Trends in Enterprise AI and Digital Decisions. Mike Gualtieri, Forrester
Day 2 of DecisionCAMP 2019 in beautiful Bolzano started out with Mike Gualtieri giving the Forrester view of trends in the market around AI and automated decisions. This was a typical analyst presentation — sorry, no notes — presented as part of the larger BRAIN 2019 (Bolzano Rules and Artificial INtelligence Summit) of which DecisionCAMP is a part.
Ron Ross presented on the current state of business rules and opportunities moving forward. To start, we have made progress in this area — DMN for one thing is an amazing leap forward — but business rules are not yet universally accepted and adopted within organizations despite the provable benefits.
One opportunity for business rules tools is to reduce developer workload, and to reduce rule programming errors. In alignment with Semantics Of Business Vocabulary and Rules (SBVR) standard, there are two types of rules: definitional rules and behavioral rules. Definitional rules may be incorrect or misapplied, but they can’t be directly violated since they are evaluated in the course of a process; declarative behavioral rules, on the other hand, require a “watcher” to track other events that may cause the state of another process or transaction to change. If implemented properly, behavioral rules can reduce developer workload since the event-driven watcher updates state constantly based on these rules firing. DMN does not allow modeling of these types of decisions, since there needs to be more awareness of state as well as the events that may cause it to change; there is no concept of a watcher daemon that can constantly evaluate rules and update state.
There is also a need to better address sentiment and human discretion in rules. With behavioral rules that are enforced by humans, there are levels of enforcement; these nuances are not captured in most rules/decision systems.
Rules tools also need to tie in more directly with business governance in order to enforce regulatory and other rules under which an organization needs to operate. Many of these are behavioral rules, which are not handled adequately by DMN and most decision management systems due to the lack of an event-driven watcher; there is also a gap caused by the lack of natural language support in defining executable rules.
Beyond Decision Models – Using Technical and Business Standards to Transform Financial Services. Brian Stucky, Quicken Loans
Although having recently joined Quicken Loans as a senior enterprise architect, Brian Stucky is also involved in the MISMO mortgage standards organization, which was the focus of his presentation. Earlier this year, MISMO recommended DMN as an official standard for documenting, implementing, exchanging and executing decision models in the mortgage industry; they are also working on officially recognizing BPMN too. The idea is to create a DMN data structure based on the existing MISMO XSD to allow these mortgage-related decision models to be shared, but the industry is still rife with paper-based processes and legacy systems that hinder adoption.
There’s a history of business rules in the mortgage industry, but it didn’t really allow for business control of the rules, didn’t have the agility required, and was expensive. DMN is changing that game — especially with decisions as a service instead of on-premise systems — and allowing mortgage companies to better meet some of the new regulations such as Ability-To-Repay, where the written government regulation can be translated into a standard DMN model to ensure that all parties are using the same evaluation criteria. They’ve proven that time required to change the DMN model for a specific rule can take as little as a couple of hours to analyze and modify the model, which is a huge push for moving from MISMO XSD to the DMN model.
In the future, this could mean that DMN plus the MISMO data model could be used directly to disseminate a regulatory rule change, rather than the 800-page text document used now. That brings up other issues, such as versioning of the model or even of DMN, and engine compliance in executing the DMN models as distributed. A better way to do it may be to roll out the model as a service with an open API, where every mortgage provide uses the same decision service; this guarantees that it will be evaluated identically everywhere. The ultimate goal may be a digital mortgage, potentially using blockchain to ensure the chain of events in this smart contract.
Meeting the Expectations with DMN and Constraint Solving: The Notary Case. Marjolein Deryck, KU Leuven
Marjolein Deryck presented on research in decision modeling and knowledge representation, and how she applied it to property registration taxes in Belgium, which is typically calculated by a notary. The use case was to support a notary when performing these calculations, using DMN for collaborative analysis with the notary and an executable prototype; then IDP to go beyond DMN capabilities in a constraint approach.
An interesting requirement was that the support application be non-intrusive: the notary felt that if he was spending too much time typing on a computer while figuring this out with a client, he would be seen as less of an expert. A tablet-based app with minimal requirement for data entry, plus interactive in terms of presenting the next best question rather than following a fixed script were seen as essential.
