Re-Learning Strategy with Big Data

An archestra notebook.

© 2013 Malcolm Ryder / archestra

The goal of Strategy is not to win; the goal is to win in a certain wayThe gap between where we think we will need to be and where we think we will probably be is the starting point of strategy.

At that starting point, there are the current potentials (based on logical relationships), and there are the current expectations (based on plausible models).

When an accepted set of potentials are organized into an argument supporting an expectation, a strategy can result as a prescription based on that argument.

The prescription directs positioning and action-types that, combined with each other, are expected to allow or cause desired outcomes.

Expectations versus Potentials• The expectations are based on the implications of

current interpretations (findings). • These implications are dominated by probability

assessments, which are themselves based on inspections.

• Expectations are often made “usable” by techniques that arrange implications into concepts.

• In other words, models are created as a way to express the significance of interpretations. Models make the expectations communicable, portable, and open to validation.

• A natural host for this effort is the Scientific Method. The interesting question is: what happens when the findings are refreshed (altered)?

• The potentials are based on the facts held within current knowledge (histories and beliefs).

• That knowledge is dominated by perspectives, which are themselves based on assumptions.

• Potentials are often made “explicit” by techniques that arrange facts into relationships.

• In other words, patterns are described as a way to express the significance of facts. Patterns make the potentials discoverable, recognizable, and open to comparison.

• A natural host for this effort is Design. The interesting question is: what happens when the knowledge is refreshed (altered)?

Expectations versus PotentialsModeling findings as usable expectations • The models are based on the implications of

current interpretations. • Expectations reflect Probability assessments that

are made based on scientific methods for using interpretation to arrange implications into models.

Patterning assertions as explicit potentials• The patterns are based on the facts held in

current knowledge. • Potentials reflect Perspectives that are made

based on design techniques for using assumptions to arrange facts into patterns.

Patterns submitted as information

Informationinterpreted for

implications

Implicationsarranged as

models

Assertionsassumed as

facts

Factsorganized as

patterns

Feedback from applying

models

Assessed probabilities

testing

revisions

testing

Designed perspectives

rejected

selected

inspection

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Arranging potentials to argue for expectations

In the end, models provide an explanation of why certain combinations of patterns are a useful representation of the experience of reality.

The technique for creating the representation can be adopted as a way to “simulate” or “predict” speculative alternatives that are either concurrent or not yet present.

The probability of a given reality (a set of conditions) and a given experience (a set of effects) becomes the focus of ongoing investigation. Both patterns and models may be used to investigate either aspect

To do the investigation:

• The idea of “reality” requires detailing, in the form of values (importance) associated with the probability of effects. (Forexample, phenomena that are both low value and low probability are often simply ignored and omitted from the investigation. Selectivity is fundamental to the picture obtained.)

• The idea of “experience” then also requires detailing, in the form of presumed stimuli (instigators) and dynamics (persistent structures of interaction accompanying persistent structures of co-incidence).

Ongoing collection and analysis of additional (fresh) observations can feed new facts and interpretations into the mix, leading to changes in the acknowledged versions of experience and reality. Automation increases the speed, scope, range and volumes of observations – by analogy moving awareness itself from the affect of still diagrams to that of streaming video.

Going Big with Strategic DataCycle times of processing the typical events for generating new scenarios are accelerated by many orders of magnitude when using the tools now affordably available for data and information analysis.

Because vast amounts of source material are now consumable by interpretive methods in a short time frame, the idea of deriving the supporting argument of a strategic prescription takes a new place within management. Multiple strategies can be produced quickly, shifting the higher priority “routine” labor to the task of assessing multiple potential strategic outcomes.

This level of discovery is not simply about a singular outcome from any potential strategy. Instead, the assessment should assume that any strategy has effects in multiple dimensions. Therefore, comparing different strategies involves two approaches. One is comparing strategy characteristics in a given dimension across strategies. The other involves comparing the relative value of a strategy’s affects in one dimension against its concurrent affects in another dimension.

By analogy, comparing two different movies in terms of their characters, narrative and production qualities does not necessarily amount to one of them being superior to the other. Instead, it will show a way that each movie managed to achieve its impacts, with those impacts being more or less meaningful to any given audience. The presumptive strategist of the movie is its Director. At any given time, the key question following what is on the screen is “where is the movie going, andwhy, and who cares?” The Director makes the decision about why to be somewhere; then the Director’s question is “how do I get there from here?”

The overall result of a strategy is a constellation and network of impacts that create an environment in which operations areexpected to be desirably meaningful. This leaves open the issues of what scope of concerns determines what particular desire is to be satisfied, but that decision is a constraint of strategy, not an output. (For example, we can presume that the movie production knew it was pursuing a comedy, not a tragedy, before it got underway, not just as a result.)

Buying In

• Values, effects, dynamics and stimuli are all variables in some potential virtual formula.

• We have had the similar vocabularies of goals, objectives, processes and prerequisites for decades; but now, as thinkers, we have a heightened sense of how indeterminate their relationships might be.

• Equally interesting is the idea that some levels of determinacy may have benefits or risks ordinarily too obscure to us, worth working with.

• Big Data promotes a type of visibility that, like meteorology, lets us think more about what to do when things become a certain way, and less about the confinement of what things we can cause to become a certain way.

• Because Big Data can take strategy into the arena of continuous adaptation, strategy focuses more directly on the immediate “progress” (benefit) of operations while also continuously suggesting and comparing alternative futures. The strategist is more a director than a planner.

Critical Visibility: easier and faster, but trickier

Concept Models are prescriptive, and are influential on decisions.

They also act as predispositions and require careful use to avoid being propaganda.

Hypotheses, algorithms and simulations are literally instrumental to determining the view of reality to be considered.

Relationship Patterns are expressive, and are supportive of suppositions.

They also act as strong rhetoric and require careful use to avoid being dis-informative.

Definitions, classifications, rules, and correlations are instrumental to identifying both implicit and explicit states that can be subscribed.

Values

Effects

Dynamics

Stimuli

© 2013 Malcolm Ryder / archestra

Proceeding with Caution

The general purposes of applying various information processes give direction to a wide variety of specialized techniques. The repeated use of the techniques leads to refinements in the understanding of their effectiveness.

But even with those refinements, the outputs of separate processes do not have guaranteed functional cooperation with each other’s form.

Additionally, the first available inputs for processing may not be at the beginning of the cycle as simplified here. In that case, inputs may need to be reverse-engineered to antecedent supporting points.

Hyper-fast information refresh cycles do not necessarily solve or survive those issues.

Conceptually, the progressive logic of this cycle of processing lends itself to whatever scale of environment (macro or micro) is expected to host the impacts. However, the information being processed must be, or become, appropriate to the scale.

Regardless of those additional concerns, the main objective will be to arrive at a model that will provide the argument for what positions to take with regard to obtaining targeted beneficial impacts.

© 2013 Malcolm Ryder / archestra