Hi and welcome to unit three of the SAP Data Intelligence for Enterprise AI course.
In this unit we will talk about some of the SAP Data Intelligence use cases,
and will share some of our customer success stories. The idea of evolving into an Intelligent Enterprise
is to be able to improve your business processes, and make the world a better place,
to focus on what really matters, and replace the repetitive works with the bots,
to increase the accuracy, to reduce the cost, and to increase the profitability
and customer satisfaction. SAP Data Intelligence addresses some
of the biggest challenges that companies are dealing with on the path to become intelligent.
Customers have stored their data in silos, some in the cloud, some on-prem.
They also have to deal with different types of data, maybe some structured data saved in their HANA system,
and some unstructured data, such as time series data, saved in Amazon S3. They need to have a single platform
which can natively connect to all their data sources, allow them to look at their metadata,
lineage, and some statistical information, allow them to cleanse and enrich their data,
and run their processes on top of them, while using ML and AI technology.
And that is what SAP Data Intelligence is about. Wherever you are in your journey,
if you need to understand and manage all the data residing in your landscape,
if you want to create a business intelligence dashboard, or visualize your data,
if you want to run some machine learning algorithms on top of your data, having the flexibility
to choose between R or Python or use some Leonardo services, if you have already run some ML experiments
in your Jupyter notebooks, and now want to deploy the result for production,
or you want to automate some work using Intelligent RPA, or to add some Conversational AI to your user interface,
SAP Data Intelligence is a solution for you. It facilitates your evolution from immature
to true data intelligence and enterprise AI. While working with our customers,
we have seen four main patterns, IoT ingestion and orchestration,
data science and machine learning data management, intelligent data warehouse,
and the last pattern: governance and data cataloging. Let's go through all these four different patterns,
and show you some customer scenarios. This process, as you can see here, was developed
as part of S/4 Business Partner workflow to calculate a vendor's risk.
We use the SAP Data Intelligence OpenAPI operator to kick off the process when adding
a new business partner to the S/4 system. As you can see, the process runs several parallel pipelines.
One pipeline tries to retrieve the risk score from the SAP Credit Management app
while another pipeline executes a sentiment analysis on social media data from the Twitter stream
for the business partner, and performs text analysis to learn more about the business partner.
The other pipeline, on the other hand, collects the supplier risk information
from their Ariba system, while the Data Quality Management, microservices for location data
matches the address with the one retrieved by Google search to validate the partner's address.
The results of all these pipelines then get combined, and the overall risk score is calculated in Python,
which stores the result in S/4. Finally, the result will be fed to the SAP Analytics Cloud
for dashboarding and executive reporting, and further analysis of the overall trend for risk.
In summary, the customer's challenges here were mainly the inability to integrate
and correlate external data, and to manage and operationalize the parallel business processes,
which is made easy by SAP Data Intelligence. The second customer story that we will discuss here
was developed for KAESER KOMPRESSOREN. Kaeser's main goal was to provide air as a service
to its customers. Air compressors built by Kaeser are in constant use.
Proactive field support helps ensure customer satisfaction, but truck rolls are expensive,
so gathering sensor data remotely from the Internet of Things might work
to minimize on-site visits, but only if that data can be processed
and combined with customer data from existing business systems.
If it could interact efficiently with all this data, Kaeser would be able to seize new opportunities
to meet customer needs for products, services, and responsiveness around the world.
Here you can see a better view of the workflow developed for Kaeser.
On the one hand, Kaeser had its own compressors and control unit installed on the customer site,
and on the other hand, Kaeser's data center and SAP Predictive Maintenance and Service were developed
and stored on S/4 and C/4HANA. By brining all this data together, the customer
was able to work with a single automated system to manage their supply chain
and optimize data ingestion from remote data sources to reduce IoT data ingestion time from days to minutes.
They also used machine learning to optimize on-premise equipment maintenance, lower costs,
and to scale the operations in response to new customer demands and new geographic opportunities.
The third story we're sharing here is a predictive quality use case.
