archive.Joe Lamantia.com

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.

Related posts:

The EuroHCIR organizers have published proceedings from this year’s workshop (which I was unfortunately unable to attend), which means I can make our paper A Taxonomy of Enterprise Search and Discovery directly available.  The complete proceedings are here, and are also packaged as a single download.

Here’s the text of the published paper, including references, and adding in a few illustrations omitted to meet page limits on papers.  Many thanks go to co-authors Tony Russell-Rose and Mark Burrell for putting this paper together.

A Taxonomy of Enterprise Search

ABSTRACT

Classic IR (information retrieval) is predicated on the notion of users searching for information in order to satisfy a particular “information need”. However, it is now accepted that much of what we recognize as search behaviour is often not informational per se. For example, Broder (2002) has shown that the need underlying a given web search could in fact be navigational (e.g. to find a particular site or known item) or transactional (e.g. to find a sites through which the user can transact, e.g. through online shopping, social media, etc.). Similarly, Rose & Levinson (2004) have identified consumption of online resources as a further category of search behaviour and query intent.

In this paper, we extend this work to the enterprise context, examining the needs and behaviours of individuals across a range of search and discovery scenarios within various types of enterprise. We present an initial taxonomy of “discovery modes”, and discuss some initial implications for the design of more effective search and discovery platforms and tools.

Categories and Subject Descriptors

H.3.3 [Information Search and Retrieval]: Search process;

H.3.5 [Online Information Services]: Web-based services

General Terms

Human Factors.

Keywords

Enterprise search, information seeking, user behaviour, knowledge workers, search modes, information discovery, user experience design.

1. INTRODUCTION

To design better search and discovery experiences we must understand the complexities of the human-information seeking process. Numerous theoretical frameworks have been proposed to characterize this complex process, notably the standard model (Sutcliffe & Ennis 1998), the cognitive model (Norman 1998) and the dynamic model (Bates, 1989). In addition, others have investigated search as a strategic process, examining the various problem solving strategies and tactics that information seekers employ over extended periods of time (e.g. Kuhlthau, 1991).

In this paper, we examine the needs and behaviours of varied individuals across a range of search and discovery scenarios within various types of enterprise. These are based on an analysis of the scenarios derived from numerous engagements involving the development of search and business intelligence solutions utilizing the Endeca Latitude software platform. In so doing, we extend the classic IR concept of information-seeking to a broader notion of discovery-oriented problem solving, accommodating the much wider range of behaviours required to fulfil the typical goals and objectives of enterprise knowledge workers.

Our approach to enterprise discovery is an activity-centred model inspired by Don Norman’s Activity Centred Design, which “organizes according to usage” whereas “…traditional human centred design organizes according to topic, in isolation, outside the context of real, everyday use.” (Norman 2006). This approach is an extension of previous activity-centred modelling efforts which focused on a “captur[ing] a systematic and holistic view of what users need to accomplish when undertaking information retrieval tasks more complex than searching” (Lamantia 2006), employing Grounded Theory to provide methodological structure (Glaser 1967).

In this context, we present an alternative model focused on information discovery rather than information seeking per se, which has at its core an initial taxonomy of the “modes of discovery” that knowledge workers employ to satisfy their information search and discovery goals. We then discuss some initial implications of this model for the design of more effective search and discovery platforms and tools.

2. INFORMATION RETRIEVAL MODELS

The classic model of IR assumes an interaction cycle consisting of four main activities: the identification an information need, the specification of an appropriate query, the examination of retrieval results, and reformulation (where necessary) of the original query. This cycle is then repeated until a suitable result set is found (Salton 1989).

In both the above models, the user’s information need is assumed to be static. However, it is now acknowledged that information seekers’ needs often change as they interact with a search system. In recognition of this, alternative models of information seeking have been proposed. For example, Bates (1989) proposed the dynamic “berry-picking” model of information seeking, in which the information need (and consequently the query) changes throughout the search process This model also recognises that information needs are not satisfied by a single, final result set, but by the aggregation of results, insights and interactions along the way.

