We blogged about the need to react quickly and accurately to change already as this is one of the key ways you can adapt your operations. A second is to ensure that you can build and sustain your customer relationships even as your customers are more remotely and have fewer in-person interactions with you.
The new era is going to require much more automation to support remote interactions. Customers are going to be more remote. They’re going to be under more stress too, so quicker responses will be more valued than ever. And the staff you would need to support a manual decision may be remote themselves, making manual responses problematic. Automated customer interactions can’t be cookie-cutter, though. Customers still want to feel that you know them and value them. You can’t pick between automated and personalized when your customers want both. And you still need the flexibility we talked about earlier, because all of this is a moving target.
An organization we work with recently began investing in building and sustaining customer relationships online, having for years relied on in-person interactions. When prospective customers start looking online at information about products, this organization makes sure it presents the right first product. Using everything it knew about the prospect, it presented targeted (and compliant) “first best offers” to these clients. Engaged customers could buy the product online but it was a relatively complex product to buy so many would choose, in the end, to talk to someone. So now the lead, the initial offer and the partially completed application are all routed to the right point of contact. Rapid feedback on customer behavior and flexible automation let them keep the initial conversation feeling personalized and relevant even as products, marketing campaigns and the day-to-day business context changes. The automation meant that customers could choose to no people when automation was what they wanted and could be routed to the right person when it wasn’t.
This organization applied this same mindset to helping its agents in this new, online engagement approach. Agents used to in-person meetings are threatened by digital channels, and by fully online direct-to-consumer competitors. Helping agents manage their customer portfolio, identifying opportunities to engage with existing customers around special events or campaigns, and helping the agents target customers and prospects precisely all help agents add value to the customer relationship, even as it becomes an increasingly electronic one.
To deliver systems that engage remotely with customers in this way requires automation of the underlying decision and the targeted application of machine learning. You can’t just look at the data though, you need to combine what the data tells you with what your expertise tells you and filter all this through the current (changing) state. The analytic insight you develop must be in service to the customer treatment decisions you are trying to make. Decision modeling will frame up the analytics you need and, integrated with business rules, deliver targeted customer treatment across all your channels.
We have a proven, established way to build systems that will engage customers remotely and we’ll be posting regularly on ideas and approaches and producing some great content. To stay up on it, sign up for our newsletter.
One of the key ways you can adapt your operations is to develop systems and processes that can react quickly and accurately to changes as they happen. The response of governments at the city, state and federal level is going to evolve as the situation changes. The behavior of customers, partners, suppliers, agents and distributors is going to change too. You need to keep operating through all this change, without creating unnecessary manual clean up, without “hacking” temporary fixes every few weeks and without putting yourself at risk for legal or compliance (or publicity) problems.
One of the persistent myths in systems development is that systems and processes can’t be both flexible and compliant. COVID-19 puts this old saying under real pressure. If you work in a regulated industry (insurance, financial services) or deal with regulated consumer data, your regulators are not suddenly going to give you a pass because there’s a pandemic on. They might be a little more flexible, give you a little more time to report things or adopt new policies, but they’re going to expect you to remain compliant – even as what it means to BE compliant changes all the time. Now you really need to be flexible AND compliant.
One marketing department we worked with has the kind of flexibility you need. They can change the marketing offers they make to new customers whenever they need to. They can try multiple approaches to picking an offer to see which works best in rapidly changing circumstances. They have the transparency and auditability they need to show regulators that they never tried to sell products to people not eligible for them. This kind of system allows you to turn on a dime as states relax and tighten restrictions, guidance changes and new polices and regulations come out.
The key to compliant flexibility lies in business enablement. Like the marketing department in this example, you need business owners to understand and be engaged in managing the system, to be empowered to make the changes they need. They are the ones closest to the regulations, closest to the customer, setting the policies. When they can see how the system is behaving, change the way the process works, and drive the outcomes they need then you’ll get the quick, accurate, compliant response to change that the new era is going to require.
