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December 10, 2020
We are an early adopter of [SAP] Data Intelligence (DI), for which we are pioneering use cases with SAP as a partner company. Our broad goals are to increase transparency and reduce risk for renewable energy investors. Specifically with with DI we can set up real time monitoring pipelines using Machine Learning models that continuously compare historic power generation asset performance with their current performance. This provides early under performance warnings to the owner/investor so they can take action to avoid excessive costs due to mechanical failures or other equipment down times.
- It integrates well with our current ecosystem of SAP products, like HANA.
- It provides end-to-end machine learning operations, with tools for the complete model life cycle.
- It has a simple user interface for novice users, with complex tools also available for power users.
- It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
- It can run in the cloud, no on-premise software management needed.
- Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
- SAP is very actively developing and improving it.
- The administration tools are still in development, currently SAP must be contacted for most admin tasks.
- Updates sometimes introduce unwanted instabilities.
- The documentation and examples use cases are still being written and need to be expanded.
December 04, 2020
We used SAP Data Intelligence to build Intelligent Asset Maintenance solution. In this current pandemic situation , clients are more focusing towards contact-less manufacturing and maintenance. We built a solution to enable machine failure prediction , anomaly detection in machine performance to proactively maintain the machine. We utilize Data orchestration and machine learning component of SAP Data intelligence product to build this solution.
- Data orchestration from multiple data sources
- Machine learning capabilities
- Containerize architecture to run on hybrid cloud
- Scaling requirement to handle large data
- Limited in-build machine learning features
- Need more stable product
March 02, 2021
We use this platform for our customers who manage a lot of data and need to integrate different data and process data quickly. Everything is done under the cloud and quickly. The complexity of big data and access to the information needed in a short time is crucial, as is security and speed for us and our customers. This is an all-in-one platform. Complex content and data management is possible through the SAP intelligent platform. It is the most important part of serverless and can process information quickly via Cloud. You are not alone with the Artificial Intelligence of SAP Data Intelligence; you can manage all part of your business. Connecting to all data sources and it automatically indexes all data assets.
- Powerful and very fast in processing big data under secure cloud servers
- Merging and refining and easy transformation data.
- Machine Learning performs very well but can still grow and deliver better results with the help of artificial intelligence
- It is still a fledgling platform that can grow well. I expect better and more professional integrations on this platform.
January 01, 2021
SAP BI/BODS, SAP LUMRIA, SAP CRYSTAL REPORTS
- AI-powered data extraction Software, Powered ML Engines that can transform your structured, unstructured, and streaming data into useful business insights.
- GPU support for inference on ML models for your one premise installation.** BYOM**
- The mode-option is for storage types that supports ADL or HDFS. Object stores such as S3 and SDL are not supported to append operation on storage level.
- It gives opportunity to utilize OCR, ICR, IDR technology to support the business needs
- Enhance ML capability between on-premise and cloud version.
- The capturing of pure voice records and voice forms similar to other businesses forms of communication such as (email, web forms, fax).
- Data integration services with other ML tools are more complex. Workflows could have been more simplified.
December 16, 2020
The administration I work for uses SAP Data Intelligence. The team uses it for data exploration and to provide insights from the data warehouse.
- Can use a wide range of SAP support like SAP HANA and languages like Python
- Its easy and efficient to use
- Eases the whole process from data prep to creating model
- Too many load issues
- Can do better in terms of reporting functions; some features for data manipulation could be made better
- Can improve customer support
December 14, 2020
Our clients belong to the energy and utility sector. They are providing services in three countries. Businesses have shown interest in resolving customer-facing issues by proactively capturing real-time data from Smart Energy Meters which were installed in residences and with corporate consumers. Using Kafka integration in SAP Data Intelligence, we are able to process our data with ML library to forecast system vulnerability.
- Numerous packages to build business specific models
- It supports telemetry data to process and organize for analysis
- Well-performing batch processing pipelines and useful data cleansing tool
- Need more learning resources to dive deeper into the application to provide more business problem resolutions
- Application cost is high
- When it comes to bug fixes, it takes a long time to locate the issue
December 08, 2020
This is one of the best data intelligence tools that we can use in our organization. It is a robust software with a good simple interface, SAP Data Intelligence significantly improves all our business processes with its functions of data visualization, data transmission, metadata processing, monitoring or simply with the tools for data pipelines to achieve a better data integration.
