After many years, “Digital Decisioning: Using Decision Management to Deliver Business Impact from AI” is finally available in Chinese! This has been a long journey with all the usual delays in getting a translation done plus challenges like changes to the US rights and publisher, COVID and much more. Here are the front and back covers.
You can only order it from JD if you access to the Chinese site but early buyers in China tell me it definitely works!
Many thanks to Jin Xu for getting this over the finish line!
The English and Japanese versions are still available too!
Designing AI Decision Agents with DMN, Machine Learning & Analytics [this post]
The most effective way to define decision agents is using decision modeling. Just as you build a data model for a database or a process model for workflow, a decision model lets you create a visual blueprint for your decision agents. We use the industry standard notation for decision models – the Decision Model and Notation or DMN. This is partly because it’s a standard and partly because the basics of DMN are incredibly simple – three shapes and two lines. Yet this simplicity supports enormously complex decision agent designs.
I won’t get into the details here – there’s a lot of material out there including my book with Jan Purchase. Suffice it to say you can model out enormously complex decision agents, breaking down their decision-making into its component pieces and then specifying the logic for any piece that must be consistent and prescriptive while identifying the right kind of machine learning model for probabilistic decisions such as determining the sentiment of a text field for instance.
The prescriptive decisions in such a model can easily be implemented using a decision platform while the others can be executed on AI/ML models and even specified using standards such as PMML – Predictive Model Markup Language, an XML standard for interchanging predictive models or ONNX, Open Neural Network Exchange, which exchanges graph models between different ML platforms.
Each decision agent can leverage one or more decision services defined this way using MCP to communicate with the stateless services.
Besides integrating LLMs into the model for execution, you can also use them to help you build the models. While it is quick to build these by hand–10x faster than writing requirements documents, LLMs can accelerate this even further by taking your policy documents, Standard Operating Procedures and regulations and extracting initial partial decision models from them. These won’t be 100% right because most organizations don’t have everything documented but they will accelerate your process. You can also use LLMs trained on programming languages to extract models from code.
The DMN model represents a precise, visual definition of your decision-making that matches the behavior of your decision agents. This allows you to easily track changes, mix and match the right technology for each agent, engage business owners in the definition of your agents and produce regulatory documentation of how you decided. All things that are REALLY hard to do any other way.
To learn more about decision modeling with DMN, there are several options:
If you are building a sophisticated agentic AI system that is intended to solve complex, real-world problems, you’re going to need decision agents. These systems must make autonomous decisions that directly impact customers, operations, and business outcomes. However, the technology that powers modern agentic AI—large language models—creates a fundamental paradox: while LLMs excel at many tasks, they are poorly suited for the precise, consistent, and transparent decision-making that business-critical applications require.
So why are Generative AI models, LLMs, unsuitable for advanced decision-making? Several reasons:
They are inconsistent by design, not something we look for in decision-making
They are opaque and black-boxy, making it hard to explain why a decision was made
They are poor at mathematical analysis and much worse than other machine learning techniques
It’s hard to make small, focused changes to their behavior, limiting continuous business-driven improvement
A good decision agent should be ruthlessly consistent, completely transparent, easy to change, accessible to domain experts and able to embed advanced analytics and machine learning. Which means they should be built using a platform that meets these criteria – a Decision Platform or a Business Rules Management System. These are widely used and are ideal for building decision agents. For instance, Decision Agents can leverage any of IBM’s Decisions technology – IBM Operational Decision Manager (ODM), Automation Decision Services (ADS), Decision Manager Open Edition (DMOE) or the new Decision Intelligence.
Plus, these platforms can be enhanced with generative AI by using it to ingest unstructured information, explain decisions made in natural language and suggest improvements.
To learn more about decision agents, there are two options:
Business automation is being transformed by Agentic AI. Generative AI (Large Language Models – LLMs) and the Agentic AI pattern together create new ways to solve business problems. As you would expect, I am particularly interested in what you might call “decision agents” – agents in an agentic framework specifically focused on automating business decisions. How do you identify, integrate, specify, design and build such decision agents and what technology and design approaches do you need?
As part of thinking about this topic, I recorded three lightboard videos for the IBM Technology YouTube channel and blogged about them here. Today I’m starting some longer pieces on the three topics.
