Cause-alities

Big Data, Analytics, And Storytelling

“I think storytelling is becoming one of the new frontiers,” said Luke Lonergan, co-founder of Greenplum, now part of EMC Corp. But beyond that, “it really matters a lot to bring the brain to the problem in a way that you can untangle the complexities.”  "Social Media, Genomics Driving Data Tsunami" Wall Street Journal 18 Feb 2011 http://on.wsj.com/g9Lt5A

I've found many times that it is very difficult for audiences to use and consume analysis -- no matter how insightful it might be.  I suspect this is why effective analysts always find and present "the story" that the data tells. Audiences, especially many decision makers, simply glaze over when presented with the details of a complex analysis.  But presented as a story they can interact, explore, test, and consume the analysis.   Used correctly, storytelling can be the common language for both consumers of analytics and the those that truly revel in the abstractions of the analysis process.

There is a measure of irony in Lonergan's comment about storytelling being a "new frontier" since it has to be one of the most ancient and powerful modes of human thinking and communication.  I'm guessing he means storytelling as a means to facilitate the application of big data (I don't know Lonergan, although I'd like to, so all I can do is guess) and that would be a new, but not unprecedented, application for storytelling.

I think that storytelling is more than a communication mechanism -- something that we think about after the analysis is complete.  Storytelling can provide an analytic framework.  As I read the interesting WSJ blog post and got to Lonergan's quote at the end I was prompted to describe some of my thinking about the relationship of storytelling and analytics and explore some ideas about how it might be relevant to the promise of "Big Data".

Outcome Maps

There are a lot of reasons to create system simulation models.  Many efforts start by simply wanting to understand what is causing some situation to develop; or, just the desire to understand how things work.  In these cases a simulation model becomes a rich and transparent cause-and-effect hypothesis.   Now, let me observe that having a solid understanding of how your business (or whatever you are exploring) works, its driving structure, and the baseline values of its parameters is a basic and broadly useful result in and of itself.  One that is surprisingly rare. 

However, in this "what have you done for me lately" world, inevitably, the "so what?" question comes up.  As in, "So you have a simulation model . . . so what?" Because, as soon as a basis for system understanding has been established, we want to improve, control, change, the system.  We want to make insightful resource allocation decisions.  So, as I've discussed many times in this blog, it's usually not enough to build a system model simply to know how things work -- we need to think about how to harness it to do useful work.

This is trickier than it might first appear. Commonly, the initial approach runs along the classical scientific reductionist line: "Now that we have a model that predicts the future we simply act in accordance with that insight."  This is so common it has a name: The predict-and-act decision framework.

In a very real sense, however, system models don't predict the future.  They describe the cause-and-effect physics that connect our actions with assumptions about the future that lie outside our control.  They describe the rules that allow us to "shape" but not dictate the future.

As the saying goes, this is not a bug, it's a feature.  Because, shocking as it might appear, good decision making does not require that we predict the future.  Good decision making requires that we understand the implications of our actions. System modeling is a practical way to differentiate the implications of our decisions from uncertain factors that are out of the sphere of our influence.  And by doing so we gain deep insight into both.

Working with my clients I've created a visualization that helps them put their system model to use in a decision making environment.  I call it an "Outcome Map". I've drawn heavily on work from "Real Options" and "Robust Decision Making" and married it to system simulation.  Take a look at this Prezi to learn more. 

Some Prezi Hints: After you fire up the Prezi, use the "more" menu to switch to full screen mode.  Advance the presentation using the "next" arrow at the bottom. After seeing the presentation explore the canvas using the pan (left click and drag) and zoom (scroll wheel).

Why Model?

I recently ran onto a short paper (speakers notes, really) by Joshua M. Epstein titled "Why Model?"  I spend a lot of my life answering that question and I am excited by Epstein's concise, reasoned explanation.  He boils it right down to the basics:

1. We're all modelers, but most of our models are implicit, not explicit.
2. Sometimes we model to predict.
3. Sometimes we model to explain.
4. And there are at least 15 other good reasons to build explicit models . . .

In some sense Epstein's position on modeling is a presentation of the scientific worldview and its moral advantages.  So, mixed in with some really concrete reasons (eg: #2 -- Guide data collection) are some seemingly more esoteric objectives (eg: #6 -- Promote a scientific habit of mind).  

Alas, business, financial, and other organizational leaders are mostly not swayed by a "scientific approach".  I find that business and organizational clients generally need an additional level of motivation to justify an investment in explicit modeling.  Usually, for the business person it is not enough to believe that an investment in explicit modeling will accomplish any of Epstein's 16 reasons.  The business person wants to know What Then?  Often phrased as a somewhat derisive "So What?"

For most business leaders explicit modeling has to be linked to some decision making or problem solving process.  And, unfortunately, this often boils down to a focus on prediction at the expense of the other 16 reasons that are also part of excellent decision making and problem solving.