- It is a robust software with a good simple interface.
- Data visualization.
- Simple tool for better data integration.
- Costly.
- Integration layer is complex.
- Stability on SaaS version is not as expected.
December 08, 2020
SAP Data Intelligence has been used specifically within my department as a means of predicting certain costs of a claim. This has been an on-going project taking data from an OCR engine which reads incoming documents passing details to a variety of databases and SAP in order to predict those claims as possibly fraudulent claims or high value claims and pass this information to different departments via report for them to "keep an eye on."
- Easy to pick up.
- Tools for data cleansing.
- Able to use existing learning models from the business.
- Operates on cloud.
- Not made for working with others.
- Debugging is needlessly complicated and obfuscates issues.
- Documentation could use more examples to highlight points.
October 22, 2020
We are currently conducting a feasibility study to which extent DI is a candidate for a company wide data integration platform. The current focus is seamless and simple data source integration in SAP and non-SAP environments. After extraction relevant data is stored on a DI based data lake for consumption of analytics and reporting applications. Data lakes are kept up to date by real time delta updates from the sources.
- Integration with SAP sources
- Real time source data access
- State of the art UI and tooling
- Enterprise readiness features
- Overall solution stability
- Reusables for data lake management
- More detailed monitoring layers for external access
November 29, 2020
We have used [SAP] Data Intelligence for internal learning purposes in order to build up know-how within our team. For this purpose, we have re-implemented an existing showcase using [SAP] Data Intelligence.
- Provides lots of standard operators and default pipelines
- Good Python notebook integration for data discovery
- Graphical UI to develop pipelines reduce amount of code written
- Better documentation with examples of how to use connectors (i.e. what input/output must look like)
- Debugging functionality required to understand where pipelines fail and how data looks at that point
- SAP libraries (e.g. python hana_ml) should be STANDARD and pre-installed to avoid dockerfiles and other workarounds
- Dockerfile execution takes long times without giving a status where it is failing/held up
- HANA Read/Write Table operator throws TLS errors which are unlikely when HANA connection generally works
November 29, 2020
SAP Data Intelligence was used within our team as part of a short-term project together with SAP. Data Intelligence is not used across the whole organization. For our use case, we used [SAP] Data Intelligence to predict the likelihood of a claim and then took this prediction as an input to the calculation of prices for different insurance offers.
- Easy handling even without prior experience
- Possibility to use Python and R libraries
- Debugging pipeline failures is not as easy and straightforward
- When working with SAP DI, new tabs get opened frequently when actions are performed. Having 10+ open tabs is not very handy
December 09, 2020
We are calculating daily, monthly and yearly expenses for our department, basically all our expenses, inventory counts, and corrections. It is the heart of my business reporting and we use it more than once an hour to solve problems. The benefits are impossible to count as so many people rely on SAP Data Intelligence.
- The best thing about SAP is that we can track our daily expenses in very little time.
- This affects my business even more than Microsoft 365.
- It updates us on our expenses and sales daily.
- I wish it the acronyms made more sense.
- Sometimes it's functionality is little bit slow.
- Data integration services with other machine language tools were more complex. Workflows could have been simplified on this front.
We are implementing SAP DI for large customers to manage their ML ecosystem. The DI system will be used for hosting homegrown models and also building newer ones from scratch. We are connecting SAP and Non SAP systems to DI. Inferences from DI would be used to support key business decisions.
- Data orchestration from diverse sources.
- Good inbuilt support to develop models (Jupyter Notebook).
- Data pipelines, container architecture for deployment.
- Tools for data cleansing.
- Ability to port existing ML models into DI.
- Product support documentation and blogs can be better.
- Debugging can take a bit of time to understand and pinpoint issues.
- Can be pricey.
October 22, 2020
As a consulting company we use SAP Data Intelligence Tenant to build apps and other consulting related content according to business use cases. It is mainly use by our Cloud Analytics department. From a content point of view, we address analytic and augmented topics in the area of sales, marketing and service.
- Integration into the SAP Environment.
- Design and deployment of AI models.
- Orchestrate data pipelines.
- Collaboration on the same artefact is challenging.