Building these kind of flexible yet compliant systems requires a focus on the decision-making embedded in these systems and on exposing that decision-making so business owners can change it themselves. The decision making is what changes the most often and the business owners are the ones who see those changes first and who understand them best. Decision modeling, business rules management and a focus on continuous improvement all contribute to developing these systems that can react quickly and accurately.
The good news is that there are proven, established ways to build these systems and we’ll be posting regularly on ideas and approaches and producing some great content. To stay up on these ideas, why not sign up for our newsletter.
The COVID-19 Coronavirus has upended the world economy – and your business. While the impacts to date have been dramatic, we have to face the fact that this is the beginning of a new normal – a world in which this virus circulates. This will have short- and medium-term consequences for your business regardless of the industry you operate in.
You’ve probably spent the last few weeks re configuring everything to function effectively as a remote enterprise. You’ve dealt with the immediate impacts like surges in VPN access, new laptop requirements, making processes work when people are working from home. Plus ,you’ve survived the immediate economic hit. In the coming weeks and months, organizations like yours will begin to focus on cost optimization and seeking opportunities in the new environment. Some companies will succeed at this – they will adapt, survive and thrive in this new environment. Others will fail.
Now is the right time to be thinking about how you should adapt operations to cope with the new normal. It’s time to plan because this is not going away. The problems you are facing will change and evolve but there’s a new normal coming and you’ll want to have new systems and processes to cope.
How will you create targeted, engaging and profitable interactions with customers you will never meet? And sustain relationships you started offline now they must move online?
How will you create resilience in your supply chain and other processes, without tying up more capital? How do you ensure you can adapt rapidly to shifting targets, regulations and opportunities?
How will you shift your adapt operations to handle an increasingly volatile and dynamic world? Can you assign and allocate resources based on what’s happening now, or soon, not on how things used to work?
Our focus here at Decision Management Solutions is on digital decisioning – using our DecisionsFirst™ approach to apply technology and deliver automated solutions to decision-making problems. The systems we help our customers build address exactly these issues: they ensue you can personalize and target the digital interactions you have with customers; they deliver resilience, transparency and agility; and they support dynamic assignment and allocation.
We’ve learned a lot about building these kinds of systems over the decade or more we’ve been doing this and we’d like to share some tips and help you see how you can do it too. We’ll be posting regularly on ideas and approaches and producing some great content. To stay up on it, sign up for our newsletter.
Some of our old friends at Gartner have just published some great research on Decision Management. Specifically they have extended their work on Decision Management Suites (blogged about here) and focused on How to Choose Your Best-Fit Decision Management Suite Vendor [Gartner subscription or modest fee]. As they say in the intro:
Decision management suites go beyond business rule management systems by providing more features for designing, deploying, maintaining and auditing decision-making software. This report describes the steps data and analytics leaders should take to identify the best vendor for their business needs.
Gartner
Selecting a decision management vendor or business rules management system is something we here at Decision Management Solutions do a lot so I was excited to see what the Gartner team had come up with.
They suggest that you identify potential providers by considering their support for both business rules and analytics. As I have described in my books (most recently Digital Decisioning: Using Decision Management to Deliver Business Impact from AI ), the decision services you build are going to need business rules, are likely to need machine learning or predictive analytics and may need optimization (though much less often). Streaming analytics and event processing are included in this list by the Gartner team but I see this more as a niche market – generally not relevant but occasionally critical.
Building decision services with a mix of these elements requires decision modeling – specifically decision requirements modeling. You can build a decision requirements model that pulls together business rules, machine learning and optimization to give you an effective, graphical blueprint. Don’t leave home without one. We build a lot of decision services and we would never do so without a decision model. You shouldn’t either.
Below is the list of topic areas they considered and they have some great content in the paper. Based on our experience developing decision services and modeling more than 4,000 decisions, I have a few additional comments:
Ease of Authoring Our experience is that this is all about decision requirements models and associated decision tables. People often evaluate other authoring elements but they don’t really matter – business users like decision tables and decision requirements models are essential for getting decision tables right..
