Last Update – November 25: Further Blog and Video Links added.
Recently SAP launched SAP Data Intelligence, which transforms distributed data sprawls into vital data insights, delivering innovation at scale. Business innovation can be accelerated by using Business Content delivered by SAP and our key partners.
Business Content for SAP Data Intelligence is an important enabler for the following reasons:
- It is focuses on a specific scenario, industry, or line-of-business
- It provides an easy starting point, giving you a quick start so you don’t have to start from scratch
- It can easily be adapted for your specific 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.
Recently the product management team for SAP Data Intelligence sponsored the SAP Data Intelligence Content Sprint-at-Home virtual initiative. This initiative featured nine key partners.
The goal of the initiative was to provide ready-to-use custom Business Content packages that you can implement quickly, reducing the time required to develop and implement a new use case on SAP Data Intelligence. Existing business cases can be found at https://www.sap.com/dataintelligenceusecases
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 are excited to announce that we have recently completed the 90-day SAP Data Intelligence Content Sprint-At-Home initiative, and we want to share with you what has been developed and the related business scenarios.
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: Camelot ITLab
Blog – Scenario Description: Extreme data maintenance automation
Data maintenance can be very complicated when multiple forms and complex measurements are required to maintain data. Camelot’s solution uses extreme data maintenance automation combining data extraction and smart data validation with optical intelligent character.
Blog – Scenario Description: Intelligent Rule Mining
Industry: Supply chain
Intelligent rule mining extracts of business logic from available data in the form of rules. This enables you to reduce data inconsistencies by validating business scenarios against historical data.
Blog – Scenario Description: Next Best Automated Risk and Price Calculation for Travel Insurance Products
This solution enables insurance companies to offer products with the right prices based on risk level. It facilitates fast decision in making accurate and high-quality decisions that provide cost and resource savings.
Blog – Scenario Description: Optimize Asset Effectiveness and Downtime Prediction
Industry: Industry Machinery and Components (IMC)
This solution helps maintain complex and diverse assets. It helps answer common questions such as equipment with the highest risk of failure, what is the lead time for equipment to fail, what is the likely cause, as well as other key asset maintenance questions.
Partner: Inspired Intellect
Blog – Scenario Description: Optimize Order Fulfillment
Industry: Manufacturing, Supply Chain, Distribution
Delays in orders and shipments reaching their customer or target destination translates to a poor customer experience and revenue loss. This content leverages machine learning to pre-empt the risks associated with order/shipment delays, optimize logistics, improve distribution planning as well as proactive customer communications.
Partner: Keytree, a Deloitte Business
Blog – Scenario Description: Project Planning (blog coming soon)
This solution provides project planning data in a novel way by applying machine learning to a range of historical projects to learn relationships between tasks, resources (suppliers, human resources, machinery, materials) and the productivity of sub-tasks within a project.
By understanding what causes productivity to vary, decisions can be made to increase productivity and significantly reduce costs on large infrastructure projects.
Blog – Scenario Description: Optimization Solution for Manufacturing Inventory
Industry: Manufacturing, Supply Chain
This solution reduces the risk and cost of stocking too many or too few parts needed for the manufacturing of products. You can use this solution to generate an optimized and highly automated parts management policy to manage Supply Chain inventory planning.
Partner: PCS Beratungscontor AG
Blog – Scenario Description: Walkthrough Machine Learning
This solution provides full functional content and objects for “machine learning core enablement”. By using this content users will have ready to run demo including guidance for necessary first steps. The offering of includes coaching for SAP Data Intelligence and machine learning use cases.
Partner: Syskoplan Reply
Blog – Scenario Description: Activity Value Prediction
Activity reporting is usually part of every CRM-Implementation at enterprises in various industries. Activity reporting enables Sales Representatives or Performance Managers to evaluate the activities (visits, calls, mails, etc.) that have been executed at a specific customer.
What is often missing from activity reporting is the prediction of a monetary value of a recent or planned sales activity. Prediction models can provide forecasted values forecasted values of future activities. This solution addresses the prediction of the value of future activities.
Blog – Scenario Description: Cognitive Customer Service
Industry: Utilities, Telecom, Insurance
In industries such as utilities, the volume of calls received by the call centers is extremely high and leads to delays in resolution and affecting customer satisfaction.
This solution aims to apply machine learning to predict the reason for a customer call based on the pattern of past communication as well as propose top 3 ‘Next Best Actions’ that the call center executive can choose to recommend to the customer.
We are proud to have such a great collaboration with these essential partners. 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!