December 01, 2020
SAP Data Intelligence is used for multiple use cases:
- Data is integrated from different source systems, which is then analysed first. After the analysis data is transformed and harmonized.
- Further, machine learning algorithms are modeled in SAP Data Intelligence and run on the data set.
- What is more, the SAP Data Intelligence environment interacts with different on premise systems and the results of the machine learning processes are sent back to the on premise systems.
- Data is integrated from different source systems, which is then analysed first. After the analysis data is transformed and harmonized.
- Further, machine learning algorithms are modeled in SAP Data Intelligence and run on the data set.
- What is more, the SAP Data Intelligence environment interacts with different on premise systems and the results of the machine learning processes are sent back to the on premise systems.
- Great machine learning capabilities
- Many possibilities to connect source systems and interact with it
- Operation on cloud reduces administration tasks to a minimum
- Great Governance capabilities
- Still fairly new product
November 25, 2020
We have used the SAP Data Intelligence as an end to end tool for both data orchestration and for ML scenario manager which helped with multiple AI/ML scenarios deployed. It’s all of it packaged in one go....
- Data orchestration
- AI/ML scenarios and pipelines
- Data cleansing
- Deployment
- Integration with R and other external services
- Connectivity with pdms and other sap iot services
SAP Data Intelligence Scorecard Summary
What is SAP Data Intelligence?
SAP Data Intelligence aims to transform distributed data sprawls into vital data insights, to deliver innovation at scale. It is a data management solution that connects, discovers, enriches, and orchestrates disjointed data assets into actionable business insights at enterprise scale. It enables the creation of data warehouses from heterogeneous enterprise data, management of IoT data streams, and facilitates scalable machine learning.
SAP Data Intelligence aims to allow users to leverage business applications to become an intelligent enterprise and provides a holistic, unified way to manage, integrate, and process all your enterprise data.
With SAP Data Intelligence users can:
1. Discover and connect to any data, anywhere, anytime from a single enterprise data fabric
2. Transform and augment data across complex data types and curate a robust searchable data catalog
3. Implement intelligent data processes by orchestrating complex data flows enriched with scalable, repeatable, production grade machine learning pipelines
SAP offers an interactive demo, a product trial, and also allows readers to explore use cases for SAP Data Intelligence.
Categories: Data Integration, Data Fabric
SAP Data Intelligence Screenshots
SAP Data Intelligence Videos (5)
Example of SAP Data Intelligence integration with SAP Data Services
Watch Try out SAP Data Intelligence with this interactive demo.
Watch Watch this short video on creating data pipelines in SAP Data Intelligence
Watch Short overview video of SAP Data Intelligence
Watch Check out some of the metadata management features in SAP Data Intelligence
SAP Data Intelligence Integrations
TensorFlow, Apache Kafka, SAP Data Services, MySQL, Oracle Database, Alibaba Cloud Object Storage Service (OSS), Open Source such as Python, R, Go, OData, HDFS, SAP such as SAP HANA, ABAP, SAP Cloud Platform OpenConnectors, SAP IQ, etc, Microsoft Azure Data Lake Store, Azure Storage Blob, Google Pub Sub, BigQuery, Cloud Dataproc cluster, Amazone Redshift, SNS, S3, IBM DB2, HTTP, IMAPm SFTP, SMTP, Rserve
SAP Data Intelligence Competitors
Talend Data Fabric, Informatica, Oracle
SAP Data Intelligence Pricing
- Has featureFree Trial Available?Yes
- Does not have featureFree or Freemium Version Available?No
- Has featurePremium Consulting/Integration Services Available?Yes
- Entry-level set up fee?Optional
| Edition | Pricing Details | Terms |
|---|---|---|
| Subscription | $1.20 | Per Unit Per Month |
For the latest information on pricing, visit https://www.sap.com/products/data-intelligence/pricing.html
SAP Data Intelligence Support Options
| Free Version | Paid Version | |
|---|---|---|
| Phone | ||
| Live Chat | ||
| Forum/Community | ||
| FAQ/Knowledgebase | ||
| Social Media | ||
| Video Tutorials / Webinar |
SAP Data Intelligence Technical Details
| Deployment Types: | On-premise, SaaS |
|---|---|
| Operating Systems: | Kubernetes & Docker |
| Mobile Application: | No |
| Supported Countries: | Global |