Bates’ work is particularly interesting as it explores the connections between the dynamic model and the search strategies and tactics that professional information-seekers employ. In particular, Bates identifies a set of 29 individual tactics, organised into four broad categories (Bates, 1979). Likewise, O’Day & Jeffries (1993) examined the use of information search results by clients of professional information intermediaries and identified three distinct “search modes” or major categories of search behaviour: (1) Monitoring a known topic or set of variables over time; (2) Following a specific plan for information gathering; (3) Exploring a topic in an undirected fashion.

O’Day and Jeffries also observed that a given search would often evolve over time into a series of interconnected searches, delimited by certain triggers and stop conditions that indicate the transitions between modes or individual searches executed as part of an overall enquiry or scenario. Moreover, O’Day & Jeffries also attempted to characterise the analysis techniques employed by the clients in interpreting the search results, identifying the following six primary categories: (1) Looking for trends or correlations; (2) Making comparisons; (3) Experimenting with different aggregations/scaling; (4) Identifying critical subsets; (5) Making assessments; (6) Interpreting data to find meaning.

More recent investigations into the relationship between information needs and search activities include that of Marchionini (2005), who identifies three major categories of search activity, namely “Lookup”, “Learn” and “Investigate”.

3. A TAXONOMY OF ENTERPRISE SEACH AND DISCOVERY

The primary source of data in this study is a set of user scenarios captured during numerous engagements involving the development of search and business intelligence solutions utilizing the Endeca Latitude software platform. These scenarios take the form of a simple narrative that illustrates the user’s end goal and the primary task or action they take to complete it, followed by a brief description of their job function or role, for example:

“I need to understand a portfolio’s exposures to assess portfolio-level investment mix” (Portfolio Manager)

“I need to understand the quality performance of a part and module set in manufacturing and the field so that I can determine if I should replace that part” (Engineering)

These scenarios were manually analyzed to identify themes or modes that appeared consistently throughout the set. For example, in each of the scenarios above there is an articulation of the need to develop an understanding or comprehension of some aspect of the data, implying that “comprehending” may constitute one such discovery mode. Inevitably, this analysis process was somewhat iterative and subjective, echoing the observations made by Bates (1979) in the identification of her search tactics: “While our goal over the long term may be a parsimonious few, highly effective tactics, our goal in the short term should be to uncover as many as we can, as being of potential assistance. Then we can test the tactics and select the good ones. If we go for closure too soon, i.e., seek that parsimonious few prematurely, then we may miss some valuable tactics.”

There are however some guiding principles that we can apply to facilitate convergence on a stable set. For example, an ideal set of modes would exhibit properties such as: Consistency (they represent approximately the same level of abstraction); Orthogonality (they operate independently to each other); and Comprehensiveness (they address the full range of discovery scenarios).

The initial set of discovery modes to emerge from this analysis consists of a set of nine, arranged into three top-level categories consistent with those of Marchionini (2005). The nine modes are as follows, each shown with a brief definition:

1. Lookup

1a. Locating: To find a specific (possibly known) item;

1b. Verifying: To confirm or substantiate that an item or set of items meets some specific criterion;

1c. Monitoring: To maintain awareness of the status of an item or data set for purposes of management or control

2. Learn

2a. Comparing: To examine two or more items to identify similarities & differences;

2b. Comprehending: To generate insight by understanding the nature or meaning of an item or data set;

2c. Exploring: To proactively investigate or examine an item or data set for the purpose of serendipitous knowledge discovery

3. Investigate

3a. Analyzing: To critically examine the detail of an item or data set to identify patterns & relationships;

3b. Evaluating: To use judgment to determine the significance or value of an item or data set with respect to a specific benchmark or model

3c. Synthesizing: To generate or communicate insight by integrating diverse inputs to create a novel artefact or composite view

Evidently, the output of this process has been optimized for the current data set and in that respect represents an initial interpretation that will need to evolve further. For example, “monitoring” may appear to be a lookup activity when considered in the context of a simple alert message, but when viewed as a strategic activity performed by an executive in the context of an organisational dashboard, a much greater degree of interaction and complexity is implied. Conversely, “exploring” is a concept whose level of abstraction may prove somewhat higher than the others, thus breaking the consistency principle suggested above.

