Friday Mar 28, 2014

Data Science Highlights: An Investigation of the Discipline

I've posted a substantial readout summarizing some of the more salient findings from a long-running programmatic research program into data science. This deck shares synthesized findings around many of the facets of data science as a discipline, including practices, workflow, tools, org models, skills, etc. This readout distills a very wide range of inputs, including; direct interviews, field-based ethnography, community participation (real-world and on-line), secondary research from industry and academic sources, analysis of hiring and investment activity in data science over several years, descriptive and definitional artifacts authored by practitioners / analysts / educators, and other external actors, media coverage of data science, historical antecedents, the structure and evolution of professional disciplines, and even more.

I consider it a sort of business-anthropology-style investigation of data science, conducted from the viewpoint of product making's primary aspects; strategy, management, design, and delivery.

I learned a great deal during the course of this effort, and expect to continue to learn, as data science will continue to evolve rapidly for the next several years.

Data science practitioners looking at this material are invited to provide feedback about where these materials are accurate or inaccurate, and most especially about what is missing, and what is coming next for this very exciting field.

Wednesday Mar 26, 2014

Data Science and Empirical Discovery: A New Discipline Pioneering a New Analytical Method

One of the essential patterns of science and industry in the modern era is that new methods for understanding — what I’ll call sensemaking from now on — often emerge hand in hand with new professional and scientific disciplines.  This linkage between new disciplines and new methods follows from the  deceptively simple imperative to realize new types of insight, which often means analysis of new kinds of data, using new techniques, applied from newly defined perspectives. New viewpoints and new ways of understanding are literally bound together in a sort of symbiosis.

One familiar example of this dynamic is the rapid development of statistics during the 18th and 19th centuries, in close parallel with the rise of new social science disciplines including economics (originally political economy) and sociology, and natural sciences such as astronomy and physics.  On a very broad scale, we can see the pattern in the tandem evolution of the scientific method for sensemaking, and the codification of modern scientific disciplines based on precursor fields such as natural history and natural philosophy during the scientific revolution

Today, we can see this pattern clearly in the simultaneous emergence of Data Science as a new and distinct discipline accompanied by Empirical Discovery, the new sensemaking and analysis method Data Science is pioneering.  Given its dramatic rise to prominence recently, declaring Data Science a new professional discipline should inspire little controversy. Declaring Empirical Discovery a new method may seem bolder, but when we with the essential pattern of new disciplines appearing in tandem with new sensemaking methods in mind, it is more controversial to suggest Data Science is a new discipline that lacks a corresponding new method for sensemaking.  (I would argue it is the method that makes the discipline, not the other way around, but that is a topic for fuller treatment elsewhere)

What is empirical discovery?  While empirical discovery is a new sensemaking method, we can build on two existing foundations to understand its distinguishing characteristics, and help craft an initial definition.  The first of these is an understanding of the empirical method. Consider the following description:

"The empirical method is not sharply defined and is often contrasted with the precision of the experimental method, where data are derived from the systematic manipulation of variables in an experiment.  ...The empirical method is generally characterized by the collection of a large amount of data before much speculation as to their significance, or without much idea of what to expect, and is to be contrasted with more theoretical methods in which the collection of empirical data is guided largely by preliminary theoretical exploration of what to expect. The empirical method is necessary in entering hitherto completely unexplored fields, and becomes less purely empirical as the acquired mastery of the field increases. Successful use of an exclusively empirical method demands a higher degree of intuitive ability in the practitioner."

Data Science as practiced is largely consistent with this picture.  Empirical prerogatives and understandings shape the procedural planning of Data Science efforts, rather than theoretical constructs.  Semi-formal approaches predominate over explicitly codified methods, signaling the importance of intuition.  Data scientists often work with data that is on-hand already from business activity, or data that is newly generated through normal business operations, rather than seeking to acquire wholly new data that is consistent with the design parameters and goals of formal experimental efforts.  Much of the sensemaking activity around data is explicitly exploratory (what I call the 'panning for gold' stage of evolution - more on this in subsequent postings), rather than systematic in the manipulation of known variables.  These exploratory techniques are used to address relatively new fields such as the Internet of Things, wearables, and large-scale social graphs and collective activity domains such as instrumented environments and the quantified self.  These new domains of application are not mature in analytical terms; analysts are still working to identify the most effective techniques for yielding insights from data within their bounds.  