In her initial evaluation, DMN was seen as lacking script interactivity/adaptability (although I saw a really interesting way to use DMN and BPMN to resolve this last week at CamundaCon), and she instead considered IDP as a more powerful implementation. This provides a better solution, although the models are less understandable by the notaries, and required enhancements to be able to provide an explanation of a specific calculation.
The lessons learned included the use of DMN as an intermediate model — for gathering and analyzing requirements together with the business user — as well as how to combine DMN and IDP in a project.
Panel: DMN and Beyond
We closed off day 1 of DecisionCAMP 2019 with a panel that included Mike Gualtieri, Alan Fish, Jan Vanthienen, Jan Purchase, Gary Hallmark and Brian Stucky, moderated by Jacob Feldman.
A few points that came up during the panel (unattributed to the specific speaker):
Many buyers of decision management systems don’t known enough about DMN to evaluate it or even ask for it.
DMN still falls short in complex representations, although works well to represent static hierarchical information from decision tables. It has the potential to include other representations and other models such as machine learning. Making it more powerful could, however, have DMN lose the simplicity that makes it more likely to be adopted.
DMN is difficult to debug, making it hard to figure out logic flaws.
The diagram/graph level of DMN is very understandable to business users/analysts, but by the time you’re doing more complex nested expression logic at the FEEL execution level, you’ve lost most of them.
Highly-regulated industries such as lending, where rules are already documented in spreadsheets, are a good target for DMN implementation.
Being able to follow the execution path is not the same as an explanation of the decision logic. The DMN standard includes support for remarks/annotations to improve explainability but that may not be sufficient.
Knowledge sources in DMN models have no programmatic representation, putting the onus on the modeler to ensure explainability and traceability.
Ethics are important to decision management in terms of decision fairness and consistency. DMN model-based decision making can improve that as long as the models are based on the right rules and data.
There’s a need to be able to integrate DMN and machine learning while still providing decision explainability.
Models are always fit for purpose: there is no all-encompassing model that is suitable for everything. As an aside, that’s definitely true in the BPMN realm too.
That’s it for day 1; I’m off to find a gelato and an Aperol Spritz on this warm evening in Bolzano.
How and Why I Turned a Rule Engine into a First-Class Serverless Component. Mario Fusco and Matteo Mortari, Red Hat
Mario Fusco, who heads up the Drools project within Red Hat, presented on modernization of the Drools architecture to support serverless execution, using GraalVM and Quarkus. He discussed Kogito, a cloud-native, open source business automation project that uses Red Hat process and decision management along with Quarkus.
I’m not a JAVA developer and likely did not appreciate many of the details in the presentation, hence the short post. You can check out his slides here.
Combining DMN, First Order Logic and Machine Learning: The creation of Saint-Gobain Seals’ Digital Engineer. Nicholas Decleyre, Saint-Gobain and Bram Aerts, KU Leuven
The seals design and manufacturing unit of Saint-Gobain had the goal to create a “digital engineer” to capture knowledge, with the intent to standardize global production processes, reduce costs and time to market, and aid in training new engineers. They create an engineering automation tool to automatically generate solutions for standard designs, and an engineering support tool to provide information and other support to engineers while they are working on a solutions.
Automation for known solutions is fairly straightforward in execution: given the input specifications, determine a standard seal that can be used as a solution. This required quite a bit of knowledge elicitation from design engineers and management, which could then be represented in decision tables and FEEL for readability by the domain experts. Not only the solution selection is automated, however: the system also generates a bill of materials and pricing details.
The engineering support system is for when the solution is not known: a design engineer uses the support system to experiment on possible solutions and compare designs. This required building a knowledge base in first-order logic to define physical constraints and preferences, represented in IDP, then allowing the system to make recommendations about a partial or complete solution or set of solutions. They built a standalone tool for engineers to use this system, presenting a set of design constraints for the engineer to apply to narrow down the possible solutions. They compared the merits of DMN versus IDP representations, where DMN is easier to model and understand, but has limitations in what it can represent as well as being more cumbersome to maintain. At RuleML yesterday, they presented a proposal for extended DMN for better representing constraints.