Application Solutions and Templates
Operating Environment
Build, Version and Deploy Always consider this as two separate threads – one focused on how non-technical users do versioning and deployment, one on integrating with IT’s processes like CI/CD. Don’t mix them and don’t assume that being good at one makes a platform good at the other. Some platforms are very programmer-friendly but baffle business users.
Scalability and Latency
Logging, Monitoring and Evaluating Remember that the data you capture to track how you made decisions and how that worked out for you will drive continuous improvement. This is much more important than the logging or monitoring of rule execution which is interesting only sometimes.
Process Orchestration/Workflow Meh. Build stateless, side-effect free decision services and leave the workflow somewhere else.
Simulation This is REALLY important. Don’t miss this. And don’t confuse it with testing, which it is not. Testing is to see if something is broken. Simulation shows the impact of a change. Business users make lots of changes that should not result in test failures. Make sure they can simulate the impact of a change before they commit it.
Rule Validation
Microsoft Excel Support Not a fan. Excel is super flexible but that’s not necessarily a good thing. Either commit to a product that does everything inside Excel or one that provides decent editors. Don’t get stuck in the middle.
Rule Harvesting Never harvest rules until you have a decision model in place. Ever. Really, just don’t. Please.
Supporting this paper is a Toolkit: Decision Management Suite Vendor Profiles [Gartner subscription required]. While I don’t think some of these are really Decision Management Suites – a couple are really workflow or streaming engines with some rules support thrown in – its mostly a good list and a reasonable framework to use. Just remember, as the authors point out, to weight the factors that matter to your project.
If you’d like our help selecting a vendor or platform, contact us and we’d be happy to talk you through what we do. If you want more background on decision requirements modeling, check out this paper.
Balancing multiple profit drivers is essential in many high volume, transactional decisions targeted with Decision Management. Being able to execute decisions, monitor their effectiveness, learn what works, and systematically improve your decision-making results in significant business benefit. Mathematical optimization let’s you go even further.
This paper lays out the proven steps to adopting decision optimization, walking through how to codify your current practice, systematically improve your decision strategy and apply mathematical optimization.
We’ve published a couple of follow-up blog posts over on the company blog too:
Don’t Just Sit There, Experiment! Continuous and systematic improvement in the quality of your decision-making requires an investment in experimentation.
Why not download the paper and start your own “Journey to Decision Optimization”?
If you’re stuck sheltering in place or bored working from home, we have a great opportunity for you. You can learn a vital new skill – Decision Requirements Modeling – from the comfort of your home FOR FREE!
We are going to take some of our popular online training materials and provide a FREE 2-hour introduction to modeling decision requirements. This session will focus on the most valuable feature of decision modeling, its power to accurate capture how business people want a decision to be made.
Decision requirements modeling captures business decision-making using a graphical representation – a decision requirements model using the Decision Model and Notation (DMN) standard. This powerful visual analogy is used place of technical requirements and enables practical, creative design of solutions to your decision-making problems.
The class is 2 hours, it’s FREE and its on April 3rd, 2020 10am-Noon Pacific (1pm-3pm Eastern)
We’ll introduce decision requirements modeling and the DMN notation, teach you how to define decision requirements using the DMN standard, show you how to drive decision-centric design sessions and help you get started using decision requirements modeling on your projects.
It’s 100% online, 100% FREE and interactive – you’ll be building a model in the class. Sign up now!
I recently got together with Irene Lyakovetsky of Saugatuck Worldwide and well known independent analyst Jim Sinur to talk Digital Decisioning. Irene released our talk on her Saugatalk channel as a great series of snippets:
Balancing key profit drivers such as revenue, risk, & costs is central to success in lending and many other high volume, transactional decisions targeted with Decision Management. The decision improvement lifecycle is central to delivering the full value of decision management. Being able to rapidly and effectively execute decisions, monitor their effectiveness, learn what works (and what does not), and iteratively improve your decision-making results in significant business benefit. Add mathematical optimization and you can go even further.