However, the true value of the modes will be realised not by their conceptual purity or elegance but by their utility as a design resource. In this respect, they should be judged by the extent to which they facilitate the design process in capturing important characteristics common to enterprise search and discovery experiences, whilst flexibly accommodating arbitrary variations in domain, information resources, etc.

4. MODE SEQUENCES AND PATTERNS

A further interesting observation arising from the above analysis is that the mapping between scenarios and modes is not one-to–one. Instead, some scenarios are seen to involve a number of modes, sometimes with a primary or dominant mode, and often with an implied linear sequence. Moreover, certain sequences of modes tend to re-occur more frequently than others, forming specific “mode chains” or patterns, analogous to higher-level syntactic units. These patterns provide a framework for understanding the transitions between modes (echoing the triggers identified by O’Day & Jeffries), and allude to the existence of natural seams that can be used be used to provide further insight into information enterprise search and discovery behaviour.

These mode chains echo the above-mentioned efforts to create goal-based information retrieval models, which yielded modes and a set of broadly applicable “information retrieval patterns that describe the ways users combine and switch modes to meet goals: Each pattern is assembled from combinations of the same four [elemental] modes” (Lamantia 2006).

Mode Networks

Figure 1. Discovery mode network

The five most frequent mode patterns are listed below. These have been assigned descriptive (if somewhat informal) labels to aid their characterisation, along with the sequence of modes they represent and an associated example scenario:

1. Comparison-driven optimization: (Analyze-Compare- Evaluate) e.g. “Replace a problematic part with an equivalent or better part without compromising quality and cost”
2. Exploration-driven optimization: (Explore-Analyze-Evaluate) e.g. “Identify opportunities to optimize use of tooling capacity for my commodity/parts”
3. Strategic Insight (Analyze-Comprehend-Evaluate) e.g. “Understand a lead’s underlying positions so that I can assess the quality of the investment opportunity”
4. Strategic Oversight (Monitor-Analyze-Evaluate) e.g. “Monitor & assess commodity status against strategy/plan/target”
5. Comparison-driven Synthesis (Analyze-Compare-Synthesize) e.g. “Analyze and understand consumer-customer-market trends to inform brand strategy & communications plan”

Further insight may be derived by examining how the mode patterns combine across all the scenarios to the form of a “mode network”, as shown in Figure 1. Evidently, some modes act as “terminal” nodes, i.e. entry points or exit points to a discovery scenario. For example, Monitor and Explore feature only as entry points at the initiation of a scenario, whilst Synthesize and Evaluate feature only as exit points to a scenario.

5. DESIGN PRINCIPLES FOR SEARCH AND DISCOVERY SOLUTIONS

The modes establish a ‘taskonomy’ or collection of defined discovery activities which are structurally consistent, domain and scale independent, orthogonal, semantically distinct, conceptually connected, and flexibly sequenceable. Such a profile — analogous to notes in the musical scale, or the words and phrases we assemble into sentences — should allow the modes to serve as a language for the design of variable scale activity-centered discovery solutions through common constructive mechanisms such as concatenation, combination and nesting. And if the modes do act as an elementary grammar for discovery, then sustained use as a functional and interaction design language should result in the creation of larger and more complex units of meaning which offer cumulative value.

