Working with companies that are investing in becoming analytic enterprises, we have determined that there are three critical success factors. Whether you are focused on business analytics, data mining, predictive analytics, machine learning, artificial intelligence, or all of the above, these factors will be critical. Check out these videos that talk about them:
Analytic Enterprises Put Business Decisions First The first critical success factor for analytic enterprises is keeping the focus on business results by beginning (and ending) with business decisions, not analytic technology. https://youtu.be/DQ9GHSOxd9s
Analytic Enterprises Predict, Prescribe, and Decide The second critical success factor for becoming an analytic enterprise is moving beyond reporting and analysis of the past to prediction and action by using more advanced analytics to predict, prescribe, and decide. https://youtu.be/2128C4p8wVM
Analytic Enterprises Learn, Adapt and Improve The third critical success factor for becoming an analytic enterprise is recognizing that applying analytics is not a one-time exercise, and focusing on how to use analytics to learn, adapt, and continuously improve. https://youtu.be/rlnNtk9bSyc
And if you enjoy the videos, check out our white paper on building an Analytic Enterprise.
Gartner recently published a piece “Top 10 Trends in Data and Analytics, 2020” that you can currently get from our friends at ThoughtSpot (registration required). It’s an interesting report you should definitely check out.
My favorite section was the one on Decision Intelligence, within which they include the kind of digital decisioning or decision management I’ve been doing for the last couple of decades (and in which the firm I founded, Decision Management Solutions, specializes).
In this section they correctly point out that, while automating decisions is a critical component of digital decisioning, it’s not necessary to automate 100% of the decision 100% of the time. Often we find that sometimes an automated decision requires some human inputs or that only a certain percentage of transactions can realistically be handled by a decision engine. We build decision models to understand the problem well enough to make these calls – to decide on the automation boundary – and it was great to see the shout out for decision modeling (and the Decision Model and Notation standard) in the report. The team at Gartner linked decision modeling to improved agility (faster changes), transparency and business user enablement – all key benefits we see in client after client.Personally I always get the biggest satisfaction from seeing how digital decisioning enables continuous improvement, generating the data you need to review, improve, simulate and compare decision-making approaches. As the report says, the key is to pass actionable insights directly to decision engines to act and then enable humans to review the effectiveness of this and close the loop. Putting business owners in the driver seat for improving their own automated decisioning systems is a powerful tool that generates a huge ROI.
There were also some good pieces of advice on how to scale your Machine Learning (ML) and Artificial Intelligence (AI) efforts. I would add to their advice that scaling ML/AI in a fast changing world requires more than just adopting the right ML/AI techniques. It needs the active engagement of business domain knowledge through decision modeling and business rules too. No matter what you do to improve your AI/ML, there’s no substitute for combining it with in-house business knowledge. I also appreciated their comment that the approaches that got you to an AI pilot won’t get you to production – you need an approach like the one Cassie Kozyrkov discussed and I comment on in Some great advice on Machine Learning from Google (and me) or the ideas in this post on Most companies are not succeeding with advanced analytics. But you can.
Anyway, it’s well worth registering for and downloading. If you want to learn more about decision modeling, check out our great white paper on Decision Modeling with DMN and if you want an overview of our approach to machine learning, check out this paper on Enabling the Predictive Enterprise.
The work from home movement, voluntary or mandatory quarantining, retail store closures, and limits on public gatherings all serve to significantly increase our dependence on digital capabilities.
He goes on to talk specifically about insurance
Digital interaction capabilities: Self-service portals for agents and policyholders, websites that are easy to navigate and built using responsive design approaches, mobile apps for policy service and claims, and world-class call center technologies will become more critical than ever. Volumes are likely to increase as fewer face-to-face interactions occur by necessity.
We do a lot of work with insurers and they have historically been challenged when it comes to digitization:
Self-service portals only allow customers to do limited things – generally simple data updates or document review.
Agent portals are likewise often very passive, presenting data to agents but not really helping them manage their business.
Mobile apps focus on reporting the status of a policy service update or a claim when the customer just wants the update made or the claim paid.
Call centers increasingly have access to all your data but must constantly refer you to others for approvals.
The problem is that insurers have added digital channels, lightly digitized their data (scanned documents) and automated (digitized) processes without reviewing how they make decisions. They have paved the cowpath. And their use of Robotic Process Automation tools increases the odds that they will continue to do so. This is going to have to change. They are going to have to digitize decisions.
If policy update approvals are automated, customers can use the portal to do things not just request them.
If agency management decisions are automate, the agent portal can suggest how to grow the business and improve customer service not just passively support an agent who might not know what to do next.
If claims handling decisions are automated, mobile apps can support submission of claims and then respond immediately with “this claim is approved and will be paid in XX amount”.
If underwriting decisions are automated, websites and mobile apps can issue binding quotes and kick off the onboarding process 24×7.
If call approvals are thought of as the decisions they are not processes then call center reps can immediately assist customers, not just promise to talk to their supervisor.
COVID-19 is driving digital transformation. Insurance is a decision-centric industry and only if decisions are also automated can it be transformed.
A survey in CIO magazine on IT leaders’ thinking in the current crisis revealed that a plurality (37%) chose digital transformation as their first priority to help the business persevere through the current disruption. Moreover, a full 61% of respondents agreed with the statement that the effects of the pandemic are actually accelerating digital transformation efforts.
