Friday Jun 20, 2014

Empirical Discovery - Concept and Workflow Model

Concept models are a powerful tool for articulating the essential elements and relationships that define new or complex things we need to understand.  We've previously defined empirical discovery as a new method, looking at antecedents, and also comparing and contrasting the distinctive characteristics of Empirical Discovery with other knowledge creation and insight seeking methods.  I'm now sharing our concept model of Empirical Discovery, which identifies the most important actors, activities, and outcomes of empirical discovery efforts, to complement the written definition by illustrating how the method works in practice.

In this model, we illustrate the activities of the three kinds of people most central to discovery efforts: Insight Consumers, Data Scientists, and Data Engineers. We have robust definitions of all the major actors involved in discovery (used to drive product development), and may share some of these various personas, profiles, and snapshots subsequently.  For reading this model, understand Insight Consumers as the people who rely on insights from discovery efforts to effect and manage the operations of the business.  Data Scientists are the sensemakers who achieve insights, and create data products, and analytical models through discovery efforts.  Data Engineers enable discovery efforts by building the enterprise data analysis infrastructure necessary for discovery, and often implement the outcomes of empirical discovery by building new tools based on the insights and models Data Scientists create.

A key assumption of this model is that discovery is by definition an iterative and serendipitous method, relying on frequent back-steps and unpredictable repetition of activities as a necessary aspect of how discovery efforts unfold.  This model also assumes the data, methods, and tools shift during discovery efforts, in keeping with the evolution of motivating questions, and the achievement of interim outcomes.  Similarly, discovery efforts do not always involve all of these elements.

To keep the essential structure and relationships between elements clear and in the foreground, we have not shown all of the possible iterative loops or repeated steps. Some closely related concepts are grouped together, to allow reading the model on two levels of detail.

For a simplified view, follow the links between named actors and groups of concepts shown with colored backgrounds and labels.  In this reading, an Insight Consumer articulates questions to a Data Scientist, who combines domain knowledge with the Empirical Discovery Method (yellow) to direct the application of Analytical Tools (blue) and Models (salmon) to Data Sets (green) drawn from Data Sources (magenta).  The Data Scientist shares Insights resulting from discovery efforts with the Insight Consumer, while Data Engineers may implement the models or data products created by the Data Scientist by turning them into tools and infrastructure for the rest of the business.  For a more detailed view of the specific concepts and activities common to Empirical discovery efforts, follow the links between the individual concepts within these named groups.  (Note: there are two kinds of connections; solid arrows indicating definite relationships, and for the Data Sets and Models groups, dashed arrows indicating possible paths of evolution.  More on this to follow)

Another way to interpret the two levels of detail in this model is as descriptions of formal vs. informal implementations of the empirical discovery method.  People and organizations who take a more formal approach to empirical discovery may require explicitly defined artifacts and activities that address each major concept, such as predictions and experimental results.  In less formal approaches, Data Scientists may implicitly address each of the major concepts and activities, such as framing hypotheses, or tracking the states of data sets they are working with, without any formal artifact or decision gateway.  This situational flexibility is follow-on of the applied nature of the empirical discovery method, which does not require scientific standards of proof and reproducibility to generate valued outcomes. 

The story begins in the upper right corner, when an Insight Consumer articulates a belief or question to a Data Scientist, who then translates this motivating statement into a planned discovery effort that addresses the business goal. The Data Scientist applies the Empirical Discovery Method (concepts in yellow); possibly generating a hypothesis and accompanying predictions which will be tested by experiments, choosing data from the range of available data sources (grouped in magenta), and selecting initial analytical methods consistent with the domain, the data sets (green), and the analytical or reference models (salmon) they will work with.  Given the particulars of the data and the analytical methods, the Data Scientist employs specific analytical tools (blue) such as algorithms and statistical or other measures, based on factors such as expected accuracy, and speed or ease of use.  As the effort progresses through iterations, or insights emerge, experiments may be added or revised, based on the conclusions the Data Scientist draws from the results and their impact on starting predictions or hypotheses.  