They finished up talking about potential applications of machine learning on the design database: searching for “similar” existing solutions, learning new constraints, and checking data consistency. They have several automated engineering tools in development, with one in testing and one in production. Their engineering support tool has working core functionality although need to expand the knowledge base and prototype the UI. On the ML work, they are expecting to have a prototype by the end of this year.
Machine Learning and Decision Management: A standards-based approach. Edson Tirelli and Matteo Mortari, Red Hat
DecisionCAMP Day 1 morning sessions continue with Edson Tirelli and Mateo Mortari presenting on the integration of machine learning and decision management to address predictive decision automation. The problem to date is that integrating machine learning into business automation (either process or decision) has required proprietary interfaces and APIs, although there is an existing standard (PMML, Predictive Model Markup Language) for specifying and exchanging many types of executable machine learning models. The entry of the DMN standard provides a potential bridge between PMML and both BPMN and CMMN, allowing for an end-to-end standards-based representation for cases, processes, decisions and predictive models.
They gave a demo of how they have implemented this using RedHat decision and process engines along with open source tools Prometheus and Grafana, with a credit card dispute use case that uses BPMN, DMN and PMML to model the process and decisions. They started with a standard use of BPMN and DMN, where the DMN decision tables and graphs calculate the risk factors of the dispute and the customer, and make a decision on whether or not the dispute process can be automated. They added a predictive model for better calculation of the risk factors, positioning this in the DMN DRD as a business knowledge model that can then drive the decision model instead of a hard-coded decision table.
They finished their demo by importing the same PMML and DMN models in the Trisotech modeler to show interoperability of the integrated model types, with the predictive models providing knowledge sources for the decision models.
Coming from the process side, this is really exciting: we’re already seeing a lot of proprietary plug-ins and APIs to add machine learning to business processes, but this goes beyond that to allow standards-based tools to be plugged together easily. There’s still obviously work to be done to make this a seamless integration, but the idea that it can be all standards-based is pretty significant.
FEEL, Is It Really Friendly Enough? Daniel Schmitz-Hübsch and Ulrich Striffler, Materna
Materna has a number of implementation projects (mostly German government) that involve decision automation, where logic is modeled by business users and require that the decision justification be able to be explained to all users for transparency of decision automation. They use both decision tables and FEEL — decision tables are easier for business users to understand, but can’t represent everything — and some of the early adopters are using DMN. Given that most requirements are documented by business users in natural language, there are some obstacles to moving that initial representation to DMN instead.
Having the business users model the details of decisions in FEEL is the biggest issue: basically, you’re asking business people to write code in a script language, with the added twist that in their case, the business users are not native English speakers but the FEEL keywords are in English. In my experience, it’s hard enough to get business people to create syntactically-correct visual models in BPMN, moving to a scripting language would be a daunting task, and doing that in a foreign language would make most business people’s heads explode in frustration.
They are trying some different approaches for dealing with this: allowing the users to read and write the logic in their native natural language (German), or replacing some FEEL elements (text statements) with graphical representations. They believe that this is a good starting point for a discussion on making FEEL a bit friendlier for business users, especially those whose native language is not English.
Good closing discussion on the use of different tools for different levels of people doing the modeling.
Collaborative decisions: coordinating automated and human decision-making. Alan Fish, FICO
Alan Fish presented on the coordination of decisions between automation, individuals and groups. He considered how DMN isn’t enough to model these interactions, since it doesn’t allow for modeling certain characteristics; for example, partitioning decisions over time is best done with a combination of BPMN and DMN, where temporal dependencies can be represented, while combining CMMN and DMN can represent the partitioning decisions between decision-makers.
He also looked at how to represent the partition between decisions and meta-decisions — which is not currently covered in DMN — where meta-decisions may be an analytical human activity that then determines some of the rules around how decisions are made. He defines an organization as a network of decision-making entities passing information to each other, with the minimum requirement for success based on having models of processes, case management, decisions and data. The OMG “Triple Crown” of DMN, BPMN and CMMN figure significantly in his ideas on a certain level of organizational modeling, and the success of the organizations that embrace them as part of their overall modeling and improvement efforts.
He sees radical process reengineering as being a risky operation, and posits that doing process reengineering once then constantly updating decision models to adapt to changing conditions. An interesting discussion on organizational models and how decision management fits into larger representations of organizations. Also some good follow-on Q&A about whether to consider modeling state in decision models, or leaving that to the process and case models; and about the value of modeling human decisions along with automated ones.
