RapidGen associate Jan Purchase was elected as inaugural chairmen of the Decision Automation Organisation’s DMN On-Ramp (DMNOR) committee in May 2021. In January 2022, RapidGen’s own Brenda Barnes was invited to join the Review Committee. In this article, we explain the purpose of the committee and its first deliverables to help businesses adopt DMN.
Decision Model and Notation (DMN) is a modelling language and notation for the precise specification of business decisions (and rules) that is easily accessible by all the different stakeholders involved in corporate decision automation, including business subject matter experts. As a result, DMN can reduce the risk and effort of automation projects and improve their agility and accountability. DMN is also a powerful means of harnessing AI in automated decision-making.
The DMN On-Ramp aims to help businesses benefit from automating their business decisions with the Decision Model and Notation (DMN) by providing a route to adoption and a conformance system.
Why Do We Need a DMN On-Ramp?
The current marketplace can be confusing for businesses wishing to benefit from the transparency, agility and powerful AI integration capabilities of DMN for automating decision-making in their enterprise. As discussed elsewhere on this blog, DMN is a standard published by the Object Management Group to model and implement repeatable decisions within an organization to ensure interchange. But how should businesses get started with this standard? How can they quickly harness the benefit of automated decision-making?
The DMN standard documentation is highly technical and aimed at vendors rather than end-users. Although it describes the details of the standard exhaustively, it does not (by design) include any business case for using DMN or any means of assessing what tools are necessary to support it. Furthermore, the standard presents vendors with an all-or-nothing approach to conformance, making it harder to support a consistent vendor offering across the market.
Businesses need to understand the scenarios in which DMN is beneficial and, for each scenario, what functionality tools must provide to be effective. Vendors and users alike need a pragmatic, standardized and staged approach to conformance. This allows them to use only as much of the DMN standard as their business needs require.
The DMN On-Ramp (DMNOR) aims to supply a vendor-independent, validated, staged route for DMN adoption and tool conformance. We aim to clarify the different ways businesses can engage with DMN and provide a compliance road map to ensure they know which tools can support them. At every stage of the DMN journey, we want to give business users confidence about assessing the tools they need and vendors clarity on what their tools must provide. We also wish to help adopters by sharing best practices.
Who is on the DMNOR Committee?
The team is a consortium of practitioners, vendors and academics, all of whom have many years’ experience of using decision modelling in projects. The current composition of the committee is:
- Representatives from vendors such as IBM (Lenny Bromberg, Pierre Berlandier), Sparkling Logic (Marc Lerman, Carlos Serrano), Decision Management Solutions (Ryan Trollip) and InRule (Chris Berg)
- Professor of information systems at KU Leuven and research leader in business rules processes and decisions, Prof Jan Vanthienen
- Entrepreneur, author and thought leader on decision automation, James Taylor
- Practitioner, author and consultant on digital decision automation, Jan Purchase
The DMN On-Ramp and the DAO
The DMN On-Ramp is a committee of the Decision Automation Organization (DAO), which is dedicated to advancing decision automation and management through education, consultancy and outreach to practitioners, vendors and end-users.
The On-Ramp Committee aims to provide:
- A comprehensive definition of how and why businesses can use DMN, the decision modelling scenarios (e.g., mining a decision inventory, eliciting decision definitions from subject matter experts, embedding machine learning into decisions)
- A set of business-centric compliance levels for vendors
- Provide consolidated advice on best practices for decision discovery and organization
These documents are aimed foremost at showing how businesses can benefit from decision automation and to describe a practical, staged process of DMN adoption based on their individual needs.
The DMNOR Review Committee
The Review committee, chaired by leading DMN consultant Marwim van Overschot, has the job of reviewing and ratifying the documents and guides produced by the On-Ramp. Their valuable business experience of applying DMN to real-world business problems helps the committee to ensure the output of the On-Ramp is pragmatic and attuned to business need.
RapidGen’s own director of Business Strategy, Brenda Barnes, was recently invited to join the Review Committee. Brenda brings RapidGen’s decades of pragmatic decision automation experience to the review of DMNOR documentation.
Watch this space for more news about DMN On-Ramp developments or post questions in the comment section below. If you have questions about DMN, or would like to discuss how you might benefit, please contact us.
About the Author
Jan Purchase has been working in investment banking for 20 years during which he has worked with nine of the world’s top 40 banks by market capitalization. In the last 13 years he has focused exclusively on helping clients with automated Business Decisions, Decision Modelling (in DMN) and Machine Learning. Dr Purchase specializes in delivering, training and mentoring all of these concepts to financial organizations and improving the integration of predictive analytics and machine learning within compliance-based operational decisions.
Dr Purchase has published a book Real World Decision Modelling with DMN, with James Taylor, which covers their experiences of using decision management and analytics in finance. He also runs a Decision Management Blog www.luxmagi.com/blog, contributes regularly to industry conferences and is currently working on ways to improve the explainability of predictive analytics, machine learning and artificial intelligence using decision modelling.