If you like this paper by Epstein try his book Generative Social Science: Studies in Agent-Based Computational Modeling.  It's one of my favorites.

Making Sense with Systems Science

When I named Decisio (almost 10 years ago now!) I was casting about for a tag line that extended the "decision motif" to capture the essence of what we do.  I settled on "Making Sense of the Future."  My idea was (and is) that if clients are going to be able to make good decisions in complicated situations then they first had to understand that situation -- they had to "make sense" of what was happening.  Then, they could use that understanding to make good decisions.  The invocation of the "future" in this was intended, firstly, to suggest that comprehending the role of time is important to understand problems.  Secondly, that we make decisions today in order to reap rewards in the future.

This idea of using systems modeling to "make sense" and support decision making was not and still isn't very common.  There seem to be two prevailing ideas about the role of models and modeling.  One common view is that they are sophisticated black box tools that consume data and produce predictions of the future.  My observation is that while good models have predictive qualities the future is slippery. All models are wrong (but some are useful). Decision making based on a "forecast" mentality will not turn out well.  An alternative perspective is that, since forecasting is difficult or impossible, modeling should be used for individual and organizational learning.  Well, that's fine but sooner or later somebody has to make decisions!

I've recently become aware of the science and some of the research around the formal idea of "sensemaking."  Gary Klein, well known in the field, describes sensemaking as "a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively".  Well, that's exactly what I help clients accomplish using systems models.  In my projects the modeling activity guides an effective sensemaking process that results in high quality decisions.

Recently, I think I've been guilty of describing my work from the perspective of systems science and modeling to the detriment of the "making sense of the future" perspective.  In fact, successful projects always integrate modeling with the sensemaking perspective.

I think that the intersection of systems modeling and sensemaking is not as well explored as it needs to be so I'll be blogging more about it.   To read more about sensemaking in general try this wikipedia article and publications by Gary Klein and K. E. Weick.

Creating a Future

I recently spent some time speaking with the director of a well known consulting firm about the possibility of incorporating complexity science models into his practice.  I was struck by his choice of language as he described to me what he was trying to accomplish for his clients.

He said that he was trying to make the "cause-and-effect relationship between the clients decisions and the resulting outcome clear."  For example, he went on to say that sometimes his clients did not seem to realize that saving $2 here was going to cost $2000 somewhere else.  Or, alternatively, the client might have a correct intellectual understanding of a situation but due to momentum or other pressures found it impossible to make decisions consistent with the outcome they wanted.  He went on to say that this was symptomatic of a general difficulty in seeing business issues holistically.

I got really excited as I listened to his story.  Recently, I have been wondering if naming this blog "Cause-alities" was wise. Much writing about complexity science seems to emphasize the elusiveness of cause and effect relationships and seem to suggest that we cannot understand the consequences of our decisions.  Well, this is where the "attitude" part of my blog's tagline kicks in.

In matters of management I think that leaders need to invest in understanding as much of the cause and effect chain that drives their business as they can. They will find the real value of a "systems understanding" (to rename the cause-and-effect chain a little bit) in the realization that, to a large extent, the future they get is the one that they create through the decisions that they make. 

This is a vastly more powerful notion than the traditional "predict the future and I'll react" mentality.

What this boils down to is that in much of the business system the "cause-and-effect chain" is really a "cause-and-effect loop."  And our decisions are in fact part of that "cause-and-effect loop."

In practice, the only way to develop deep causal insight into business systems is through a complexity science simulation model.  As I've written before a spreadsheet, while sometimes useful, is inadequate to describe how a business works because business systems are feedback systems and spreadsheets simply don't have the expressive power to describe feedback. 

Finally, I have to acknowledge that not everything that happens in our business is something that we created through our actions.  The environment is important and external events are important.  Nevertheless, in most cases, the trajectory of the firm is a result of management leadership, not outside factors.

Extending "Predictive Analytics" Using Complexity Science Models -- Part 1

"Predictive Analytics" is the "next generation of data-mining" according to  Forrester Research. The promise of significant bottomline benefits is making it big business with most enterprise software vendors participating.  I believe that complexity sciences modeling has a generally unrecognized potential to play an important role in the development of this capability.

It's About Time

What's about time? Readers of this occasional blog are thinking "Yeah, its about time he posted something!" True, but not the answer I'm thinking of:)

The practice of management is about time. It is about making a decision in the present that will affect results at some time in the future. Peter Drucker used a somewhat unusual word for this in his famous tome "Management: Tasks, Responsibilities, Practices." He wrote about the "futurity" of decisions. If you look up futurity you will mostly read about horse racing. However, the definition that I think Drucker meant is "state of the future." According to Drucker, to manage well is, in part at least, to have a good grasp of what the future state of the business system will be as a result of a decision taken today.