In this case, system learns from your behaviour and gets better over time. ML algorithms are for generic application like conversationsor for a specific use case.
Conversation UX (Generic service): This technology provides one more way to interact with system. It takes away friction of UI limitation. This technology relies on 'Voice recognition', 'Text recognition' and 'Image recognition'.
Use case (UX): SAP products using this technology are SAP CoPilot or SAP developed Chabot. SAP CoPilot is directly integrated to SAP Fiori Launchpad. Users can chat with the system. Cool! I see this as a low risk implementation that can be implemented organization wide.
Predictions (Specialized services): In this use case one needs to feed model with tons of raw data and outcome. Once model is ready, system can predict outcomes. If business use case is specific/unique (Fraud detection, Predictive Maintenance) then you may need to plan time for 'teaching model' before you can use this feature.
Use case (SAP EAM): Predictive maintenance where ML algorithm digests all post historical data and predicts future failures. This is only viable where cost of repair is much higher than cost of failure. e.g. Critical components of chemical plants, power plants, airplane etc.
Use case (SAP MM/PP - MRP): Predictive models to predict material shortage based on sales orders, production order and historical trends.
IoT is network of all sensors like voice, text, images, temperature etc. These sensors collect data and feed into an aggregator. Most of the time IoT will be implemented in conjunction with ML and Big Data technologies.
Use case (SAP EAM/SAP MM): Vehicle data tracking: Transportation company tracking vehicle GPS locations to optimize vehicle utilization is a typical business case. IoT framework can be implemented to collect and digest vital sensors data from engine, tires, transmission system and utilize to predict vehicle failure. In enterprise asset management (EAM) 'Digital twin' is based on this technology as IoT data can be shared between asset owner, operator and service provider.
Use case (SAP PP/SAP PM): Integrate machine sensor data to collect machine's utilization. Link production work centers and maintenance work center using sensors. When machines is not working efficiently adjust production work center capacity and generate maintenance notification to report this issue. Collect and analyze data using analytics/ML.
This is geared towards visualizations of data. SAP BI, Cloud analytics etc. can be used to implement analytics solution for the organization. More emphasis is on building self-serve models and dashboards that can be consumed by a wide range of users.
Use case: Digital Boardroom dashboard or various KPI dashboard that aggregates data from multiple sources. Display data in visually appealing and interactive fashion.
Data intelligence connects, aggregates, and anonymizes your data to prepare it for commercial consumption. This allows user to monetize on their data to generate new revenue stream, improve organisational efficiency or provide better customer experience.
Big data refers to large and complex data that traditional applications are unable to process. Big data technologies are application where data velocity and variability is high as well i.e. combination of structure data (database table) and unstructured data (text feeds). Big data technologies are used to collect different types of data and to provides tool (SAP Data hub) to generate insight in this data set. This data hub can integrate SAP and non-SAP data in one data set. This data can be used for aggregation (to feed to ML algorithm) or to the visual dashboards.
This is a gateway to explore distributed ledger or block chain technology.
This is SAP’s service for providing distributed ledger functionality to the customers. In my opinion, this technology is going through the hype cycle. It's hard to come-up with a commercially viable business assess in an enterprise processes managed in today’s SAP footprint where blockchain technology can be easily integrated. Blockchain will be more applicable in 'intra organization’ scenarios.
My simple mind can’t see application of distributed ledger in SAP finance world as Block chain is not a 'double entry' ledger.
Use case (SAP MM & SAP GTS): Pharmaceutical product tracing through global supply chain or Citizen's data managed and shared by various level of the government.
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