How AI Agents and Decision Agents Combine Rules & ML in Automation [this post]
Agentic AI combines a new architectural approach with LLMs to significantly improve business automation. It’s particularly well suited for creating systems with greater autonomy and such systems rely on agents making critical business decisions without human intervention. That said, agents based on LLMs have real limitations of transparency, consistency, state management and regulatory compliance of complex logic. Plus relying on large language models for autonomous decision-making fails to leverage existing investments and best practices in business automation.
Multi-method Agentic AI – an architectural approach that combines agents built with large language models with others based on proven automation technologies including workflow and decision platforms – resolves these limitations and leverages existing investments.
A multi-method approach divides agents into different types or classifications, applying the right technology (or mix of technologies for each). For instance:
Chat agents for natural language interactions use generative AI to handle questions and requests from users.
Orchestration agents use generative AI to route interpreted requests to the right specialty agent.
Policy agents use Retrieval Augmented Generation (RAG) and generative AI to answer a wide range of general questions.
Workflow agents use process management technology to handle complex sequences of steps and manage state.
Decision agents deliver consistent and explainable decisions using decision platforms and business rules.
Document ingestion agents use generative AI to extract needed information from free from documents.
Explainer agents translate decisions made by decision agents into natural language for customers and staff.
Companion agents use generative AI to support staff as they handle manual steps and reviews.
While many agents in such a framework do use generative AI, not all do. Such a multi-method approach improves confidence and transparency, leverages existing technology investments AND puts generative AI to work effectively. It combines the best tools for each task, maintains transparency where it matters, and builds systems that serve both customers and stakeholders with excellence.
Along with a bunch of friends and colleagues, I have spent the last few years working on DecisionAutomation.org – a multi-vendor organization dedicated to documenting decision automation best practices. A key focus of this organization has been on how best to use decision modeling with the Decision Model and Notation standard. This matters because OMG, the organization that runs the standard, explicitly forbids “methodology” in its standards – you can’t standardize how to do things, only the result. This means there are elements of DMN that are rarely a good idea, but the standard can’t tell you when they are – as long as there is SOME reason to use them, the standard needs to define them.
To help organizations adopt DMN effectively and maximize their value, DAO identified multiple ways they might want to use DMN. Each of these uses different pieces of the standard and needs different kinds of software support. To make it easy to identify what will work for you, we built a great tool we call the Interactive Usage Scenario tool. This lets you identify what you are trying to achieve and see which elements of DMN and which software features in a DMN tool will help you be successful.
The tool has you pick a business path such as transformation through technology, people development or focusing on a specific target use case – and then pick one of the core scenarios for decision modeling – anything from a simple decision inventory to detailed decision definitions to deployable decision services. Based on these, it identifies DMN and tool capabilities you will need across decision requirements diagrams, model support, decision logic and KPIs, interpretability, implementation and terminology management. It will show you what’s needed, what’s helpful and what’s optional.
You can watch an introductory video here
Check out the Interactive Usage Scenario tool, see who’s participating and sign up at Decision Automation.Org
Business automation is being transformed by AI, specifically Generative AI or Large Language Models (LLMs). More recently, the Agentic AI pattern has gained real traction among those trying to apply LLMs in an enterprise context. Like any new pattern, there’s a ton of hype about Agentic AI but it’s clearly here to stay.
I have been thinking about this new paradigm and this new technology and my 25+ years of thinking about decision automation plays into it. Specifically, how you identify, integrate, design and build decision agents in an agentic framework. This is a hot topic among our clients at Blue Polaris, as you can imagine, and a space in which we are investing heavily.
While I was thinking about this, the folks at IBM introduced me to lightboards and their incredible lightboard channel – IBM Technology – YouTube. There’s a huge array of great videos on here – from very short to very long on everything from AI to data storage to REST to Docker.
I really wanted to be part of this great channel, so I recorded three lightboard videos for it:
The first is an overview of the role of Decision Agents in Agentic AI – How AI Agents and Decision Agents Combine Rules & ML in Automation. This discusses an agentic AI approach to loan origination and shows how Decision Agents can deliver efficient, consistent decision-making in an agentic framework.
The second drills into how a Decision Agent works – Building Decision Agents with LLMs & Machine Learning Models. This discusses how they complement LLMs and how decision agents can combine LLMs, machine learning models and business rules to enable scalable decision-making.
The final one shows specifically how you can design these Decision Agents visually using the Decision Model and Notation or DMN standard – Designing AI Decision Agents with DMN, Machine Learning & Analytics. It shows how DMN lets you think through the behavior you need in a decision agent and design an agent that leverages the right mix of technologies for a successful implementation. You can check out mine and Jan’s book Real-World Decision Modeling with DMN for more details on decision modeling.
