Category Archives: Decision intelligence

Senate Intelligence Briefing features AI and Decision Advantage

Today’s senate intelligence meeting is probably the most important two-hour video you should make time to watch this week, especially if you are interested in the intersection between AI, DI, and national security.  Also some important cautions about Internet of Things (IOT) security.

Some important quotes:

Admiral Mark Rogers: “Clearly I think we are not where need to be…the challenge I think is that we have [multiple areas of knowledge and insight within the federal government and within the private sector, how  do we bring this together and create and integrated team, with some real-time flow back and forth.”

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Death by proxy

“This year, our priority is customer experience.  Everything we do must connect to that.”

“We’ll upgrade the network in a neighborhood when the bandwidth utilization exceeds 80%.”

“I’m going to get rich, then I’ll be happy.”

Good ideas, on the surface.  Problem is: they’re often wrong.

And they share a common thought pattern: the use of a proxy—a substitute—for what we actually care about.

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Responsibility, authority, and insanity

So this is pretty basic.  But it’s huge.

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.

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Pulling Back the Curtain on #MachineLearning Apps in #Business

Please enjoy this interview broadcast today with me and Daniel G. Faggella of TechEmergence.  I touch on intelligence augmentation (IA), machine learning in vision, text, and other domains, the emerging decision intelligence ecosystem, the limits of data, and how to hire a machine learning consultant.

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Artificial intelligence and human limits

Are we getting dumber?  Or is stuff just harder?

Both are true.  Between-silo problems are the new bottleneck.  We’re inundated with information, so we take cognitive short cuts.   And “wicked” problems keep getting wickeder.

Take this “invisible art” artist.  She sold a few.*

Real decisions are made in the heart, the gut, based on a good story.  So we’re vulnerable to master wizards: good story tellers.  And, often, we’ll do what they say.

It’s impossible to assemble hundreds of graphs and data visualizations in our heads to make good decisions. It’s a fiction that we can.  So we’re overwhelmed, take short-cuts, but it’s hard to admit.

The good news: we have new superpowers.

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The World Resources Sim Center: on its way to Silicon Valley

Last month I received an intriguing email inviting me to an event at Kimberly Wiefling’s house.  I’d met Kimberly before through Jonathan Trent, as part of the work I’ve been doing to help out the Omega Global Initiative.  I knew she was an international consultant, but it was great to also learn that she was passionate about systems thinking and visualization.  Jonathan and I drove up to Kimberly’s house together, where she and  Peter Meisen explained their initiative to bring a Buckminster Fuller-inspired Sim Center, based on a similar center in San Diego, to Silicon Valley.

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Decision Intelligence solutions

Decision engineering Decision intelligence Decison model elements Dependencies Externals For Decision Professionals Introduction Levers Outcomes

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…

Most organizations look backwards, assuming future equals past. This is no longer true. Click To Tweet

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:outcomes

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.

  1. Decision intelligence training. I run half-day to full-week workshops on the core concepts behind decision intelligence.  Here’s a typical course outline.
  2. Company-wide presentation, so everyone knows what we’re up to.  Here are some videos of my public appearances, to give you a flavor of this.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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:

  • Additional training:
    • 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
  • Data services
    • Data modeling
    • Enterprise architecture integration
    • Data cleansing
    • 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.

Drop me a line and let's talk!

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Guest post: Ignite people to change by leveraging a simple quirk of the human mind

Before Martin Luther King had a dream, E.D. Nixon had a plan. It was a good plan, too.  Nixon thought he could make huge strides in the struggle for racial equality in his hometown of Montgomery, Alabama by orchestrating a boycott of the city bus system–both a bastion of segregation and a huge money-maker for the city.

He had a sound strategy and a thorough plan.  But it was proving difficult to get the masses of African-American bus riders to go along with the change plan, and actually stop riding the buses.  Just like countless team leaders and project managers in today’s organizations, Nixon had all the dominoes lined up, but he couldn’t get them to start falling.

Then one morning in the early spring of 1955, a courageous young woman took a seat on a city bus. After the bus filled up, the driver ordered her to move so that a white woman could take her seat. When she refused, the irritated bus driver then flagged down two police officers who grabbed the young lady and hauled her off to jail.  And the rest of the story is history.

Today, everyone knows that Rosa Parks’ decision to stay seated on that bus inspired a domino effect of change that tore down the walls of racial segregation in America.There is just one problem with that story.

Rosa Parks wasn’t there.

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