Designed to help you optimize your decisions and aimed at rules and business analysts, this paper lays out the proven steps to adopting decision optimization. It walks through how you can codify your current practice, systematically improve your decision strategy and apply mathematical optimization to make better decisions. It will show you the value of decision optimization and identify the critical success factors.
Decision Optimization can accelerate your success by helping you develop decision-making approaches that better meet your goals and objectives. This paper will show you how.
Over the next few months we are going to publish some supporting blog posts, focusing in on some key aspects from the paper. Next week is the first, discussing the importance of continuous improvement, and the others will follow every other week.
Meanwhile, don’t forget to download the paper and start your own “Journey to Decision Optimization”.
At Decision Management Solutions we do a lot of work with IBM and an old friend of ours – Harley Davis – just published a nice blog post “How do you best operationalize AI?” We’ve had many conversations on this topic with Harley and his team and it’s no surprise we’re completely aligned on this.
Harley outlines three steps:
Reimagine your process Specifically by identifying and modeling your decisions
Assemble a decisions team One that has all three legs – business operations, IT and data science/ML
Map the data Make sure the ML/AI algorithms can use the data available to make the decision where it is in the process
We have a whole approach for these kinds of projects – what we call DecisionsFirst™. We have successfully applied this with multiple clients, helping them operationalize their machine learning (ML) and AI investments and deliver more effective decision-making. We see three critical success factors:
DecisionsFirst Design Thinking Always begin by identifying your decisions, modeling them directly with the business owner and using design thinking approaches to ideate ML and AI opportunities
Mix and Match Technology Use ML and AI technology for sure but mix it with business rules technology (using a Business Rules Management System like IBM’s ODM) and use a decision model to coordinate all the pieces.
Continuous Improvement It’s not about the first version you deploy, it’s all about how regularly and effectively you can improve it. Invest in business enablement and continuous testing, experimentation and improvement.
Historically, organizations have codified their best practices in rules. Now they are hungry to take advantage of the possibilities of AI to leverage the wealth of information in their historical data and take even better decisions. They are struggling to do so. James Taylor makes it clear how to succeed: By combining business rules and machine learning in a single digital decisioning framework. With clear explanations and examples based on years of practice, this book lays out what can be accomplished with digital decisioning platforms – and how to go about it successfully.
Craig Bedell – an insurance industry luminary and old friend – published a great article last week on Carrier Management – The Insanity of Analytics in Insurance (NOW FREE – no membership required).
Despite an abundance of optimism over analytics, AI and more, the insurance industry still has challenges realizing the potential benefits of these new insights. Upon simple examination, the reasons are rather straightforward. “Actionable insights” are only valuable when applied to the day-to-day business of insurance and its processes and decision-making.
Craig’s perspective matches our experience in Insurance too – it’s easy to apply analytics to your data, and spend a lot of money doing so, without getting much of a return. It’s just too easy to get fixated on the cleverness of the analysis, the power of the analytic tools, even the accuracy of the prediction, while loosing sight of the business value – better decision-making. As Mike Gualtieri of Forrester once said:
if analytics does not lead to more informed decisions and more effective actions, then why do it at all?
Mike Gualtieri, Forrester Research
This sounds simple to solve. Once I have analytic insight, surely I can just use it to improve my decision-making? Well no. It turns out that this is nearly impossible to do. Insurance companies, like most large, established companies, are hard to change. A piece of analytic insight, no matter how powerful or predictive, is not generally enough to turn the ship.
The traditional approach, which does not work well, is:
Assemble data
Do analysis
Deliver insight
Get better decision-making
This leads to the insanity Craig talks about because it is to hard to change the decision-making. To succeed, you have to approach the problem backwards:
What decision do I need to improve?
What analytic insight would help me improve?