I’ve been working in decision automation a long time. While I’ve never been an expert in business process modeling, I’ve interacted with a lot of business process experts, worked on some projects that combined processes with decisions and even helped write a book (the MicroGuide I wrote with Tom Debevoise).
I recently got my hands on a great new book from Packt. I’ve interacted with Jim and Zbigniew many times over the years going back more than 20 years in Jim’s case and at least 10 in Zbigniew’s.
The new big has 10 chapters:
Winning at Digital Transformation with Process Modeling The role of process modeling in successful digital transformations
Pillars of a Successful Digital Transformation Key elements of the various kinds of digital transformation and how to lead them
The Wheel of BPM Driving Your Competitive Advantage Using BPM to drive transformations and start the improvement flywheel
Long term Trends and the Impact on Your Job Why digital change is so central
Business Process 101 The basics
Establishing Process Architecture The architecture
Process Modeling Notations Why we ended up with BPMN
BPMN What You need to Know The core BPMN concepts
Advanced BPMN More advanced stuff and a little (too little) on DMN
Measuring the Business Value of Process Transformation How to measure value and calculate returns – business value assessments
There’s a lot of good stuff here. I really liked how the BPMN mechanics are wrapped with a focus on transformation and business value. The authors tie the act of process modeling to some key business concepts in a really useful way. There’s some good thinking about how AI will, and won’t, impact all of this, and plenty of focus on the human and organizational elements that are so important.
The book reads well and easily, with only small differences in style between the authors – each of whom brings a unique focus to their chapters and complement the others nicely. Its target audience – business professionals who need better ways to design and think about their business as they transform it – will find it easy to read and full of great insight.
As 2024 wraps up, I thought I would share a few thoughts on Artificial Intelligence (AI) in the context of automating your business. It’s been a really interesting year for AI and I suspect 2025 will be more interesting yet.
It’s important to remember that AI > Generative AI. Focus on applying the right mix of technologies to any artificial intelligence problem – business rules and decision automation, statistical models and predictive analytics, neural networks and machine learning, AND generative AI. Few problems only need one of these technologies, most are efficiently solved using a mixture. All these technologies are improving and evolving, and all will have a role to play in 2025. Make sure they’re all in your tech stack.
Given you’ll need a mixture of technologies to solve problems, make sure to adopt a way to define requirements and do design that isn’t tied to a particular technology but let’s you focus on your automation goals. Decision modeling and process modeling are both great places to start. Far too many organizations have great plans for using new technology next year but no matching plan to change the way they plan and design their systems. Make sure you do.
Continuous improvement will always matter. You’re not going to solve the whole problem with v1 so don’t assume you will. Plan for change, evolution and improvement. And capture the data about what you did and how well it worked out so you can do this. Good business-centric design and transparent capture of execution logs will be important next year – more so as your systems get more complex.
Don’t let worries about generative AI stop you getting started now. These tools are getting really good at handling complex inputs (processing documents, supporting conversational interfaces) and explaining outcomes – you can’t afford to wait. Bring foundational models in house and add a good design so you control both how your AI is built and how it is used so you can ensure compliance and safety. Start soon.
Plan for governance. Manage your process, your decisions and the way ML and AI are being used to support both. Even if you are just getting started, you’ll eventually get asked to prove you have everything under control so start early.
Finally, I’m reminded of Amar’s Law – we tend to overestimate the short-term impact of new technologies while underestimating their long-term effects. AI seems likely to follow this law – it’s not going to upend your business tomorrow, but it is going to radically reshape in the coming years.
Gartner has increasingly been discussing Decision Intelligence as a concept, as well as Decision Intelligence Platforms. Recently, they published a Market Guide for Decision Intelligence Platforms, which is highly recommended reading.
Decision intelligence platforms combine explicit decision modeling, AI, analytics and related capabilities to support, augment or automate decision making, driving business outcomes. Data and analytics leaders can use this guide for investing in DIPs to make their organization more decision centric.
Gartner’s focus on Decision Intelligence represents an effort to consolidate fragmented tools and approaches aimed at enhancing decision-making. They correctly argue that the growing complexity of business environments, regulatory demands, and adoption of consumer technology necessitate improved automation and management of decision-making processes. The proliferation of AI and advanced machine learning technologies is further accelerating this trend.