Making the Right Decision at the Right Time: Introducing Temporal Reasoning to DMN. Denis Gagné, Trisotech
Denis Gagné covered the concepts of temporal reasoning in DMN, including a new proposal to the DMN RTF for adding temporal reasoning concepts. Temporal logic is “any system of rules and symbolism for representing, and reasoning about, propositions qualified in terms of time”, that is, representing events in terms of whether they happened sequentially or concurrently, or what time that a particular event occurred.
The proposal will be for an extension to FEEL — which already has some basic temporal constructs with date and time types — that provides a more comprehensive representation based on Allen’s interval algebra and Zaidi’s point-interval logic. This would have built-in functions regarding intervals and points, with two levels of abstraction for expressiveness and business friendliness, allowing for DMN to represent temporal relationships between points, between points and intervals, and between intervals.
The proposal also includes a more “business person common sense” interpretation for interval overlaps and other constructs: note that 11 of the possible interval-interval relationships fall into this category, which makes this into a simpler before/after/overlap designation. Given all of these representations, plus more robust temporal functions, the standard can then allow expressions such as “interval X starts 3 days before interval Y” or “did this happen in September”.
This is my first time at DecisionCAMP (formerly RulesFest), and I’m totally loving it. It’s full of technology practitioners — vendors, researchers and consultants — who more interested in discussing interesting ways to improve decision management and the DMN standard rather than plugging their own products. I’m not as much of a decision management expert as I am in process management, so great learning opportunities for me.
I’m finishing up a European tour of three conferences with DecisionCAMP in Bolzano, which has a focus on business rules and decision management technology. This is really a technology conference, with sessions intended to be more discussions about what’s happening with new advances rather than the business or marketing side of products. Jacob Feldman of OpenRules was kind enough to invite me to attend when he heard that I was going to be with striking distance at CamundaCon last week in Berlin, and I’ll be moderating a panel tomorrow afternoon in return.
Feldman opened the conference with an overview of operational decision services for decision-making applications, such as smart processes, and the new requirements for decision services regarding performance, security and architectural models. He sees operational decision services as breaking down into three components: business knowledge (managed by business subject matter experts), business decision models (managed by business analysts) and deployed decision services (managed by developers/devops) — the last of these is what is triggered by decision-making applications when they pass data and request a decision. There are defined standards for the business decision models (e.g., DMN) and transferring those to execution engines for the deployed services, but issues arise in standardizing how SMEs capture business knowledge and pass it on the to BAs for the creation of the decision models; definitely an area requiring more work from both standards groups and vendors.
I’ll do some blog posts that combine multiple presentations; you can see copies of most of the presentations here.
Business Automation as a Service, with Denis Gagne of Trisotech
Denis demoed a new product release from Trisotech, their business automation as a service platform: competing with services such as Zapier and IFTTT but with better process and decision management, and more complex service types available for integration. He showed creating a service built on a Twitter trigger, using BPMN to model the orchestration and FEEL as the scripting language in script activities, and incorporating a machine learning sentiment score and a decision service for categorizing the results, with the result displayed in the color of a flashing smart light bulb. Every service created exposes an Open API and REST API by default, and is deployed as a self-contained microservice. He showed a more complex example of marketing automation that extracts data from an input form, uses a geo-locator to find the customer location, uses a DMN decision model to assign to a sales team based on geography and other form parameters, then creates a lead in Microsoft Dynamics CRM. He finished up with an RPA task example that included the funniest execution of an “I am not a robot” CAPTCHA ever. Key point here is that Trisotech has moved from a pure modeling vendor into the execution space, integrated with any Open API service, and deployable across a number of different cloud platforms using standard protocols. Looking forward to playing around with this.
Business-Composable Services for the Mortgage Industry, with Bruce Silver of Method and Style
Bruce showed the business automation services that he’s created using Trisotech’s platform for the mortgage industry. Although he started looking at decision services around how to determine if someone should be approved for a mortgage (or how large of a mortgage), process was also required to do things like handle mapping and validation of data. Everything is driven by a standard application form and a standard set of underwriting rules used in the US mortgage industry, although this could be modified to suit other markets with different rules. The DMN rules are written in business-readable language, allowing them to be changed by non-developers. The BPMN process does the data validation and mapping before invoking the underwriting decision service. The entire process can be published as a service to be called from any environment, such as a web app used by underwriters inside a financial company or by an online prequalification review done directly by the consumer. The plan is to make these models and services available to see what the adoption is like, to help highlight the value and drive the usage of BPMN and DMN in practice.
