Part 5 of 5 in the What Is Data Intelligence blog series.
In this series we have highlighted the importance of bringing together Enterprise AI and Intelligence Information Management to rapidly derive value from business data in a trusted and governed manner. But it is also important to talk about the process itself –the best tools and technology are useless if the people and the process aren’t also aligned, and that is why it is important to think about how we combine the technology, the process, and the people together to make a sustainable, scalable, and high impact approach.
Since it has required such specialized skills, and since a lot of the tools and infrastructure requirements for AI have been missing until very recently, we have seen the rise of very specialized teams working in a more job-shop manner. Everything was being done by a single team of highly trained and hyper-specialized individuals who often set up their own IT infrastructure and create their own data marts or data environments. They would develop and run small scale finished models in their own environments—sometimes working with IT to do so. And this hyper-bespoke approach has its benefits to be sure, especially when no other options existed, but it fails to make use of the powers of specialization and collaboration. It was with this collaborative approach in mind that we designed SAP Data Intelligence.
We wanted to give data engineers and enterprise architects the tools to connect to and manage data across the organization for multiple use cases. If you are orchestrating and enriching data to load into business applications or merging IoT and sensor data with corporate data and extracting value from that data (predictive maintenance being a good example), or developing and deploying machine learning in a scalable manner, you have specialists that understand your data systems and landscapes better than anyone else.
We also wanted to make sure that Data Science teams had access to the data they needed without having to always go and acquire it manually. We wanted to make it possible to search and find the data directly and to work with it in a trusted and governed manner that gives IT the power to easily reproduce and manipulate it. And for the teams that support the infrastructure for Data Science we wanted to make it easy to rapidly provision experimental environments, with support for the infrastructure and open source frameworks required, and then shut them down again when they are no longer needed. This reduces the time it takes to get Data Science teams up and running, as well as the overall cost of managing and running infrastructure.
Finally, we wanted to make it easier for IT teams to manage this entire process and be able to easily take the work from various data science teams and replicate those experiments into production environments where they can generate business value. We ensure that users can meaningfully consume data insights by:
- Feeding the models’ results into Data warehouses for visualization with analytics tools
- Embedding the results into business applications
- Automating a response through SAP Intelligent Robotic Process Automation
- Driving personal interactions based on the models’ results with SAP Conversational AI
The idea of being able to use people, process, and technology together to drive Enterprise AI and more importantly business insights is why we think of SAP Data Intelligence as the foundation of the AI Assembly Line.