What data do I need to produce that insight
This approach – DecisionsFirst™ as we call it – is the core of our approach to Digital Decisioning. Only by focusing explicitly on the operational decisions that matter to your business can you avoid the insanity of unactionable (or at least unactioned) analytics.
As the team at Forrester put it so nicely:
Enterprises waste time and money on unactionable analytics and rigid applications. Digital decisioning can stop this insanity.
According to IDC, 85% of enterprise decision-makers say they have two years to make significant inroads into digital transformation or they will fall behind their competitors and suffer financially.
While many Digital Transformation initiatives are focused on improving the customer experience, often too little attention is paid to the customer-facing operational decisions that impact customers every day. To get the most from your Digital Transformation efforts, your customers’ experience and the decisions that impact it cannot be ignored.
That’s why experts recommend incorporating Digital Decisioning into your Digital Transformation strategy. Digital Decisioning is a proven approach to operational decision-making that radically improves performance. Digital Decisioning is also the key to delivering real value from AI and machine learning.
In this webinar, hear Digital Decisioning pioneer, James Taylor, explain how this approach is a key component to achieving your objectives with your Digital Transformation initiatives. He will discuss several valuable use cases and show you the path to the winning side of Digital Transformation.
The Machine Learning Times (previously Predictive Analytics Times) is the only full-scale content portal devoted exclusively to predictive analytics. It has become a standard must-read and machine learning professionals’ premier resource, delivering timely, relevant industry-leading articles, videos, events, white papers, and community.
In his article, Eric warns, “Predictive models often fail to launch. They’re never deployed to drive decisions. This is ultimately a management error. We must pursue the business of machine learning only so that it delivers the business value of machine learning.” and goes on to illustrate how “the analytics and number crunching alone do not determine what to actually do with a model’s predictions – only business acumen can dictate how to best deploy a model.”
Eric also mentions my new book, “Digital Decisioning: Using Decision Management to Deliver Business Impact from AI,” stating “This comprehensive book guides you to leverage the potential of machine learning. It delivers the business-level finesse needed to ensure predictive models are operationalization-ready. It lays the groundwork and sets the standard. It’s a great place to start… and to finish.”
Are you looking for a
career where you can orchestrate, execute and support the go-to-market strategies
for FICO’s platform, products, and solutions? Then look no further – the
Portfolio Marketing role is for you.
FICO’s Decision
Management practice represents our core set of cloud-ready, platform
technologies for FICO’s B2B offerings for financial services, and insurance, as
well as other industries across the globe. As Director of Portfolio Marketing,
you’ll be responsible for the messaging, positioning, and enablement of FICO’s
products and solutions, and for driving the strategy for integrated campaign
management. This position focuses on FICO’s core decision technologies
including FICO Blaze Advisor/Decision Modeler, Strategy Director, and Decision
Central, which are all essential to businesses seeking to transform through
centralized decisioning, operationalizing AI, and other decision-centric
strategies.
What You’ll Contribute
Design and
validate the business plan for bringing FICO’s portfolio, capabilities, and
product/solutions to market. Articulate FICO’s decision technology value
proposition in the context of broader FICO initiatives, as well as contextually
within specific customer industries or solutions (working collaborative with
Risk and other teams as appropriate).
Craft positioning,
messaging and value propositions that resonate with specific audiences.
Understand influencer and buyer personas and how they buy, to inform campaigns,
content, and innovation. Prioritize and localize messaging for specific regions
and industry impact. Create content that is reflective of positioning and
messaging, and that maps to stages of the buyer journey – and that also helps
accelerate deals.
Engage
with multiple constituents – field marketing, digital, analyst relations, sales
and pre-sales, product management, public relations, community, customer
advocacy and partners – to help validate, review, and deploy content for those
specific uses.
Continually
validate personas and build out customer journeys for the decision technology
product areas, and work with other portfolio marketers to collaborate on
efforts that also leverage decision technology (e.g., Platform, Decision
Optimization, Auto Finance, Telco, etc.).
