Data and Domain Knowledge are Where Strategies Originate

An organization with an explicit strategy has an increased probability of success. That game plan has a hierarchical architecture. An institution constructs that framework, using iteration and incrementalism. One part of that scaffolding is domain knowledge and data. Those components interact with one another to form the wellspring from which an organization generates a strategy.

That company’s approach does not just emerge from nowhere. Its fountainhead can be domain knowledge. A business acquires expertise, over the course its existence. For example, an institution makes widgets, for decades. Over those years, it acquires knowledge about problem points in producing and selling its sprockets. That shrewdness leads to conclusions from which that organization develops a model. That design results in a process from which that institution constructs an implementation. A strategy can have its origin be domain knowledge.

Yet, that game plan does not have to spring forth from a company’s expertise. Its roots can be data. A business can analyze its numbers, finding certain trends. For example, a football team has statistics about draft picks and value. It crunches those figures, discovering that average approximate value declines the later a player is picked. It draws conclusions from that tendency. It creates a model from those judgments, and it develops a process from that design. That organization eventually rolls out an implementation of that methodology. An institution’s strategy can have its roots be data.

That company’s scheme can also have a more complex origin. Its wellspring can be both data and domain knowledge. The two elements can be mined by an organization to draw trends from which conclusions can be drawn. For example, the numbers show that average value declines the later a player is picked. However, expertise holds that teams value certain positions more than they do others. An institution uses those statistics and that experience to draw the conclusions from which its strategy eventually emerges. Data and domain knowledge can combine to form the origin of an approach.

Those elements do not just combine to form an admixture of inspiration. Numbers can be used by an institution, to validate expertise (and vice versa). For example, accumulated wisdom holds that specific problems are common in making and selling widgets. A company tracks statistics about its issues. That business analyzes those figures to determine how common various difficulties are. That data verifies an organization’s experience, or it prompts questions in need of further investigation. Inquiries can run the opposite direction too, where trends in statistics are not supported by domain knowledge. The two components verify one another.

Yet, expertise and numbers interact in ways, beyond verification and reinforcement. They feed back into one another. For example, accumulated wisdom can inform an organization about what figures to investigate and how to examine them. That institution might have buckets of data and many modes of analysis, at its disposal. Many of those statistics and approaches might be unlikely to yield any trend, creating a sizeable haystack with very few needles. That pile’s size can be shrunk by domain knowledge. That expertise can be informed by patterns culled from numbers. The informational components of an organization’s strategy influence each other.

An institution’s game plan is built from pieces that interact in complex ways. The two descriptive elements verify and influence each other, in a way that helps a company build its approach. That business collects data and accumulates wisdom, over its existence. It can use one of them or both, to draw the conclusions on which it bases a methodology. That process has its roots, in numbers and domain knowledge. How to move from those parts to a strategy is discussed in future articles.

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This article originally appeared in the Strategy Construction Journal hosted by ExperTech Insights.