Professional experience with employing the modes as both an analytical framework for understanding discovery needs and as a design grammar for the definition of discovery solutions suggests that both implications are valid. Further, our observations of using the modes suggest the existence of recognizable patterns in the design of discovery solutions. We will briefly discuss some of the patterns observed, doing so at three common levels of solution scale: on the level of a single functional or interface element, for whole screens or interfaces composed of multiple functional elements, and for applications comprising multiple screens.

5.1 Single element patterns

5.1.1 Comparison Views

One of the most common design patterns is to support the need for the Compare mode by creating A/B type comparison views that present two display panes – each containing data display charts or tables; or single items or groups of items – side by side to emphasize similarities and differences.

5.1.2 Contextual Views

Another common design pattern supports the Analysis mode by allowing a fore-grounded view of a single chart, table, item, or list, accompanied by its contextual ‘halo’ – the full body of information available about the element such as status, origin, format, relationships to other elements; annotations; etc.

5.2 Whole screen patterns

5.2.1 Dashboard

One of the most common screen-level design patterns is to support the Monitoring and Synthesis modes by presenting a collection of metrics which in aggregate provide the status of independent processes, groups, or progress versus goals in a ‘dashboard’ style screen.

Figure: Dashboard Screen

5.2.2 Visual Discovery Screen: 4-Dimensions

 

A second common screen-level design pattern for discovery experiences is the visual discovery screen, which supports modes such Exploration, Evaluation, and Verification by layering views that present visualizations of several dimensions of a single axis of focus such as a core process, organizational unit, or KPI. When switching between layered views, the axis in focus remains the same, but the data and presentation in the dimensions adjusts to match the preferred discovery mode.

Figure: Visual Analysis Screen

5.3 Application-level patterns

5.3.1 Differentiated Application

The ‘Differentiated Application’ pattern assembles a collection of individual screens whose distinct compositions and designs support individual discovery modes of Analysis, Comparison, Evaluation and Monitoring in aggregate to address the ‘Strategic Oversight’ mode sequence. Application-level patterns often address a spectrum of discovery needs for a group of users with differing organizational responsibilities, such as management vs. detailed analysis.

Figure: Differentiated Application Structure

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6. DISCUSSION

The above analysis is predicated on the notion that the user scenarios provide a unique insight into the information needs of enterprise knowledge workers. However, a number of caveats apply to both the data and the approach.

Firstly, the scenarios were originally generated to support the development of a specific implementation rather than for the analysis above. Therefore, the principles governing their creation may not faithfully reflect the true distribution or priority of information needs among the various end user populations. Secondly, the particular sample we selected for this study was based on a number of pragmatic factors (including availability), which may not faithfully represent the true distribution or priority among enterprise organizations. Thirdly, the data will inevitably contain some degree of subjectivity, particularly in cases where scenarios were generated by proxy rather than with direct end-user contact. Fourthly, the data will inevitably contain some degree of inconsistency in cases where scenarios were documented by different individuals.

We should also acknowledge a number of caveats concerning the process itself. In inductive work with foundations in qualitatively centered frameworks such as Grounded Theory, it is expected that a number of iterations of a “propose-classify-refine” cycle will be required for the process to converge on a stable output (e.g. Rose & Levinson, 2004). In addition, those iterations should involve a variety of critical viewpoints, with the output tested and refined using a separate, independent sample on each iteration. Likewise, the process by which scenarios are classified would benefit from further rigour: this is a critical part of the process and of course relies on human judgement and inference, but that judgement needs to go beyond simple word matching and be consistently applied to each scenario so that subtle distinctions in meaning and intent can be accurately identified and recorded.

That said, some interesting comparisons can already be made with the existing frameworks. For example, the first and third of the search modes suggested by O’Day and Jeffries have also been identified as distinct discovery modes in our own study, and the second (arguably) maps on to one or more of the mode chains identified above. Likewise, the search results analysis techniques that O’Day & Jeffries identified also present some interesting parallels.

