Category Archives: Decision engineering

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: Can technology help fix us?

No matter whether you pull for Donald or Bernie or are an “occupier” or a “tea partier” or anywhere in between, you have to admit that we have a problem. The system is broken, due to gerrymandering, big money, or any other possible reason that you can name. We have issues, such as “income inequality,” or “climate change” that are of major concern to one of our major parties while the other is somewhat unconcerned to the extent that some within the party don’t think that the problems exists; we have one party claiming credit for what it sees as tremendous success of its health care bill while the other party constantly tries to repeal or dismantle it.

Gone are the days of the bipartisanship that brought us the Civil Rights and Voting Rights bills (even in the face of southern segregationists) and Environmental legislation. We have a booming economy in terms of Wall Street and corporate profits and a stagnant one in terms of worker salaries and buying power. Continue reading

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Top-ten decision lever best practices

“We need to win more work,” says the CEO.  “Can you think through how we could lower our prices to become more attractive to our customers?”

A good decision engineer can’t help but ask “the lever question” at this point: “Ma’am, are we only to consider pricing, or would you be open to other approaches to winning more work?”  Confirming the scope of levers allowed like this is the most fundamental decision lever best practice, but there are many more.

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Decision: I do not think it means what you think it means

We use the word “decision” to mean two very different things.  If I say “I’ve decided that the moon is made of green cheese”, or “I’ve decided that the economy will deteriorate next year”, these statements aren’t necessarily about actions I’m going to take.  If, instead, I say, “I’ve decided to go to go to graduate school” or “I’ve decided to institute a new policy”, that’s fundamentally different.

How?  The first kind of decision leads to a fact, either well-supported or not.  It is, essentially, using data and expertise, following its implications (deductively, inductively, or otherwise), and leading to a conclusion (which may have more or less justification: to fit this category it doesn’t have to be right). Continue reading

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The Decision Intelligence and Decision Engineering Ecosystem

It’s been an incredible last few weeks in the Decision Intelligence world, as we’re seeing an ecosystem emerging with new vendors, articles in IEEE, the New York Times, at HBR, and much more. I’ve taken a first shot, in the graphic below, of mapping the ecosystem. It’s not at all complete, so please send me entries for new nodes. I’ll also be tweaking the graphic going forward to make it easier to navigate. Enjoy! Continue reading

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About Levers

I’m going to write a series of posts about the core elements of a decision model.  This one’s about Levers: simulations of things you can change as you make the decision.   We might also have called levers choices.

It can be confusing: you’d think that a decision model would produce the choices as output, not as input.  Because it’s supposed to tell us what decision to make, right?  But things are a little backwards: the right decision is the one for which the levers will set in motion a chain of events, that in the future will lead to a desired outcome.  So from this point of view, the action of a lever belongs at the beginning of a decision model, not the end. Continue reading

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Mark Zangari speaks on Agency and Machine Learning: from data to actions

Once upon a time there were programmers, but not software engineers.  As businesses and other organizations learned the value of this new technology, software engineering emerged as a discipline to derive maximum business value from it.

There is a similar need emerging in data science today.  This means that machine learning is underutilized compared to its potential in solving business problems. So the question is, how to bridge from the business to data and machine learning to drive maximum value?

Quantellia co-founder Mark Zangari presented a talk on this topic, called “Agency”, in Seattle on May 1 at MLConf 2015.

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Decision models are the requirements language for DI apps

The software engineering revolution is happening again.

I was a coder before software engineering, and it wasn’t pretty.  When we needed to build a new program, we’d get together with the end customer, and ask a lot of questions, then go back to the office to write code.  It didn’t go very well.  It was like construction without blueprints, manufacturing without CAD. Continue reading

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Guest Post: What is Decision Intelligence (DI), anyway?

Decision Intelligence: An easy and pedagogical way to make Informed Decisions using collective intelligence

Have you sometimes had the feeling that you missed important aspects in your decision making which make you feel somewhat uneasy?

Did you perhaps forget to take certain facts into consideration or did you misjudge the relative importance of an influencing factor? Did you realize the unintended consequences of the decision taken?

You know there is a tacit cause-and-effect mechanism under the surface, but maybe you did not capture it, or even not understood it.

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