Our longstanding advocacy for building a comprehensive decision management capability—by focusing on platforms and techniques that allow broad application of decisioning technology rather than piecemeal solutions—aligns well with the report’s emphasis. It is also encouraging to see AI, simulation, data science, and business rules highlighted collectively, a stance we have maintained for more than 15 years and is one of our three best practices.
In addition to technology, Gartner is talking more about modeling decision-making explicitly. This aligns with another of our three best practices – DecisionsFirst as we like to say. However, Gartner still promotes flow-based models for decision-making, whereas the industry has shifted towards declarative models like those based on the Decision Model and Notation standard. These models facilitate quicker requirements gathering, greater business engagement, increased reuse, and better integration of rules with AI and machine learning.
Their emphasis on monitoring and continuously enhancing decision-making is crucial and constitutes the third of our three best practices. Since decision-making is dynamic, it is essential to build systems with flexibility for change and improvement. Selecting Decision Intelligence Platforms based on their support for maintenance and enhancement is therefore key.
As for the technology vendors themselves, they’re a real mix. Some are very focused on specific pieces of the platform (simulation or collaborative decision-making) while others are “legacy” vendors with business rules, optimization or analytic platforms. Some though really do have a complete platform – certainly for the kind of high-volume, operational decisions we specialize in.
If you are looking to adopt a Decision Intelligence Platform or need assistance in evolving your existing system to provide more comprehensive support for robust decision-making capabilities, please reach out.
We’ve been focusing on this for quite some time – long before it was cool enough to have a Gartner market name! ?
As regular contacts of mine will have noticed, we have a new company name and a new brand
Artificial Intelligence (AI) is changing the way companies approach technology, and we are broadening our palette of technologies and services to match. A new name, and a new focus, are required. Blue Polaris will still be helping companies apply machine learning (ML), AI, business rules and decision modeling but we’ll be helping think about how AI changes their workflow and document management, how governance needs to evolve to bring ML/AI into compliance, how to capture all the knowledge they have and much more.
It’s been 15 years since I founded Decision Management Solutions and I’m super-excited about this next chapter.
You may have noticed articles about a chatbot recently that got a little out of line – an airline’s chatbot misstated the rules for a fare class (see The Guardian‘s article or The Washington Post‘s). The airline has, of course, been held accountable for its chatbot – just as it would have been for an employee. Two key lessons can be learned from this outcome:
You are responsible for everything your chatbots say, even their hallucinations and errors. I would have thought this was obvious but apparently the airline’s lawyers thought that blaming the chatbot might work!
You don’t want your chatbot making decisions about things like eligibility, pricing, discounts – decisions that are regulated, based on complex and published policies, and that impact customers.
The airline’s intent here was a good one I think – use a chatbot to make it easier for people to get answers to questions about the notoriously complex topic of fares. The power of Large Language Models (LLMs) and Generative AI (GenAI) to power more interactive chatbots is real and is going to change how consumers use your website and understand your intent. They can dramatically improve explicability, making your website/systems easier to use, easier to understand and fundamentally less technical to access.
But there are issues. What AI chatbots say is not always reproduceable. They may hallucinate – sometimes spectacularly and with references! How they work is largely inexplicable – especially to regulators. And even bad answers look like good ones. And, as this story shows, you’re going to be held accountable for them.
The solution is not to dump LLMs/GenAI from your roadmap but to recognize that this technology has no sense of the truth or facts and simply generates the most likely content – it’s not prescriptive. You need to add prescription so you can precisely define what the chatbot should do in which circumstances that is based on ground truth and factual content. While LLMs and GenAI are great for interacting with customers and explaining results, they can’t be trusted to prescriptively make regulated or policy-based decisions.
Adding decisioning based on business rules – explicit decision logic – grounds their behavior in facts and rules. Modern decisioning platforms are great for transparency and consistency, especially when decision modeling is used to manage the logic. Using a decisioning platform to automate decisions like eligibility (for a fare, product, service or benefit), dynamic or complex pricing, risk assessment gives you precise business control over your decisions. Unlike a chatbot, the logic is explicit, explainable and managed.
So why not JUST use decisioning? Decisioning platforms deliver APIs aimed at internal systems. The decisions are compliant, precise, transparent – but not accessible to a customer. Typically, you have to put all the data needed for the decision into forms and processes before you can get an answer. Adding LLMs/GenAI to handle the interaction provides a customer-friendly interface to the decisioning APIs and delivers both a great interaction and reliable, compliant decisions.