Industry Round Table: The Coming Impact of Decision Services and Machine Learning on Business Automation
We finished the morning of day 2 with a discussion that included three of the earlier demo presenters: Denis Gagne, Bruce Silver and Scott Menter. They each gave a short talk on how decision services and machine learning are changing the automation landscape. Some ideas discussed:
It’s still up in the air whether DMN will “cross the chasm” and become generally used (to the same degree as, for example, BPMN); this means that vendors need to fully support it, potentially as an execution as well as requirements language.
Having machine learning algorithms expressed as DMN can improve transparency of decisions, which is essential in some jurisdictions (e.g., GDPR). There is a need for “explainable AI”.
The population using DMN is lower than BPMN, and the skill level is higher, although still well within the capabilities of data-focused business people who are comfortable with formulas and expression languages.
There’s a distinction between symbolic (rules-based) and sub symbolic (neural network) AI algorithms in terms of what they can do and how they perform; however, sub symbolic AI is less of a black box in terms of decision transparency.
If we here at bpmNEXT aren’t thinking about the ethics of automation, who will? Consider the labor disruption of automation, or decisions that make a choice involving the value of life (the AI “trolley problem”), or old norms used as training data to create biased machine learning.
We’re still in a culture of having people at a certain skill level (e.g., surgeons, pilots) make their own decisions, although they might be advised by AI. How soon before we accept automated decisions at that level?
Individually-targeted decisions are happening now by what is presented to specific people through platforms like Google Search and Amazon. How is our behavior being controlled by the limited set of options presented to us?
The closer that a technology gets to the end effect, the more responsibility that the creator of the technology needs to take in how it is used.
Machine learning may be the best way to discover the best transparent decision logic from human action (unfortunately that will also include the human biases), allowing for people to understand how and why specific decisions are made.
When AI is a black box, it needs to be understood as being a black box, so that adequate constructs can be created around it for testing and usage.
Great discussion and audience participation, and a good follow-on from the two demos that showed decision services in action.
We’re on the final session of bpmNEXT 2018 — it’s been an amazing three days with great demos and wonderful conversations.
Exploiting Cloud Infrastructure for Efficient Business Process Execution, Red Hat
Kris Verlaenen, project lead for jBPM as part of Red Hat, presented on cloud BPM infrastructure, specifically for execution and monitoring. Cloud makes BPM lightweight, scalable, embedable and able to take advantage of the larger cloud app ecosystem. They are introducing some new cloud infrastructure, including a controller for managing server deployments, a smart router for delegating and aggregating requests from applications to servers, and monitoring that aggregates process statistics across servers and containers. The demo showed using Red Hat’s OpenShift container application platform (actually MiniShift running on his laptop) to create a new environment and deploy an IT hardware ordering BPM application. He walked through using the application to create a new order and see the milestone-based monitoring of the order, then the hardware provider’s view of their steps in the process to provide information and advance the process to the next stage. The process engine and monitoring engine can be deployed in different containers on different hardware, in any combination of cloud providers and on-premise infrastructure. Applications and servers can be bundled into a single immutable image for easy provisioning — more of a microservices style — or can be deployed independently. Multiple versions of the same application can be deployed, allowing current instances to play out in the original version while new instances use the most recent version, or other strategies that would allow new instances of any version to be created, while monitoring can aggregate instance data from all versions in all containers.
Kris is also live-blogging the conference, check out his posts. He has gone back and included the video of each presentation when they are released (something that I didn’t do for page load performance reasons) as well as providing his commentary on each presentation.