Manage
competitive analysis, both at the core product level, as well as FICO’s overall
DMS/Platform level (working closely with other DMS portfolio marketers).
Drive
marketing new product introductions and define strategy for content
distribution and adoption of our decision technology offerings. Help manage
product naming initiatives, track analyst reports that correlate with emerging
decision technology and related topics and collect and share market data as it
becomes available.
Working
closely with the FICO DMS Community Manager, oversee the ongoing development
and expansion of the Decision Technology group in the FICO Community, including
developing blogs/vlogs, release content, product and industry pages, and other
specific content as it adds value for our extended user and prospect community.
Develop
and execute a plan for developing and delivering product demos, leveraging SMEs
such as product managers, pre-sales consultants, and others.
Grow
and defend market share of offerings via knowledge transfer, content, and
programs for internal stakeholders – in particular, sales partners.
What We’re Seeking
BA, BS
or MBA in a related field, with at least 10 years of relevant product,
solution, and/or portfolio marketing experience.
Demonstrated
ability to effectively collaborate with a variety of stakeholders, from
technical resources to C-level executive management.
Solid
understanding of and experience within any or all of decision management,
analytics, data science, and platform technologies.
Ability
to serve as an ambassador and evangelist for decision technologies with both
internal and external stakeholders.
Ability
to rapidly brainstorm and develop content, often under tight time constraints,
for a variety of uses.
Proven
ability to rapidly shift directions when business priorities change, and also
consistently rebalance work priorities to ensure tight alignment with other
constituents.
Software
skills: Word, PowerPoint, Excel, Camtasia and Salesforce.
Cassie Kozyrkov – Head of Decision Intelligence at Google- has a great piece on 12 Steps to Applied AI. As usual she’s got lots of great tips. I don’t have anything to add to her more technical steps but I want to add some commentary on Step 0 and Step 1.
Let’s start with her Step 0 thoughts:
Check that you actually need ML/AI. Can you identify many small decisions you need help with? Has the non-ML/AI approach already been shown to be worthless? Do you have data to learn from? Do you have access to hardware? If not, don’t pass GO.
Great focus here on operational decisions (small, transaction-level decisions you make many times) not on big, one-off decisions. Also good to make sure you don’t have a non-ML/AI approach that will work. I would say it differently – “Has the non-ML/AI approach already been shown to be sub-optimal (rather than “worthless)” – as we see a lot of clients where adding some ML/AI boost results without the need to replace the old approach completely.
…leaders who try to shove AI approaches where they don’t belong usually end up with solutions which are too costly to maintain in production. … If you can do it without AI, so much the better. ML/AI is for those situations where the other approaches don’t get you the performance you need.
Yup. Nothing to add.
The right first step is to focus on outputs and objectives. Imagine that this ML/AI system is already operating perfectly. Ask yourself what you would like it to produce when it does the next task. Don’t worry how it does it. Imagine that it works already and it is solving some need your business has.
We have a game we play to do this. We call it the “if only” game. We ask business owners to fill in the blank in the following sentence “if only we knew BLANK we would decide differently”. This let’s them imagine that some ML/AI algorithm can magically produce the insight they need. Exactly as Cassie suggests, focusing on your objectives and what outputs from ML/AI would help.
Moving on to Step 1: Define your objectives she says
Clearly express what success means for your project. … How promising does it need to be in order to be worth productionizing? What’s the minimum acceptable performance for it to be worth launching?
Pro tip:make sure this part is done by whoever knows the business best and has the sharpest decision-making skills, not the best equation nerdery. Skipping this step or doing it out of sequence is the leading cause of data science project failure. Don’t. Even. Think. About. Skipping. It.