There’s huge potential in AI, yet far too many AI projects fail to deliver meaningful business value. The technology works, the team has skills, the data is available – and yet it never comes together. To address this critical issue, Eric Siegel has just published The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. In The AI Playbook, Eric has distilled years of experience and a ton of great advice into an easy-to-follow roadmap for success.
Even before its release, the book hit the #1 slot on Amazon’s top 100 Hot New Releases in Technology. Fast Company said “An antidote to overheated rhetoric of all-powerful AI… helpfully lays out the key steps to deploying the technology we’re now all obsessed with.” while The Forecast said it “Separates AI fact from AI fantasy.”
Eric’s approach – BizML – puts deployment and business value at the heart of machine learning and artificial intelligence projects. Like Eric, I see a focus on the business problem – what I call the decision and Eric calls the deployment goal – as the essential first step. We’re also both big believers in making sure you deploy the model into business operations and focus on continuous improvement. Eric’s book does a great job of outlining where these steps fit and illustrates the whole with some compelling story. If you’re interested, there’s more information, including a nice cheat sheet for the approach at http://www.bizML.com but you should really just buy the book.
Most practical use cases of ML — designed to improve the efficiencies of existing business operations — innovate in fairly straightforward ways. Don’t let the glare emanating from this glitzy technology obscure the simplicity of its fundamental duty: the purpose of ML is to issue actionable predictions
This focus on improving existing business operations in a straightforward way is critical. We see a lot of companies spending a lot of money on ML and AI. Much of it is wasted because the ML/AI team, keen to show how smart they are and to justify the investment, insists on putting all their effort into “transformational” projects or “new businesses”. The potential for ML to improve their current business in meaningful but boring ways is ignored. These ML/AI teams are often more focused on using the coolest technology, so they will be hired by bigger companies and be given bigger budgets, than they are on delivering business value NOW.
In contrast, successful ML/AI teams are ruthlessly focused on incremental improvements – taking well understood problems in the business and using machine learning to improve results in each area in a very focused way. Often the improvement is small at a per-transaction level but the team focuses on high-volume problems, multiplying that small improvement by very large numbers of customers, products or transactions.
Not only does AI hype tend to distract from these very practical problems, it tends to result in a model-first or technology-first mindset. It becomes more important that the project uses AI than that it generates results. As Eric goes on to say:
This exacerbates a significant problem with ML projects: They often lack a keen focus on their value — exactly how ML will render business processes more effective. As a result, most ML projects fail to deliver value.
Our experience is that you really need:
A clear understanding of what decision needs to be made differently to generate a result
A detailed awareness of how exactly your ML model will influence that decision
A sense of what organizational change will need to happen to get from the current decision-making approach to the new one.
We ensure this on our projects using decision modeling and our DecisionsFirstTM Approach to projects. This means we always know how the decision is being made, and can automate most of it, before we start applying ML to improve it.
AI Decisioning Platforms are a superset of ML/AI Platforms (which Mike also covers) and this wave represents an evolution – it started as a review of Business Rules Management Platforms, evolve to talk about Digital Decisioning Platforms and now focuses on AI Decisioning Platforms to emphasize the value of these platforms to those deploying and exploiting AI.
Mike pointed out that making decisions is the best possible use case for AI – especially as you should consider making a recommendations as a decision. He emphasized that enterprises rise or fail based on the collective efficacy of their decisions. And, while some of those decisions are big, strategic decisions, many more are rapid, transactional and operational. He also pointed out that insights are perishable, real-time insights especially so, meaning that decisioning really matters to the effective use of real-time insights. And as the time to decide shrinks, enterprise need to do more real-time decisioning.
Legacy architectures are not geared to this kind of data provisioning while legacy development approaches – writing code – is not going to keep human experts in control. Mike thinks a focus on “human governed AI” is essential and this means using an AI decisioning platform that combines a broad set of technologies, supports rapid learning loops, and can have industry accelerators.
Before getting into the details of the platform, Mike reminded the audience to begin “Decisions First”, pointing out that before using one of these platforms you need a decision model that combines several elements- rules, ML, AI , optimization. Our experience tracks strongly with Mike’s – you NEED a model first, ideally one built using the Decision Model and Notation (DMN) and a top-down, business-centric approach.