Lloyd Dugan of Serco has the unenviable position of being the last presenter of the conference, although he gave a presentation of dynamic work assignment implementation rather than an actual demo (with a quick view of the simple process model in the Trisotech animator near the end, plus an animation of the work assignment in action). His company is a call center business process outsourcer, where knowledge workers use a case management application implemented in BPMN, driven by events such as inbound calls and documents, as well as timers. Real-time work prioritization and assignment is necessary because of SLAs around inbound calls, and the task management model is moving from work being selected (and potentially cherry-picked) by workers, to push assignments. Tasks are scored and assigned using decision models that include task type and SLAs, and worker eligibility based on each individual’s skills and training. Although work assignment products exist, this one is specifically for the complex rules around the US Affordable Care Act administration, which requires a combination of decision tables, database table-driven rules, and lower-level coding to provide the right combination of flexibility and performance.
DMN TCK (Technical Compatibility Kit) Working Group
Keith Swenson of Fujitsu (but presenting here in his role on the DMN standards) started on the idea of a set of standardized DMN technical compatibility tests based on conversations at bpmNEXT in 2016, and he presented today on where they’re at with the TCK. Basically, the TCK provides a way for DMN vendors to demonstrate their compliance with the standard by providing a set of DMN models, input data, and expected results, testing decision tables, boxed expressions and FEEL. Vendors who can demonstrate that they pass all of the TCK tests are listed on a github site along with information about individual test results, providing a way for DMN customers to assess the compliance level of vendors. Keith wrote an update on this last September that provides a good summary up to that point, and in today’s presentation he walked through some of the additional things that they’ve done including identifying sections of the DMN specification that require clarifications or additions due to ambiguity that can lead to different implementations. DMN 1.2 is coming out this year, which will require a new set of tests specifically for that version while maintaining the previous version tests; they are also trying to improve testing of error cases and introducing more real-world decision models. If you create and use DMN models, or make a DMN-compliant decision management product, or you’re otherwise interested in the DMN TCK, you can find out here how to get involved in the working group.
That’s it for bpmNEXT 2018. There will be voting for the best in show and some wrapup after lunch, but we’re pretty much done for this year. Another amazing year that makes me proud to be a part of this community.
Nathaniel Palmer kicked off day 2 of bpmNEXT 2018 with his ever-prescient views on the next five years of BPM. Bots, decisions and automation are key, with the three R’s (robots, rules and relationships) defining BPM in the years to come. More and more, commercial transactions (or services that form part of those transactions) will happen on servers outside your organization, and often outside of your control; robots and intelligent agents will be doing a big part of that work. He also believes that we’re seeing the beginning of the death of smartphones, to be replaced with other devices and other interfaces such as conversational UI and wearable technology. This is going to radically change how apps have to be designed, and will leave a lot of companies scrambling to catch up with this change as people move more of their interactions off smartphones and laptops. Although more conservative organizations — including government agencies — will continue to support the least common denominator in interaction style (probably email and traditional websites), commercial organizations don’t have that luxury, and need to rethink sooner. He envisions that your fastest-growing competitors will have fewer employees than robots, although some interesting news out of Tesla this week may indicate that it’s premature to replace some human functions.
He spoke about how this will refine application architecture to four tiers: a client tier unique to each platform, a separate delivery tier that optimizes delivery for the platforms, an aggregation tier that integrates services and data, and a services tier that pulls data from both internal and external source. This creates an abstraction between what a task is and how it is performed, and even whether it is automated or performed by a person. Decision as a service for both commercial and government services will become a primary delivery model, allowing decisions (and the automation enabled by them) to be easily plugged into applications; this will require more of a business-first, model-driven approach rather than having decisions built in code by developers.
His Future-Proof BPM architecture — what others are calling a digital transformation platform — brings together a variety of capabilities that can be provided by many vendors or other organizations, and fed by events. In fact, the core capabilities (automation, machine learning, decision management, workflow management) also generate events that feed back into the data flooding into these processes. BPM platforms have the ability to become the orchestrating platforms for this, which is possibly why many of the BPMS vendors are rebranding as low-code application development environments, but be aware of fundamental differences in the underlying architecture: do they support modularity and microservices, or are they just lifting and shifting to monolithic containers in the cloud?
Finishing up, he returned to the concept that intelligent agents can act autonomously in complex transactions, and this will be becoming more common over the next few years. Interestingly, an interview that I did for a European publication is being translated into German, and the translator emailed me this morning to tell me that they needed to change some of my comments on automating loan transactions since that’s not permitted in Germany. My response: not yet, but it will be. We all need to be prepared for a more automated future.