We certainly see this in some of our customers. The movement of staff to working from home, the need for clients to interact remotely, restrictions on travel and meetings – all these are increasing the value of digital channels while also providing concrete motivation to get over hurdles previously seen as insurmountable.
What’s interesting, though, is the extent to which digital transformation driven by COVID-19 is decision-centric – how much it relies on digital decisions not just digital data, digital channels and digital processes.
COVID-19 means digitizing decisions about customer interactions so you can build and sustain profitable interactions with your customers. Decisions about how to personalize and target the content you display, the emails you send and the offers you make are essential. Companies are finding that they have neglected these digital decisions, investing in digital channels only to deliver cookie-cutter digital content when they could be engaging customers directly and precisely. Digital “micro” decisions need to be made for each customer, each time.
COVID-19 means that manual approvals, manual discount calculations, manual eligibility checks and manual pricing are all problematic. Many companies have found that their highly automated digital processes just route work to a person for critical decisions. With more remote workers, more workers having to flex their schedules to cope with home schooling and customers increasingly doing likewise, trying to coordinate human decision-makers for these transactional decisions is not getting it done. These decisions need to be digitized.
Finally COVID-19 means that the way people and resources are assigned needs to change. When everyone was in the office together, informal ways to assign work and manage resources worked OK. Now they’re a recipe for delay and confusion. Decisions about assignments and allocation need to move beyond first-in,first-out queues and informal group discussions to precise, data-driven and digitized decisions.
Digital transformation is happening faster thanks to COVID-19 but the real opportunity is for companies to digitize their customer and transaction decisions.
With everything else going on at the moment, you may be finding it difficult to think about improving your decision-making. Yet COVID-19 has added a new set of constraints on your decision making and changed you objectives. This makes the management, and optimization, of decisions even more valuable.
A few weeks back we published a new white paper – The Customer Journey to Decision Optimization. Sponsored by FICO, this is available on their website. This paper lays out steps to adopt 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:
We blogged previously about the need to react quickly and accurately to change and the importance of building and sustaining customer relationships remotely. One final area of focus is the need to be more flexible and dynamic in how resources are assigned and managed.
Companies are going to have more staff working remotely. Remote workers can’t share work as readily with their co-workers, or bounce assignments to others by simply standing up and waving across the room. A group of collocated workers could be assigned work pretty mindlessly and left to sort it out for themselves – simply queuing up the work for the group worked OK. This approach had problems before but now these problems become too serious to ignore. Organizations need to rethink how work is assigned and move away from first-in, first-out queues to more sophisticated allocation and assignment.
For instance, one call center we know supports remote workers and multiple locations by investing in its call routing approach. Instead of just dumping calls into a queue, the stated or predicted topic of the call as well as customer details and value are used to route it appropriately. To maximize the engagement and value of these calls, the organization also determines the best possible upsell or cross-sell to be made at the end of the call (including the possibility than not making one was the best option). Where possible, calls are routed not just to someone who can solve the problem the customer has but also to someone who’s good at selling the identified upsell. This focused assignment is highly dynamic and can be changed whenever new products or campaigns are launched or when the kinds of calls being received is impacted by outside events. Staff are given work they are good at, customers get agents who can help and who are enthusiastic about the products being up-sold, and results improve.
Even when organizations have built dynamic allocation and assignment approaches, there are often overrides. When call volumes get high, when the quarter-end is looming or when marketing campaigns are in flight, all the sophistication is thrown away as the override kicks in. This is never great – overriding things like this inflicts long term damage on data-driven improvement for instance – but it becomes very high risk when circumstances may often result in new overrides. Like now, when the constant changes to COVID-19 rules and regulations are causing companies to keep overriding their systems and process to cope.
The new environment is going to require much more managed overrides and more rapid change. One of our clients, for instance, has a routing algorithm that considers a wide range of factors before deciding how to handle a particular product return. Various situations exist that “override” the default algorithm but these are built into it. Business users can simply identify that a particular circumstance has occurred and the built-in changes to the algorithm are triggered. The business gets the appropriate response, users still rely on the system in exactly the same way and all the usual data gets collected. The “override” gets 100% compliance from users because its built in and the cost and impacts of the override are transparent because the system still runs and still creates the data needed for analysis.
All of this brings up one last point. Precise resource assignment increasingly involves algorithms – machine learning and predictive analytics. Which customers are churn risks, which products will be appealing, who’s likely to be able to handle this problem – all of these are candidates for advanced analytic models and machine learning algorithms. Focusing on the business problem – how to do the assignment or allocation – frames your need for these analytics so you can not just build the algorithm, you can also get it across the “last mile” and into production.
These systems are going to require new decisions to be made – dynamic, managed, automated assignments and allocations will replace first in, first out queues and simple distribution. Processes will change to add these decisions. Analytic insight will matter in these decisions, as will expertise and experience. Batch analysis will be replaced with real-time scoring and simple logic with managed business rules. Decision models will help you integrate these elements to deliver the real-time decisions you need.
Dynamic, managed assignment is something you can build into your systems now. We’ll be posting regularly on ideas and approaches and producing some great content. To stay up on it, why not sign up for our newsletter.
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.