He then identified 9 of the most important criteria that were used in the Wave
Data An ability to connect to sources, manage features and pipelines, support data annotation and cleansing.
Provide a range of intelligence technologies
Statistics and queryable analytics
Pure math
Constraint based optimization / Operations Research / Mixed Integer Programming
Machine Learning
Human decision logic as rules, policies knowledge and processes. Capturing this business expertise is an essential feature of an AI Decisioning Platform he said.
Low/no code. Tools for business experts e.g. decision modeling, abstraction as well as productivity tools for data engineers, data scientists and developers.
Composability and reuse to drive enterprises decision agility, strong collaboration tools
Trust, understanding and transparency. Business simulation is a critical element.
Management (several layers up on top of Kubernetes)
Model Ops – not just MLOps but a more holistic Ops function that deploys your whole decision model
Multiple deployment options
Scalability
Mike wrapped by pointing out that AI Decisioning can’t deliver itself – business users need to define the strategy criteria. Business experts MUST decide what and how to decide!
You can get reprints of the report directly from Forrester (if you are a subscriber) or from vendors like FICO who might offer it for free. If you want help selecting an AI Decisioning Platform or maximizing the value of one, drop us a line – that’s what we do.
In March 2023, three U.S. banks failed. This triggered a sharp decline in global bank stock prices and swift response by regulators to prevent potential global contagion. Banks across the US scrambled to respond to the crisis.
Join me on May 24th for a discussion on how you can protect your business and be prepared for the next crisis. You’ll learn how to achieve the flexibility and agility you need to navigate rapid market change. You’ll understand the tools you need for dynamic impact analysis.
During the webinar, we will discuss:
How to recession-proof your business by ensuring your infrastructure is prepared for rapid change in market conditions and regulatory environment.
How to simulate the impact of market, interest rate and regulatory changes for loss forecasting and stress testing.
Managing and automating loan origination to maximize value, reduce defaults and safely stay in the market.
It’s time to modernize your platform with digital decisioning. Even if you already own the right infrastructure, it’s time to make it more nimble and responsive.
Our friends at IBM are runningran a webinar on May 9 that is a great opportunity to see decision automation in action:
As expectation grows for faster and more personalized digital experiences, business decisions are increasingly important – and often more complex. Intelligent decisions that fuse predictions and policies can deliver more effective decisions that help every element of your businesses run more smoothly. Join us for an insightful webinar that explores this new domain:
Why intelligent decisions
Anatomy of intelligent decisions
Examples of intelligent decisions by industry
How you can build intelligent decisions
Register to join here and get your calendar invite
The slides are available to download below and please share any of yourquestions here.
I worked on a paper for IBM called “Operationalizing AI: Beyond AI Pilots with Digital Decisioning” on the same topic. This is very much top of mind with our customers these days – how do they use a rules-based decision platform to effectively operationalize advanced machine learning and AI models.
Our CTO Ryan Trollip is presenting with Scott Horwitz from FICO in a great webinar coming up on April 20th:
Insurance claims management is a complex business. Customers want their claims processed and approved quickly. Insurance providers need to manage risk, improve scalability, retain institutional knowledge when staff changes, reduce overhead costs of management, and comply with myriad government regulatory requirements.
To optimize operations and meet all of their compliance requirements, many insurance companies have a goal to increase the percentage of claims that can be processed and adjudicated with no human decision-making involved. In other words, increasing their rate of straight-through processing.
Drawing from lessons learned from our customers, Decision Management Solutions and FICO will share how organizations can:
Simplify the management of the decision-making capability by automating it
Visually provide clarity and elicit valuable knowledge from working with decision models
Clarify the What and Why when streamlining and automating the decision making
It’s that time again – time to take the Rexer Analytics Data Science Survey
Rexer Analytics has been conducting this survey since 2007! Each survey explores the analytic behaviors, views, and preferences of data scientists and analytic professionals. This year Karl is working with Eric Siegel and Machine Learning Week to design, promote, and analyze the survey.
Summary reports from previous surveys are available FREE to download from the Rexer Analytics website – and are fascinating! Karl Rexer and Eric Siegel will present highlights of the 2023 survey at the Machine Learning Week conference in Las Vegas in June 2023 (I’m speaking and teach there too) and a full summary report will be available for download from the Rexer Analytics website later in 2023.