Great audience discussion at the end on how this architecture is manifesting, how to model/represent some of these automation concepts, the role of a smarter event bus, the future of the word “bot” and more. Max Young from Capital BPM took over to discuss the development of a grammar for RPA, with an invitation for the brain trust in the room to start thinking about this in more detail. RPA vendors are creating their own notation, but a vendor-agnostic standard would go a long ways towards helping business people to directly specify automation.
Since they’re pumping out the video on the same day as the presentations, check the bpmNEXT YouTube channel later for a replay of Nathaniel’s presentation.
Elmar Nathe of MID GmbH presented on their enterprise decision maps, which provides an aggregated visualization of strategic, tactical and operational decisions with business events. They provide a variety of modeling tools, but see decisions as key to understanding how organizations are driven by data and events. Clearly a rich decision modeling environment, including support for PMML for including predictive models and other data scientist analysis tools, plus links to other model types such as ERDs that can show what data contributes to which decision model, and business process models. Much more of an enterprise architecture approach to model-driven design that can incorporate the work of data scientists.
Using Customer Journeys to Connect Theory with Reality, Signavio
Till Reiter and Enrico Teterra of Signavio started with a great example of an Ignite presentation, with few words, lots of graphics and a bit of humor, discussing their new notation for modeling an outside-in view of the customer journey rather than just having an undifferentiated “customer” swimlane in a BPMN diagram. The demo walked through their customer journey mapping tool, and how their collaboration hub overlays on that to allow information about each component of the journey map to be discussed amongst process modeling users. The journey map contains a lot of information about KPIs and other process metrics in a form most consumable by process owners and modelers, but also has a notebook/dashboard view for analysts to determine problems with the process and identify potential resolution actions. This includes a variety of analysis tools including process discovery, where process mining techniques are applied to determine which paths in the process model may be contributing to specific problems such as cycle time, then overlay this on the process model to assist with root cause analysis. Although their product does a good job of combing CJMs, process models and process analysis, this was more of a walkthrough of a set of pre-calculated dashboard screens rather than an actual demo — a far cry from the experimental features that Gero Decker showed off in their demo at the first bpmNEXT.
The final presentation of this section was with Jude Chagas Pereira of IYCON and Frank Kowalkowski of Knowledge Consultants presenting IYCON’s Afterspyre modeling tool for creating a catalog of complex business objects, their attributes and their linkages to create organizational DNA diagrams. Ranking these with machine learning algorithms for semantic and sentiment analysis allows identification of process improvement opportunities. They have a number of standard business analysis techniques built in, and robust analytics focused on problem solving. The demo walked through their catalog, drilling down into the “Strategy DNA” section and into “Technology Solutions” subsection to show an enumeration of the platforms currently in place together with attributes such as technology risk and obsolescence, which can be used to rank technology upgrade plans. Relationships between business objects can be auto-detected based on existing data. Levels including Objectives, Key Processes, Technology Solutions, Database Technology and Datacenter and their interrelationships are mapped into a DNA diagram and an alluvial diagram, starting at any point in the catalog and drilling down a specific number of levels as selected by the modeling analyst. These diagrams can then be refined further based on factors such as scaling the individual markers based on actual performance. They showed sentiment analysis for a hotel rank on a review site, which included extracting specific phrases that related to certain sentiments. They also demonstrated a two-model comparison, which compared the models for two different companies to determine the overlap and unique processes; a good indicator for a merger/acquisition (or even divestiture) level of difficulty. They finished up with affinity modeling, such as the type used by Amazon when they tell you what books that other people bought who also bought the book that you’re looking at: easy to do in a matrix form with a small data set, but computationally intensive once you get into non-trivial amounts of data. Affinity modeling is most commonly used in marketing to analyze buying habits and offering people something that they are likely to buy, even if that’s what they didn’t plan to buy at first — this sort of “would you like fries with that” technique can increase purchase value by 30-40%. Related to that is correlation modeling, which can be used as a first step for determining causation. Impressive semantic data-driven analytics tool for modeling a lot of different organizational characteristics.
That’s it for day one; if everyone else is as overloaded with information as I am, we’re all ready for tonight’s wine tasting! Check the Twitter stream for opinions and photos from other attendees.