In this particular customer example, SAP Data Intelligence assists the detection of products of low quality.
The raw materials were going through the manufacturing processes
and, as part of that, the molding press. At the same time, the IR cameras were taking images of the material,
and sensors were capturing the pressure and temperature. The idea was to combine the information extracted
from the images, and the temperature and pressure measurements captured from the sensor data
with the ERP data, which had the information about the vendors who sold the raw materials
to the customer, to detect quality deviations in the end products.
A machine learning model is used to evaluate several parameters and improve quality
of the detection process by combining three data types. That way they were able to reduce waste
and rework, due to a higher accuracy of quality checks and early issue detection, due to solution performance.
As you can see in the workflow, we were able to integrate and process the images,
the sensor signals, and the ERP data with one single tool, across multiple locations.
One pipeline, executed in the cloud, enabled the customer to run the image feature extraction
on Hadoop, using their Python OpenCV libraries. At the same time, we were able
to build another pipeline that ingests and analyses the streaming data coming
from the pressure sensor, integrating with Apache Kafka. And finally, a third pipeline is correlating the outcome
of the previous two pipelines, joining it with the material master from the ERP,
and feeding a predictive analytics algorithm, executed in the HANA PAL engine.
This is how we have built the final smart quality application.
A key advantage of this approach is that the data scientists that were working
on the image processing model had the flexibility to use the open source tool of their preference,
for instance, Python, in this case, on their cloud data lake,
and that we didn't have to centralize all the data in HANA. We only moved the high-value data to HANA,
after the local processing had been done close to where the source data resides, which is HANA.
The last story we will talk about is the predictive maintenance
in a renewable energy company. The challenge was to predict wind turbine failure
to enable predictive maintenance and optimize wind park performance.
Before, predictive models in R have been used based on CSV files.
With SAP Data Intelligence and IoT data, wind turbine data can be ingested continuously
for continuous prediction, for example, for gear oil temperature.
If there is a significant mismatch between predicted gear oil temperature
and measured gear oil temperature, this is a hint for either a defective gear
or a defective sensor. In case of a defective gear,
this is a very early sign, and the gear can be repaired
before there is a complete failure of the gear. Like this, the repair costs are much lower,
and also the wind turbine outage time can significantly be reduced.
Let's look at a demo. This is the SAP Data Intelligence launchpad.
We will talk more about this in the future units. Let's click on the Metadata Explorer
and look at the turbine data that is saved in SAP, in the semantic data lake
of the SAP Data Intelligence. As you can see here,
you can click on different column types and then, for instance, look at the top distinct values
of a particular column, then row counts, the percentage
of that value, and then you can also define, for instance, how many rows you want to see,
and look at all the columns that you have. After you have your data in your Metadata Explorer,
you can go to ML Scenario Manager, and there you can basically use your Jupyter notebooks
to run some experiments. For instance, looking at your dataset
after making some changes to it or, for instance, plotting the wind speed
from the dataset, or, for instance, looking at the correlation between different columns
and different features. After you're happy with the PoC that you developed,
or your experiment, you can deploy that on the pipeline modeler, and this is a pipeline modeler
as you can see it. So there are different operators
which are developed for you. You can use one after another
to train your model and deploy your model. For instance, in this case, we use Python3,
and here you can basically run your training model, and then submit the KPIs to your ML Scenario Manager,
and also save the model that is created as a result of your training in your ML Scenario Manager.
That way you can manage everything in one single place. And you can also manage the deployment
of that model from the ML Scenario Manager all in one single place.
You can also connect to SAC, which is SAP Analytics Cloud,
using the SAC operator, and there create a dashboard and then visualize this result
that you have created. So for instance, here you can see the predicted
versus actual gear oil temperature, which, as we explained, could be used for the early prediction of a failure,
which can basically decrease the cost of repair. Thank you for joining this unit
and hope you enjoyed learning about some of our customer use cases.
In the next unit, we will introduce you to the SAP Data Intelligence capabilities
for data scientists. Good luck with your self-assessment test.