It’s completely confidential and not being conducted for any vendor. The survey should take approximately 10 minutes to complete. So go take it…
I got my hands of a copy of Krishna Pera’s new book, Big Data for Big Decisions recently. I met Krishna several years ago when he published some articles on being decision-driven not data-driven and on why it’s essential to prioritize decisions for your analytic efforts. He’d found some of my articles on being decision-centric and we connected. Now, one pandemic later, I’m delighted to be able to review the book that resulted from his experience in this topic.
The book’s subtitle is “Building a Data-Driven Organization” and it covers how to begin the journey, how to focus on the right (“big”) decisions, the challenges in getting value from analytics, data strategy and much more. His focus throughout is on building a robust roadmap and an enterprise-level plan. Crucially, he wants those establishing data-driven organizations to focus on the decisions that add the most value to the organization. This specific focus on decisions and on selecting the right decisions is key to the book and, indeed, to succeeding at becoming data-driven.
He encourages an assessment of your current state and the development of a coherent roadmap. A new operating model is going to be required to become insights-driven and understanding this new model will give you a sense of where to make strategic investments. He focuses immediately on explicitly assessing and improving decision-making (rather than asserting that better data will lead inexorably to better decisions as so many do). Furthermore, he emphasizes tying improvements in this decision-making to concrete business value.
His prescription for creating the organization begins, as it should, with a discussion of decisions and the importance of beginning “with the decision in mind” when considering data and analytics. He dives right in, pointing out that most organizations lack any clear understanding of their decisions, except perhaps for purchasing and investment decisions. They don’t really know what their decisions are, who has what role in those decisions, how those decisions are made or how they could be improved. To address this, he recommends an immediate investment in understanding these decisions by modeling them (ideally using the Decision Model and Notation or DMN standard).
His chapter on finding the “big decisions” of the title is particularly worthwhile. He provides some good insights on how to prioritize decisions – comparing high impact but rare decisions with those that offer low value per decision but very high volumes for instance. The key, he says, is to find a core set of decisions that offer your organization the most value. If you can make those decisions data-driven, you’ll realize most of the analytic value available to you.
He goes on to discuss the potentially elusive value of analytics in decision making. To address this, he encourages a focus on incremental improvement to known problems rather than pure research-oriented analytic projects. Then you can prioritize decisions based on both the potential for analytic improvement and the likely cost and complexity of data-driven improvement.
With the value of analytic improvements in decisions clearly identified, he transitions to discussing data challenges and an IT strategy to support data driven decision-making. There are many elements to such a strategy, and he does a nice job of outlining how these elements come together to support a data-driven organization. I particularly liked the discussion of an information supply chain and his ideas around mapping system and IT maturity to the data and analytic needs identified. He wraps up with solid chapters on data strategy, data governance and on data-driven marketing as an example.
One of the great things about Krishna’s book is that he cites a huge number of books, papers and articles, giving you a rich set of information to drill into. He also leverages established ways of documenting business, data and IT plans and strategy. He shows how these established techniques can be used to benchmark, analyze and re-design an organization to become data-driven and apply (big) data and analytics to critical decisions.
If you are trying to make your organization data-driven and striving to use analytics, machine learning and AI to improve your business decisions, this book should be on your bookshelf.
Here at Decision Management Solutions, we’re helping our clients become truly data-driven through decision automation. Automating and improving the most common decisions in your organization creates immediate business value because these operational decisions literally run the business. While some of our clients have already identified the best use cases for decisions automation, we often help clients with an assessment to help them prioritize their investments in decision automation and data infrastructure. Krishna outlines an approach very similar to the one we use – so I am confident it works!
Buy Krishna’s book to help you put a plan together and get in touch if you need some help!
We do a lot of work in insurance and we seem many companies spend heavily automating their claims process. Their intent is to improve their loss ratio – both by reducing losses due to fraud or bad claims and by reducing processing costs. But it often doesn’t make much difference because they are focused on handling a claim efficiently not effectively. Digitizing claims documentation and the claims workflow might help pay a claim more cheaply, but it does little to ensure that the right claims are paid or that the right approach is taken. And if most claims still need to be manually reviewed, it won’t do that much to reduce processing costs either.
In contrast, our customers invest in digital decisioning to make sure that their claims handling decisions – which claims to pay, when to investigate, when to fastrack – are digitized. This focus reduces fraud and waste, assigns the right people at the right time and reduces manual work for a really significant bottom-line impact on loss ratios
To illustrate what this means, I wrote three blog posts over on the company blog about real customer stories:
