This is part of a 5-part blog series on bpmNEXT 2019:
Day 1 (part 2)
Day 2 (part 2)
Day 1 (part 2)
Day 2 (part 2)
Keynote: Best of Breed: Rolling Your Own Digital Automation Platform using BPMS and Microservices
Sandy Kemsley (who is also blogging about the various presentations) is starting off day 2 with a keynote on how large customers are building their own digital automation platforms in house leveraging available technologies, like BPM. Nowadays, taking a best of breed approach is replacing the legacy “monolith”. In the last decade, the BPMS became the new monolith because it was trying to fill a gap in app development (with constantly increasing requirements around forms, graphical modeling, BAM, etc.) which lead to large suites including one specific solution for each of these requirements. Agility however is a new competitive differentiator.
The new Digital Automation Platform is much more a (dynamic) collection of independent microservices, where the best-of-breed approach allows you to swap services in or out.
This might not be the solution for everyone (yet), but might be interesting for small to mid-sized companies looking for a COTS system to manage core processes, or for large companies with a large development team.
As a lesson for vendors, she recommends to separate components and price accordingly, and to make sure you can build microservices for your processes and decisions.
Business Automation as a Service
Denis Gagne – Trisotech
Trisotech is presenting their business automation as a service offering, allowing business users to express their logic in a simple way and now execute it directly as well.
The demo starts with a simple process to turn on the light when a twitter message is received. After defining this simple process within the tool, it’s deployed into the cloud with one click, and the lamp he brought on stage starts flashing every time someone tweets #Trisotech. Next, the process is extended to include a sentiment analysis service, to analyze the text included in the tweet, after which the light starts flashing green (or yellow or red) for every positive (or neutral or negative) tweet.
Next, a more complex example is used to track customer leads. When going to a demo website, you can register your details and the process will route your request to the right sales person, email you the slides and register you in the CRM system.
Trisotech is working closely with Red Hat, so great to see how they have built this great tool to allow people to quickly create and deploy processes and decisions into the cloud.
Business-Composable Services for the Mortgage Industry
Bruce Silver – Method and Style
Bruce shares his experience of using Trisotech to model a use case in the mortgage industry, using a combination of BPMN and DMN.
Applying for a loan requires quite complex decision models to determine eligibility and determine loan amount etc. The mortgage industry has standardized quite of lot of this, which enables creating some form of reusable service. A DMN model is used to describe the logic, using FEEL for the expressions. While the logic is complex sometimes, the resulting model should still be understandable by domain experts.
The data that is used as input is a standardized XML format (Mismo), which is mapped to a more DMN-friendly format (including validation etc.) using a separate process that is deployed as a service as well. Similarly, the input can also be a pdf file in which case a different process is used to extract the data from there. Using a simple test web page to provide the inputs (that is generated as part of the process deployment), the service produces the expected results.
Industry Round Table: The Coming Impact of Decision Services and Machine Learning on Business Automation
Another panel, this time focusing on decision services and A.I.
- Consensus (at least here it seems) that decision management has great synergy with process automation.
- Standards are really important, although not all vendors are using BPMN or DMN, which is fine
- DMN is not backed as much by the big vendors (Red Hat is one of them though), so it’s future is still much less clear
- Need to define and demystify A.I. as there are various types of intelligence
- Challenges with “black box” A.I. that cannot clearly explain why
- Ethical considerations
- Automation is disrupting labor force
- Some decisions are now being implemented in cold hard code
- The required skillset to deal with A.I. is only increasing