One of the most compelling outcomes of our strategic research efforts over the past several years is a growing vocabulary that articulates our cumulative understanding of the deep structure of the domains of discovery and business analytics.

Modes are one example of the deep structure we’ve found.  After looking at discovery activities across a very wide range of industries, question types, business needs, and problem solving approaches, we've identified distinct and recurring kinds of sensemaking activity, independent of context.  We label these activities Modes: Explore, compare, and comprehend are three of the nine recognizable modes.  Modes describe *how* people go about realizing insights.  (Read more about the programmatic research and formal academic grounding and discussion of the modes here: https://www.researchgate.net/publication/235971352_A_Taxonomy_of_Enterprise_Search_and_Discovery) By analogy to languages, modes are the 'verbs' of discovery activity.  When applied to the practical questions of product strategy and development, the modes of discovery allow one to identify what kinds of analytical activity a product, platform, or solution needs to support across a spread of usage scenarios, and then make concrete and well-informed decisions about every aspect of the solution, from high-level capabilities, to which specific types of information visualizations better enable these scenarios for the types of data users will analyze.

The modes are a powerful generative tool for product making, but if you've spent time with young children, or had a really bad hangover (or both at the same time...), you understand the difficult of communicating using only verbs. 

So I'm happy to share that we've found traction on another facet of the deep structure of discovery and business analytics.  Continuing the language analogy, we've identified some of the ‘nouns’ in the language of discovery: specifically, the consistently recurring aspects of a business that people are looking for insight into.  We call these discovery Subjects, since they identify *what* people focus on during discovery efforts, rather than *how* they go about discovery as with the Modes.

Defining the collection of Subjects people repeatedly focus on allows us to understand and articulate sense making needs and activity in more specific, consistent, and complete fashion.  In combination with the Modes, we can use Subjects to concretely identify and define scenarios that describe people’s analytical needs and goals.  For example, a scenario such as ‘Explore [a Mode] the attrition rates [a Measure, one type of Subject] of our largest customers [Entities, another type of Subject] clearly captures the nature of the activity — exploration of trends vs. deep analysis of underlying factors — and the central focus — attrition rates for customers above a certain set of size criteria — from which follow many of the specifics needed to address this scenario in terms of data, analytical tools, and methods.

We can also use Subjects to translate effectively between the different perspectives that shape discovery efforts, reducing ambiguity and increasing impact on both sides the perspective divide.  For example, from the language of business, which often motivates analytical work by asking questions in business terms, to the perspective of analysis.  The question posed to a Data Scientist or analyst may be something like “Why are sales of our new kinds of potato chips to our largest customers fluctuating unexpectedly this year?” or “Where can innovate, by expanding our product portfolio to meet unmet needs?”.  Analysts translate questions and beliefs like these into one or more empirical discovery efforts that more formally and granularly indicate the plan, methods, tools, and desired outcomes of analysis.  From the perspective of analysis this second question might become, “Which customer needs of type ‘A', identified and measured in terms of ‘B’, that are not directly or indirectly addressed by any of our current products, offer 'X' potential for ‘Y' positive return on the investment ‘Z' required to launch a new offering, in time frame ‘W’?  And how do these compare to each other?”.  Translation also happens from the perspective of analysis to the perspective of data; in terms of availability, quality, completeness, format, volume, etc.

By implication, we are proposing that most working organizations — small and large, for profit and non-profit, domestic and international, and in the majority of industries — can be described for analytical purposes using this collection of Subjects.  This is a bold claim, but simplified articulation of complexity is one of the primary goals of sensemaking frameworks such as this one.  (And, yes, this is in fact a framework for making sense of sensemaking as a category of activity - but we’re not considering the recursive aspects of this exercise at the moment.)

Compellingly, we can place the collection of subjects on a single continuum — we call it the Sensemaking Spectrum — that simply and coherently illustrates some of the most important relationships between the different types of Subjects, and also illuminates several of the fundamental dynamics shaping business analytics as a domain.  As a corollary, the Sensemaking Spectrum also suggests innovation opportunities for products and services related to business analytics.

The first illustration below shows Subjects arrayed along the Sensemaking Spectrum; the second illustration presents examples of each kind of Subject.  Subjects appear in colors ranging from blue to reddish-orange, reflecting their place along the Spectrum, which indicates whether a Subject addresses more the viewpoint of systems and data (Data centric and blue), or people (User centric and orange).  This axis is shown explicitly above the Spectrum.  Annotations suggest how Subjects align with the three significant perspectives of Data, Analysis, and Business that shape business analytics activity.  This rendering makes explicit the translation and bridging function of Analysts as a role, and analysis as an activity.