The second relevant perspective is our understanding of discovery as an activity that is distinct and recognizable in comparison to generalized analysis: from this, we can summarize as sensemaking intended to arrive at novel insights, through exploration and analysis of diverse and dynamic data in an iterative and evolving fashion. 

Looking deeper, one specific characteristic of discovery as an activity is the absence of formally articulated statements of belief and expected outcomes at the beginning of most discovery efforts.  Another is the iterative nature of discovery efforts, which can change course in non-linear ways and even 'backtrack' on the way to arriving at insights: both the data and the techniques used to analyze data change during discovery efforts.  Formally defined experiments are much more clearly determined from the beginning, and their definition is less open to change during their course. A program of related experiments conducted over time may show iterative adaptation of goals, data and methods, but the individual experiments themselves are not malleable and dynamic in the fashion of discovery.  Discovery's emphasis on novel insight as preferred outcome is another important characteristic; by contrast, formal experiments are repeatable and verifiable by definition, and the degree of repeatability is a criteria of well-designed experiments.  Discovery efforts often involve an intuitive shift in perspective that is recountable and retraceable in retrospect, but cannot be anticipated.

Building on these two foundations, we can define Empirical Discovery as a hybrid, purposeful, applied, augmented, iterative and serendipitous method for realizing novel insights for business, through analysis of large and diverse data sets.

Let’s look at these facets in more detail.  

Empirical discovery primarily addresses the practical goals and audiences of business (or industry), rather than scientific, academic, or theoretical objectives.  This is tremendously important, since  the practical context impacts every aspect of Empirical Discovery. 

'Large and diverse data sets' reflects the fact that Data Science practitioners engage with Big Data as we currently understand it; situations in which the confluence of data types and volumes exceeds the capabilities of business analytics to practically realize insights in terms of tools, infrastructure, practices, etc.

Empirical discovery uses a rapidly evolving hybridized toolkit, blending a wide range of general and advanced statistical techniques with sophisticated exploratory and analytical methods from a wide variety of sources that includes data mining, natural language processing, machine learning, neural networks, bayesian analysis, and emerging techniques such as topological data analysis and deep learning

What's most notable about this hybrid toolkit is that Empirical Discovery does not originate novel analysis techniques, it borrows tools from established disciplines such information retrieval, artificial intelligence, computer science, and the social sciences.  Many of the more specialized or apparently exotic techniques data science and empirical discovery rely on, such as support vector machines, deep learning, or measuring mutual information in data sets, have established histories of usage in academic or other industry settings, and have reached reasonable levels of maturity.  Empirical discovery's hybrid toolkit is  transposed from one domain of application to another, rather than invented.  

Empirical Discovery is an applied method in the same way Data Science is an applied discipline: it originates in and is adapted to business contexts, it focuses on arriving at useful insights to inform business activities, and it is not used to conduct basic research.  At this early stage of development, Empirical Discovery has no independent and articulated theoretical basis and does not (yet) advance a distinct body of knowledge based on theory or practice. All viable disciplines have a body of knowledge, whether formal or informal, and applied disciplines have only their cumulative body of knowledge to distinguish them, so I expect this to change.

Empirical discovery is not only applied, but explicitly purposeful in that it is always set in motion and directed by an agenda from a larger context, typically the specific business goals of the organization acting as a prime mover and funding data science positions and tools.  Data Science practitioners effect Empirical Discovery by making it happen on a daily basis - but wherever there is empirical discovery activity, there is sure to be intentionality from a business view.  For example, even in organizations with a formal hack time policy, our research suggests there is little or no completely undirected or self-directed empirical discovery activity, whether conducted by formally recognized Data Science practitioners, business analysts, or others. 

One very important implication of the situational purposefulness of Empirical Discovery is that there is no direct imperative for generating a body of cumulative knowledge through original research: the insights that result from Empirical Discovery efforts are judged by their practical utility in an immediate context.  There is also no explicit scientific burden of proof or verifiability associated with Empirical Discovery within it's primary context of application.  Many practitioners encourage some aspects of verifiability, for example, by annotating the various sources of data used for their efforts and the transformations involved in wrangling data on the road to insights or data products, but this is not a requirement of the method.  Another implication is that empirical discovery does not adhere to any explicit moral, ethical, or value-based missions that transcend working context.  While Data Scientists often interpret their role as transformative, this is in reference to business.  Data Science is not medicine, for example, with a Hippocratic oath.