For example, an Insight Consumer who works in a product management capacity for an on-line social network with a business goal of increasing users’ level of engagement with the service wishes to identify opportunities to recommend users establish new connections with other similar and possibly known users based on unrecognized affinities in their posted profiles.  The data scientist translates this business goal into a series of experiments investigating predictions about which aspects of user profiles more effectively predict the likelihood of creating new connections in response to system-generated recommendations for similarity.  The Data Scientist frames experiments that rely on data from the accumulated logs of user activities within the network that have been anonymized to comply with privacy policies, selecting specific working sets of data to analyze based on awareness of the shoe and nature of the attributes that appear directly in users’ profiles both across the entire network, and among pools of similar but unconnected users. The Data Scientist plans to begin with analytical methods useful for predictive modeling of the effectiveness of recommender systems in network contexts, such as measurements of the affinity of users’ interests based on semantic analysis of social objects shared by users within this network and also publicly in other online media, and also structural or topological measures of relative position and distance from the field of network science.  The Data Scientist chooses a set of standard social network analysisalgorithms and measures, combined with custom models for interpreting user activity and interest unique to this network.  The Data Scientist has predefined scripts and open source libraries available for ready application to data (MLlib, Gephi, Weka, Pandas, etc.) in the form of Analytical tools, which she will combine in sequences according to the desired analytical flow for each experiment.

The nature of analytical engagement with data sets varies during the course of discovery efforts, with different types of data sets playing different roles at specific stages of the discovery workflow.  Our concept map simplifies the lifecycle of data for purposes of description, identifying five distinct and recognizable ways data are used by the Data Scientist, with five corresponding types of data sets.  In some cases, formal criteria on data quality, completeness, accuracy, and content govern which stage of the data lifecycle any  given data set is at.  In most discovery efforts, however, Data Scientists themselves make a series of judgements about when and how the data in hand is suitable for use.  The dashed arrows linking the five types of data sets capture the approximate and conditional nature of these different stages of evolution.  In practice, discovery efforts begin with exploration of data that may or may not be relevant for focused analysis, but which requires some direct engagement to and attention to rule in or out of consideration. Focused analytical investigation of the relevant data follows, made possible by the iterative addition, refinement and transformation (wrangling - more on this in later posts) of the exploratory data in hand.  At this stage, the Data Scientist applies analytical tools identified by their chosen analytical method.  The model building stage seeks to create explicit, formal, and reusable models that articulate the patterns and structures found during investigation.  When validation of newly created analytical models is necessary, the Data Scientist uses appropriate data - typically data that was not part of explicit model creation.  Finally, training data is sometimes necessary to put models into production - either using them for further steps in analytical workflows (which can be very complex), or in business operations outside the analytical context.  

Because so much discovery activity requires transformation of the data before or during analysis, there is great interest in the Data Science and business analytics industries in how Data Scientists and sensemakers work with data at these various stages.  Much of this attention focuses on the need for better tools for transforming data in order to make analysis possible.  This model does not explicitly represent wrangling as an activity, because it is not directly a part of the empirical discovery method; transformation is done only as and when needed to make analysis possible.  However, understanding the nature of wrangling and transformation activities is a very important topic for grasping discovery, so I’ll address in later postings. (We have a good model for this too…)

Empirical discovery efforts aim to create one or more of the three types of outcomes shown in orange: insights, models, and data products.  Insights, as we’ve defined them previously, are discoveries that change people’s perspective or understanding, not simply the results of analytical activity, such as the end values of analytical calculations, the generation of reports, or the retrieval and aggregation of stored information.   

One of the most valuable outcomes of discovery efforts is the creation of externalized models that describe behavior, structure or relationships in clear and quantified terms.  The models that result from empirical discovery efforts can take many forms — google 'predictive model’ for a sense of the tremendous variation in what people active in business analytics consider to be a useful model — but their defining characteristic is that a model always describes aspects of a subject of discovery and analysis that are not directly present in the data itself.  For example, if given the node and edge data identifying all of the connections between people in the social network above, one possible model resulting from analysis of the network structure is a descriptive readout of the topology of the network as scale-free, with some set of subgraphs, a range of node centrality values', a matrix of possible shortest paths between nodes or subgraphs, etc.  It is possible to make sense of, interpret, or circulate a model independently of the data it describes and is derived from.  