Critical to this step for us is building a decision model. With a model of the decision making – the current approach for sure and perhaps also a model of your intentions – it is much easier to identify specific ML/AI opportunities and to define how promising it has to be – how predictive – and to capture the minimum acceptable performance. We have had some real-world problems were very low levels of accuracy were good enough (“better than 50/50”) and others where it had to be pretty accurate (“if it’s not better than 95% accurate we won’t use it at all”). Don’t guess, build a model and know.
And how do we build these decision models? Well we ask the person who “knows the business best” how they decide. And, as Cassie says, not doing this is the leading cause of failures so don’t skip it! I wrote this post on analytic failures – some are acceptable, even inevitable given the nature of analytics. But many are avoidable just as Cassie says.
The Decision Management team at Gartner (Rob Dunie , Roy Schulte, Derek Miers , Pieter den Hamer , Paul Vincent , Marc Kerremans and Erick Brethenoux ) have recently published a new paper entitled “Should Your Project Use a Decision Management Suite?” [Gartner subscription required].
It’s a great paper and I strongly recommend it if you have access. The quotes below are from the public page with a few additional notes from me. If this is a topic that interests you, check out the Decision Management Systems Platform Technology Report.
Decision management suites are very useful for implementing software that makes business decisions, although other products are sometimes better — depending on your business requirements. Data and analytics leaders should consider the issues highlighted when selecting tools for decision making.
Key here is the focus on data and analytics leaders – Decision Management and Digital Decisioning are essential components in an analytics strategy.
Use Decision Management Suites to Automate or Augment Operational Decisions
The focus on operational decisions – decisions about a single transaction – are central. There’s a whole set of use cases in the Report.
Use ML, Stream Analytics, Optimization or Other AI Techniques for Decisions That Require Runtime Analytics
I would note here, as I do in my new book on Digital Decisioning, that these advanced analytic techniques are best injected into real-time or runtime environments using decision management tools and techniques.
Decision Management is a proven approach for delivering Digital Decisioning and injecting machine learning and other AI techniques into your operational systems. Check out the new book for more details and a set of best practices and advice on how to get Decision Management done.
Digital Decisioning: Using Decision Management to Deliver Business Value from AI
There is an artificial intelligence (AI) revolution underway in enterprises across the globe as companies continue to adopt predictive analytics, machine learning and other AI across their businesses. This revolution puts managers and executives under enormous pressure to use AI to run their businesses more effectively. How do they do this? Will people lose their jobs? Most importantly, how will they become beneficiaries rather than victims of this new AI economy?
The speed, volume and complexity of decisions – as well as the impact they have on customer experience – demand automated, real-time decision making. Digital decisioning is an emerging best practice for delivering business impact from AI, machine learning, and analytics. Digital decisioning is an approach that ensures your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every customer touchpoint.
Next Session November 18-20, 2019 Three 2-hour sessions with homework. 12:30 pm – 2:30 pm ET
Decision modeling with the Decision Model and Notation (DMN) standard is fast becoming the definitive approach for sustained business engagement, improved traceability and flexible and analytic enterprise applications.
With decision modeling, you can:
Re-use, evolve, and manage business rules.
Effectively frame the requirements for analytic projects.
This 3-part online live training class taught by leading expert James Taylor, CEO of Decision Management Solutions, will prepare you to be immediately effective in a modern, collaborative, and standards-based approach to decision modeling.
You will learn how to identify and prioritize the decisions that drive your success, see how to analyze and model these decisions, and understand the role these decisions play in delivering more powerful information systems.
Each step is supported by interactive decision modeling work sessions focused on problems that reinforce key points. All the decision modeling and notation in the class is based on the DMN standard, future-proofing your investment in decision modeling. Decision modeling is also a Technique in V3 of the BABOK® Guide by the International Institute of Business Analysis (IIBA).
McKinsey recently reported that “Most carriers are struggling to meet their cost of capital, and productivity has barely moved over the past decade” and that “The insurance industry is facing a serious structural challenge … the majority of carriers are not making their cost of capital.” Growing productivity by improving operations is an essential ingredient in insurance carriers’ business strategy. Digital technologies in insurance have been focused on digitizing data and processes. They have made little impact on productivity because insurance is a decision-centric industry. Without digital decisions, productivity will remain flat.