Empirical Discovery is an augmented method in that it depends on computing and machine resources to increase human analytical capabilities: It is simply impractical for people to manually undertake many of the analytical techniques common to Data Science.  An important point to remember about augmented methods is that they are not automated; people remain necessary, and it is the combination of human and machine that is effective at yielding insights.  In the problem domain of discovery, the patterns of sensemaking activity leading to insight are intuitive, non-linear, and associative; activites with these characteristics are not fully automatable with current technology. And while many analytical techniques can be usefully automated within boundaries, these tasks typically make up just a portion of an complete discovery effort.  For example, using latent class analysis to explore a machine-sampled subset of a larger data corpus is task-specific automation complementing human perspective at particular points of the Empirical Discovery workflow.  This dependence on machine augmented analytical capability is recent within the history of analytical methods.  In most of the modern era -- roughly the later 17th, 18th, 19th and early 20th centuries -- the data employed in discovery efforts was manageable 'by hand', even when using the newest mathematical and analytical methods emerging at the time.  This remained true until the effective commercialization of machine computing ended the need for human computers as a recognized role in the middle of the 20th century.

The reality of most analytical efforts -- even those with good initial definition -- is that insights often emerge in response to and in tandem with changing and evolving questions which were not identified, or perhaps not even understood, at the outset.  During discovery efforts, analytical goals and techniques, as well as the data under consideration, often shift in unpredictable ways, making the path to insight dynamic and non-linear.  Further, the sources of and inspirations for insight are  difficult or impossible to identify both at the time and in retrospect. Empirical discovery addresses the complex and opaque nature of discovery with iteration and adaptation, which combine  to set the stage for serendipity.

With this initial definition of Empirical Discovery in hand, the natural question is what this means for Data Science and business analytics?  Three thigns stand out for me.  First, I think one of the central roles played by Data Science is in pioneering the application of existing analytical methods from specialized domains to serve general business goals and perspectives, seeking effective ways to work with the new types (graph, sensor, social, etc.) and tremendous volumes (yotta, yotta, yotta...) of business data at hand in the Big Data moment and realize insights

Second, following from this, Empirical Discovery is methodological a framework within and through which a great variety of analytical techniques at differing levels of maturity and from other disciplines are vetted for business analytical utility in iterative fashion by Data Science practitioners. 

And third, it seems this vetting function is deliberately part of the makeup of empirical discovery, which I consider a very clever way to create a feedback loop that enhances Data Science practice by using Empirical Discovery as a discovery tool for refining its own methods.

Tuesday Oct 22, 2013

Understanding Data Science: Recent Studies

If you need such a deeper understanding of data science than Drew Conway's popular venn diagram model, or Josh Wills' tongue in cheek characterization, "Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician." two relatively recent studies are worth reading.  

'Analyzing the Analyzers,' an O'Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists -- effectively personas, in a design sense -- based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That's not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science. 

And the paper 'Enterprise Data Analysis and Visualization: An Interview Study' by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there's correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I'd suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work.

We've undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years -- part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities -- and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I'll share and discuss in upcoming posts.

Friday Oct 18, 2013

Defining Discovery: Core Concepts

Discovery tools have had a referencable working definition since at least 2001, when Ben Shneiderman published 'Inventing Discovery Tools: Combining Information Visualization with Data Mining'.  Dr. Shneiderman suggested the combination of the two distinct fields of data mining and information visualization could manifest as new category of tools for discovery, an understanding that remains essentially unaltered over ten years later.  An industry analyst report titled Visual Discovery Tools: Market Segmentation and Product Positioning from March of this year, for example, reads, "Visual discovery tools are designed for visual data exploration, analysis and lightweight data mining."

