Problem: You are working at a bank and have been tasked with developing a new customer care program. You’re making good progress, and reach out one day to a colleague for their ideas. Word of the meeting gets around, and an executive walks into your office one day, assuming you’re floundering, and tells you what to do.
Problem: Your second grader is in trouble at school. Teachers and other parents call you on the phone, asking you to fix the problem. You are starting to develop some good ideas and making plans. But one day, the school principal makes the decision to move your child to a special classroom.
Problem: You work on an automobile assembly line. Your bonus depends on the quality of the cars you help to build. You see a problem with a welding machine which would cost $100 to fix. But management won’t approve the repair.
What’s the common pattern here? It’s responsibility without authority: a good recipe for insanity.
When asked “who created Apple?”, it’s tempting to say Steve Jobs did it. The truth is that, although he may have been necessarily, he was not sufficient.
Similarly Bill Gates, who (as Malcolm Gladwell tells us in Outliers) experienced a unique confluence of circumstances that led to the founding of Microsoft. Gates deserves tremendous credit, but alone he was not sufficient.
The brain likes to simplify, and history sometimes prefers to leave out the details for the benefit of a better story. Continue reading
Much of the world’s information is handled in data centers: nondescript on the outside, indoors you’ll find a din of humming machine in acres of racks. If an earthquake or other natural disaster should strike a data center, the cost can be enormous.
So spending money on risk mitigation strategies like earthquake-proofing makes sense.
But it’s a tricky balancing act: spend too much for a disaster that never happens, and it’s money down the drain. Spend too little, and a disaster can spell tremendous costs.
A decision model can help. We built a demonstration of one shown in the video above, at the link here, and in the bottom of this article, for those viewing this online.
My team and I offer decision intelligence and decision engineering to solve your complex problems. Simply put, data is backwards-facing. It’s like watching an elephant’s footprints, rather than understanding the elephant itself. Systems are forward-looking. There’s no coincidence in the fact that your rear-view mirror is smaller than your windshield. Yet…
So let’s roll up our sleeves together, and take your organization to the next level.
It doesn’t have to be hard.
You still need your data. It’s always going to be an important tool in validating where you’ve been, and of course, operational data (like a customer’s address) is critical.
But when it comes to making decisions, data is only part of the picture. Once you make a decision and execute on it, this creates a cascade of events within your organization. Understanding how the decision leads to an impact…and another…and another…until you’ve reached your objective (or not) may sound like a daunting proposition. But here’s the rub: you’re doing it already, in your head, every time you think through the consequences of a decision. Why not get some computer help with that? Even a little can go a long way. Because otherwise, you’re facing the fog shown below:
Here’s what you can expect when working with me to improve your organizational decision making. Think of this as an “a la carte” menu; some organizations choose the whole bundle, some do one part at a time. Each has considerable value on its own.
Decision-specific workshop. I typically work with a multi-functional team. We begin by aligning around desired business objectives, then proceed through decision specification, design (levers, links, external data, and more), and iterate. We work visually and collaboratively, and often the focus is understanding the links between previously silo’d groups, and modeling the “whack-a-mole” of unintended consequences.
Data assessment. Usually, only 20% of the data contains 80% of the business value. The challenge is to determine which data contains the value. Decision intelligence provides a structured and rigorous approach to figuring this out. If you don’t take this approach, you can waste a lot of time managing data using “operational” methods, where less expensive “analytical” approaches are a tremendously better fit.
Decision model implementation in World Modeler. You can get a lot out of steps 1-3, but implementing these concepts in an enterprise-class tool is where the big benefits start to kick in. Sometimes we begin by modeling in Microsoft Excel as well, and often we generate data that can be read by Excel as the model runs.
Decision visualization. We usually build custom visualizations for our clients. You can see a few demos, here (for program management) , here (for data center risk assessment), and here (a full-CG decision model visualization). We build these to your specifications, to meet the needs of your team.
Data and systems integration. Here, we work with your organization to integrate a real-time decision navigation infrastructure into your business. Our value proposition: “the best of both worlds”: using our World Modeler platform means that we can built solutions at low risk and lower cost and much more quickly than other companies (indeed, we once built a full bank risk analysis system in under six weeks). At the same time, it’s fully customized to your needs, as if we’d built it from scratch. Why this is possible? We’ve discovered what’s required in a general-purpose data and decision management framework, including optimization, data binding, visualization, simulation, and more (read our API documentation here).
Add-ons to the above include:
Introduction to R
Introduction to neural networks in R
Introduction to machine learning in R
Building World Modeler custom visualizations
Deep-dive decision dependency design
Enterprise architecture integration
Web-based interactive data survey / gathering creation
Custom model visualization development
..and much more
So please drop me a line, and let’s explore together whether supercharging your organization’s decision intelligence makes sense for you.
Inattention to critical knowledge is an old problem. Lessons are forgotten, near misses are ignored, caution is dismissed, disasters result. Titanic. Bhopal. AIG. Katrina. Fukushima. And on and on.
Knowledge Management (KM) is supposedto make the right information available to the right people at the right time in the right form—and to the best level of certainty possible—for making the most appropriate decisions when and where they are needed. KM should also direct the attention of decision makers to critical information and help them make sense of it. The bigger the stakes, the more situational awareness and mindfulness are needed. Continue reading
There’s a scene near the beginning of the Oscar-winning Shakespeare in Love:
The theatres, we have heard, are all closed by the plague. And then:
HENSLOWE: Mr. Fennyman, let me explain about the theatre business.The natural condition is one of insurmountable obstacles on the road to imminent disaster. Believe me, to be closed by the plague is a bagatelle in the ups and downs of owning a theatre.
FENNYMAN: So what do we do?
HENSLOWE Nothing. Strangely enough , it all turns out well.
HENSLOWE I don’t know. It’s a mystery
As computer scientists, one of our first lessons is about “big O” complexity of software. It’s used to understand the expected time for a program to run. “Big O” theory tells us that it’s the order of magnitude of a system—like O(n^2) or O(n)— that matters, much more than smaller factors. To understand the parts of a computer program that dominate the time required to run it, we know not to focus on the tiny parts of the system that irrelevant to the overall behavior. Measuring the performance of a line of code to the fourth decimal place, when it only runs a ten-thousandth of the time that other code does, is wasted time.
As introduced in Beyond Data Part 1, we need to apply the same kind of insight to analyzing data, whether it be for business intelligence, decision support, dashboards, or other systems.
Think about your company as a snowball rolling downhill. If you start five balls down a slope, they’ll all roll a little while, but some will happen to hit stickier snow, and get a little bit bigger than the others. Those heavier snowballs will have just a bit more momentum, so they’ll roll a bit further, and use up all the snow, gaining a bit more size in the process. Which makes them heavier, which makes them roll faster. You get the idea: in general the fortunate few snowballs will end up much bigger, leaving the smaller ones stuck at the top of the hill and others much bigger and much faster. Continue reading