reports as soon as financial data is extracted, transformed, and loaded into the target system. This increase in transparency will support early detection of issues in data quality or load programs, and subsequently support accelerated data migration reconciliation activities. ​


Partner: DXC

Scenario Description: Digital Transformation with Micro Services

Industry: Cross-industry

Business Case:  Automated Supplier Invoice Management

This solution leverages micro services for digital transformation to simplify invoice processing and detect and correct duplications.


Blog - Scenario Description: Maintenance and Reliability Intelligence

Industry: Utilities, Oil & Gas and other Asset Intensive Industries

Business Case:  Maintenance and Reliability Intelligence

Monitoring and managing assets in a cost-effective and timely manner are challenging organizations to implement effective and efficient processes and ensure high safety, reliability, and compliance within Asset Management processes.​

Process discovery and improvement have arisen as a popular industry topic that can be applied to Asset Intensive businesses. This solution will take traditional Asset Maintenance analytics and make it ‘process aware’.  A process map, powered by SAP Analytics Cloud and SAP Data Intelligence,  will enable several animated views, and present this together with a collection of Asset Maintenance KPIs to provide insights from a maintenance process perspective.​

This solution bridges the gap between pure Analytics solutions and professional Process Mining tools to enable an integrated management view of assets.


Partner: EY India

Scenario Description: Dealer 360

Industry: Cross-Industry

Dealer management is important for any business that relies extensively on dealers. Without the proper analysis of dealer data, it becomes challenging to manage the dealers with respect to various metrics such as margin, orders, scheme allocation, etc.​

​This solution helps businesses identify and segregate dealers based on profit margin, volume & value of goods sold, credit limit, collections, schemes allowed and the response to them. It can also help dealers with the right ordering quantity thereby improving their prospects.​

The solution uses SAP Data Intelligence and SAP Data Warehouse Cloud based multi facet recommendation engine for dealer to build suggestive monthly orders based on:​

  1. Historical orders from dealers​

  2. Inventory movements to push discounts and offers for slow moving/ aged goods​

  3. Market Basket Analysis of secondary sales data for providing insights to the dealer for their customer buying patterns


Scenario Description: Email-Classifier

Industry: Cross-Industry

Various departments in any organization often receive large number of queries through emails. The resolution to these queries often requires input from various sources which in turn leads to delay in response and affects the resolution time.​

​The solution uses Natural Language Processing (NLP) to understand, interpret and auto-classify the users’ emails into categories and auto-revert on these queries after compiling required information from different SAP systems. It also has the capability to route them to the relevant department for resolution or request missing information from the user for specific use cases. Straight through processing of emails and automation can reduce the manual effort as well as query processing time leading to an enhanced user experience.


Partner: EY Agility Works

Blog - Scenario Description: Asset Data Intelligence

Industry: Energy and Utilities

Business Case:  Asset Data Intelligence

Asset Data Intelligence is a solution developed by EY AgilityWorks, built on the SAP Business Technology Platform (BTP), that aims to automatically identify data quality issues in asset data. The premise of this solution is to automate detection of data anomalies through the use of artificial intelligence (AI) and integrate the results into business workflows. The intended outcomes are to accelerate time-to-value over typical rule-based approaches, to support reactive and pro-active data governance, and to drive new insights through the power of advanced data processing and AI. The primary beneficiaries of this solution are organizations that use the Plant Maintenance module in SAP S/4 HANA or ERP to store their asset master data. Through a flexible data processing framework and the capabilities of SAP Data Intelligence, the solution can be extended to other data domains in SAP and non-SAP sources.


Partner: Perfekt

Blog – Scenario Description: Mining Industry Predictive Asset Management

Industry: Mining, Oil & Gas, Energy

Podcast:  Let’s Talk Data Episode 40:  Assets Information Management and Analytics For Mining

Business Case:  Mineral Processing Plant Productivity

Business Case:  Mobile Asset Productivity and Safety

Perfekt developed Asset Information Management and Analytics (AIMA) to address challenges in asset intensive industries such as mining, oil and gas, and energy utilities. mining industry specific challenges. It also applies to oil and gas as well as energy utilities. AIMA helps users utilize equipment generated data (IoT) and to provide visibility on equipment performance, optimize maintenance schedules, reduce down-time and safety incidents and to improve productivity.


Partner: PWC Australia

Scenario Description: Plant Intelligence

Industry: Utilities, Telecom, Insurance

Smart Factory is a modular and customizable manufacturing analytics suite, which uniquely combines existing production and IT systems and provides pre-configured and pre-built applications built on the SAP Business Technology Platform.   The Smart Factory helps you to:

  • Boost productivity, create capacity, and help protect quality and safety on the shop floor. ​

  • Enable clients to connect and aggregate all required sources of data in real-time (machine, material, labour, flow). ​

  • Help stakeholders to free up capacity and focus on what matters. ​

  • Analyze data to detect performance issues and provide insights to resolve them.


Partner: VASPP

Blog - Scenario Description: Commodity Price Prediction

Industry: Cross-industry

PodcastLet’s Talk Data Episode 47: Commodity Price Prediction with SAP Data Intelligence

Business Case:  Commodity Price Prediction

The price of commodities is influenced by several ​ factors globally . The businesses who deal with a particular commodity need good forecasting solution which enables them to have a foresight on changes in pricing.    ​

The solution aims to include those factors along with the price of concerned commodity and forecast the future price based on the previous patterns. This is expected to save huge amount of cost spent for the businesses who are in the Commodity Market directly or a receiver of the commodities for their production operations.



We are proud to have such a great collaboration with all the partners who developed content, as well as Dell and Red Hat who provided infrastructure, expertise, and systems.   We look forward to continuing our co-innovation with partners to deliver relevant Business Content for SAP Data Intelligence.

With this Business Content for SAP Data Intelligence, you can easily get started driving innovation in your business. Please reach out to your preferred partner and get started with Business Content on SAP Data Intelligence!