Watch the recording of this webinar with me and insurance industry expert Craig Bedell as we discuss the importance of digital decisioning to improving insurance productivity. Learn digital decisioning integrates your existing technology investments with machine learning and AI to drive increased productivity in everything from underwriting to claims handling, and pricing to next best offer.
In January of this
year, Wired Magazine published an article about a
collaboration between The Department of Veterans Affairs (VA) and Google parent Alphabet’s DeepMind unit to
create software powered by artificial intelligence that attempts to predict
which patients in the intensive care unit (ICU) are likely to develop acute
kidney injury (AKI). The article stated that more than 50% of adults admitted
to an ICU end up getting AKI, which is life-threatening.
According to the
article, the Department of Veterans Affairs contributed 700,000 medical records
to the project. The goal of the project was to test whether artificial
intelligence could be developed to help doctors better predict which patients
were at risk for developing AKI so preventative measures could be taken sooner.
Fast forward to the August Volume of the journal Nature and the article “A clinically applicable approach to continuous prediction
of future acute kidney injury”.
It looks like artificial intelligence may in fact be able to help doctors
identify which patients in the ICU are at serious risk of AKI. This study shows
that artificial intelligence can predict kidney failure up to two days before
it occurs. During the research study, the software was able to predict nearly
56% of all serious kidney problems and approximately 90% of those problems
serious enough to require dialysis.
The work is
still in the early stages — there were two false positives for every true
positive — but it certainly advances what’s known about how deep learning may
be helpful in clinical healthcare practice.
According to Dr. Dominic King, DeepMind’s
health lead and coauthor of the research paper, kidney issues are particularly
tricky to identify in advance. Today, doctors and nurses are alerted to acute
kidney injury via a patient’s blood test, but by the time that information
comes through, the organ may already be damaged; making him hopeful for the
long-term value of these types of predictive solutions. The team hopes a
similar model can be developed to identify other major causes of disease and
deterioration, including life-threatening infection sepsis.
I live in Palo Alto – within walking distance of Google and the home of the VA hospital that an article in the Financial Times identified as the planned location for a clinical trial of this algorithm. I also have a particular interest in how to develop effective clinical decisioning systems. At Decision Management Solutions we have done a couple of interesting prototypes and recently written a paper for the Department of Health and Human Services on this topic.
The value of a prediction like this is that
it could help medical professionals better triage patients and get those
who require intervention on a treatment plan right away. Doing so could
potentially save hundreds of thousands of lives each year and lessen the need
for invasive, uncomfortable treatments such as dialysis or kidney transplant.
The prediction itself cannot do this – it’s just a prediction – but acting on
the prediction can. Making the prediction readily available to medical
professionals MIGHT change their behavior. If they understand the prediction,
if the prediction is clearly explained, if there is nothing about the patient
that triggers their own personal experience, if their first few cases aren’t
false positives… if, if, if.
To take advantage of this kind of prediction, it needs to be
embedded into a clinical decision support framework. Working with clinicians,
you can develop a model of how they do triage for patients today and how they
select appropriate treatments for a patient. This model will be different in
each clinical setting – the VA is likely to do this differently from your local
hospital network, for instance. The availability of facilities, distance to
them, organization of specialties and much more go into this decision. And if
the decision about triage requires medical judgment that can’t be automated,
this too can be input to the system.
With a clear understanding of the decision, you can improve it
using the prediction. The medical professionals can see how they would change
the decision given the prediction, its accuracy and its false positives.
Instead of simply showing them the prediction and hoping they will change their
decision, the system can change its recommendation in alignment with their
approach.
Remember, AI, Machine Learning and predictive models don’t DO
anything – they just make predictions. If you want to save lives (or engage
customers, prevent fraud, manage risk or anything else), then you have to make decisions
with those predictions.