Data Scientists also engage with models in distinct and recognizable ways during discovery efforts.  Reference models, determined by the domain of investigation, often guide exploratory analysis of discovery subjects by providing Data Scientists with general  explanations and quantifications for processes and relationships common to the domain.  And the models generated as insight and understanding accumulate during discovery evolve in stages from initial articulation through validation to readiness for production implementation; which means being put into effect directly on the operations of the business.   

Data products are best understood as 'packages' of data which have utility for other analytical or business purposes, such as a list of users in the social network who will form new connections in response to system-generated suggestions of other similar users.  Data products are not literally finished products that the business offers for external sale or consumption.  And as background, we assume operationalization or ‘implementation’ of the outcomes of empirical discovery efforts to change the functioning of the business is the goal of different business processes, such as product development.  While empirical discovery focuses on achieving understanding, rather than making things, this is not the only thing Data Scientists do for the business.  The classic definition of Data Science as aimed at creating new products based on data which impact the business, is a broad mandate, and many of the position descriptions for data science jobs require participation in product development efforts.

Two or more kinds of outcomes are often bundled together as the results of a genuinely successful discovery effort; for example, an insight that two apparently unconnected business processes are in fact related through mutual feedback loops, and a model explicitly describing and quantifying the nature of the relationships as discovered through analysis.

There’s more to the story, but as one trip through the essential elements of empirical discovery, this is a logical point to pause and ask what might be missing from this model? And how can it be improved?

Monday Jun 09, 2014

The Sensemaking Spectrum for Business Analytics: Translating from Data to Business Through Analysis

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: 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.


Subjects are best understood as fuzzy categories [], rather than tightly defined buckets.  For each Subject, we suggest some of the most common examples: Entities may be physical things such as named products, or locations (a building, or a city); they could be Concepts, such as satisfaction; or they could be Relationships between entities, such as the variety of possible connections that define linkage in social networks.  Likewise, Events may indicate a time and place in the dictionary sense; or they may be Transactions involving named entities; or take the form of Signals, such as ‘some Measure had some value at some time’ - what many enterprises understand as alerts.  

The central story of the Spectrum is that though consumers of analytical insights (represented here by the Business perspective) need to work in terms of Subjects that are directly meaningful to their perspective — such as Themes, Plans, and Goals — the working realities of data (condition, structure, availability, completeness, cost) and the changing nature of most discovery efforts make direct engagement with source data in this fashion impossible.  Accordingly, business analytics as a domain is structured around the fundamental assumption that sense making depends on analytical transformation of data.  Analytical activity incrementally synthesizes more complex and larger scope Subjects from data in its starting condition, accumulating insight (and value) by moving through a progression of stages in which increasingly meaningful Subjects are iteratively synthesized from the data, and recombined with other Subjects.  The end goal of  ‘laddering’ successive transformations is to enable sense making from the business perspective, rather than the analytical perspective.

Synthesis through laddering is typically accomplished by specialized Analysts using dedicated tools and methods. Beginning with some motivating question such as seeking opportunities to increase the efficiency (a Theme) of fulfillment processes to reach some level of profitability by the end of the year (Plan), Analysts will iteratively wrangle and transform source data Records, Values and Attributes into recognizable Entities, such as Products, that can be combined with Measures or other data into the Events (shipment of orders) that indicate the workings of the business.  

More complex Subjects (to the right of the Spectrum) are composed of or make reference to less complex Subjects: a business Process such as Fulfillment will include Activities such as confirming, packing, and then shipping orders.  These Activities occur within or are conducted by organizational units such as teams of staff or partner firms (Networks), composed of Entities which are structured via Relationships, such as supplier and buyer.  The fulfillment process will involve other types of Entities, such as the products or services the business provides.  The success of the fulfillment process overall may be judged according to a sophisticated operating efficiency Model, which includes tiered Measures of business activity and health for the transactions and activities included.  All of this may be interpreted through an understanding of the operational domain of the businesses supply chain (a Domain).

We'll discuss the Spectrum in more depth in succeeding posts.


Exploring the emerging space of discovery interactions, analytics, and sensemaking.


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