Tools should follow from the activities people undertake (a foundational tenet of activity centered design), however, and Dr. Shneiderman does not in fact describe or define discovery activity or capability. As I read it, discovery is assumed to be the implied sum of the separate fields of visualization and data mining as they were then understood.  As a working definition that catalyzes a field of product prototyping, it's adequate in the short term.  In the long term, it makes the boundaries of discovery both derived and temporary, and leaves a substantial gap in the landscape of core concepts around discovery, making consensus on the nature of most aspects of discovery difficult or impossible to reach.  I think this definitional gap is a major reason that discovery is still an ambiguous product landscape.

To help close that gap, I'm suggesting a few definitions of four core aspects of discovery.  These come out of our sustained research into discovery needs and practices, and have the goal of clarifying the relationship between discvoery and other analytical categories.  They are suggested, but should be internally coherent and consistent.  

Discovery activity is: "Purposeful sense making activity that intends to arrive at new insights and understanding through exploration and analysis (and for these we have specific defintions as well) of all types and sources of data."

Discovery capability is: "The ability of people and organizations to purposefully realize valuable insights that address the full spectrum of business questions and problems by engaging effectively with all types and sources of data."

Discovery tools: "Enhance individual and organizational ability to realize novel insights by augmenting and accelerating human sense making to allow engagement with all types of data at all useful scales."

Discovery environments: "Enable organizations to undertake effective discovery efforts for all business purposes and perspectives, in an empirical and cooperative fashion."

Note: applicability to a world of Big data is assumed - thus the refs to all scales / types / sources - rather than stated explicitly.  I like that Big Data doesn't have to be written into this core set of definitions, b/c I think it's a transitional label - the new version of Web 2.0 - and goes away over time.

References and Resources:

Tuesday Aug 20, 2013

Discovery and the Age of Insight

Several weeks ago, I was invited to speak to an audience of IT and business leaders at Walmart about the Language of Discovery.   Every presentation is a feedback opportunity as much as a chance to broadcast our latest thinking (musicians call it trying out new material), so I make a point to share evolving ideas and synthesize what we've learned since the last instance of public dialog.  

For the audience at Walmart, as part of the broader framing for the Age of Insight, I took the opportunity to share findings from some of the recent research we've done on Data Science (that's right, we're studying data science).  We've engaged consistently with data science practitioners for several years now (some of the field's leaders are alumni of Endeca), as part of our ongoing effort to understand the changing nature of analytical and sense making activities, the people undertaking them, and the contexts in which they take place.  We've seen the discipline emerge from an esoteric specialty into full mainstream visibility for the business community.  Interpreting what we've learned about data science through a structural and historic perspective lead me to draw a broad parallel between data science now and natural philosophy at its early stages of evolution.

We also shared some exciting new models for enterprise information engagement; crafting scenarios using language of discovery to describe discovery needs and activity at the level of discovery architecture, IT portfolio planning,  and knowledge management (which correspond to UX, technology, and business perspectives) - demonstrating the versatility of the language as a source of linkage across separate disciplines.

We continue to identify new frontiers for the language of discovery - I'm looking forward to sharing some of this work soon.

Wednesday Jun 26, 2013

The Endeca UI Design Pattern Library Returns

I'm happy to announce that the Endeca UI Design Pattern Library - now titled the Endeca Discovery Pattern Library - is once again providing guidance and good practices on the design of discovery experiences.  Launched publicly in 2010 following several years of internal development and usage, the Endeca Pattern Library is a unique and valued source of industry-leading perspective on discovery - something I've come to appreciate directly through  fielding the consistent stream of inquiries about the library's status, and requests for its rapid return to public availability.

Restoring the library as a public resource is only the first step!  For the next stage of the library's evolution, we plan to increase the scope of the guidance it offers beyond user interface design to the broader topic of discovery.  This could include patterns for architecture at the systems, user experience, and business levels; information and process models; analytical method and activity patterns for conducting discovery; and organizational and resource patterns for provisioning discovery capability in different settings.  We'd like guidance from the community on the kinds of patterns that are most valuable - so make sure to let us know.

And we're also considering ways to increase the number of patterns the library offers, possibly by expanding the set of contributors and the authoring mechanisms. If you'd like to contribute, please get in touch.

Here's the new address of the library:

And I should say 'Many thanks' to the UXDirect team and all the others within the Oracle family who helped - literally - keep the library alive, and restore it as a public resource.

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


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