7. CONCLUSIONS AND FUTURE DIRECTIONS

To design better search and discovery experiences we must understand the complexities of the human-information seeking process. In this paper, we have examined the needs and behaviours of varied individuals across a range of search and discovery scenarios within various types of enterprise. In so doing, we have extended the classic IR concept of information-seeking to a broader notion of discovery-oriented problem solving, accommodating the much wider range of behaviours required to fulfil the typical goals and objectives of enterprise knowledge workers.

In addition, we have proposed an alternative model focused on information discovery rather than information seeking which has at its core a taxonomy of “modes of discovery” that knowledge workers employ to satisfy their information search and discovery goals. We have also examined some of the initial implications of this model for the design of more effective search and discovery platforms and tools.

Suggestions for future work include further iterations on the “propose-classify-refine” cycle using independent data. This data should ideally be acquired based on a principled sampling strategy that attempts where possible to address any biases introduced in the creation of the original scenarios. In addition, this process should be complemented by empirical research and observation of knowledge workers in context to validate and refine the discovery modes and triggers that give rise to the observed patterns of usage.

8. REFERENCES

[1] Bates, Marcia J. 1979. “Information Search Tactics.” Journal of the American Society for Information Science 30: 205-214

[2] Bates, Marcia J. 1989. “The Design of Browsing and Berrypicking Techniques for the Online Search Interface.” Online Review 13: 407-424.

[3] Broder, A. 2002. A taxonomy of web search, ACM SIGIR Forum, v.36 n.2, Fall 2002

[4] Kuhlthau, C. C. 1991. Inside the information search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science, 42, 361-371.

[5] Lamantia, J. 2006. “10 Information Retrieval Patterns” JoeLamantia.com, /information-architecture/10-information-retrieval-patterns

[6] Glaser, B. & Strauss, A. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine de Gruyter.

[7] Marchionini, G. 2006. Exploratory search: from finding to understanding. Commun. ACM 49(4): 41-46

[8] Norman, Donald A. 1988. The psychology of everyday things. New York, NY, US: Basic Books.

[9] Donald A. Norman. 2006. Logic versus usage: the case for activity centered design. Interactions 13, 6

[10] O’Day, V. and Jeffries, R. 1993. Orienteering in an information landscape: how information seekers get from here to there. INTERCHI 1993: 438-445

[11] Rose, D. and Levinson, D. 2004. Understanding user goals in web search, Proceedings of the 13th international conference on World Wide Web, New York, NY, USA

[12] Salton, G. (1989). Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA.

[13] A.G. Sutcliffe and M. Ennis. Towards a cognitive theory of information retrieval. Interacting with Computers, 10:321–351, 1998.

I’m pleased to be presenting ‘A Taxonomy of Enterprise Search’ at the upcoming EuroHCIR workshop, part of the 2011 HCI conference in the UK.  Co-authored with Tony Russell-Rose of UXLabs, and Mark Burrell here at Endeca, this is our first publication of some of the very exciting work we’re doing to understand and describe discovery activities in enterprise settings, and do so within a changed and broader framing than traditional information retrieval.  The paper builds on work I’ve done previously on understanding and defining information needs and patterns of information retrieval activity, while working on search and discovery problems as part of larger user experience architecture efforts.

Here’s the abstract of the paper:

Classic IR (information retrieval) is predicated on the notion of users searching for information in order to satisfy a particular “information need”. However, it is now accepted that much of what we recognize as search behaviour is often not informational per se. For example, Broder (2002) has shown that the need underlying a given web search could in fact be navigational (e.g. to find a particular site or known item) or transactional (e.g. to find a sites through which the user can transact, e.g. through online shopping, social media, etc.). Similarly, Rose & Levinson (2004) have identified consumption of online resources as a further category of search behaviour and query intent.

In this paper, we extend this work to the enterprise context, examining the needs and behaviours of individuals across a range of search and discovery scenarios within various types of enterprise. We present an initial taxonomy of “discovery modes”, and discuss some initial implications for the design of more effective search and discovery platforms and tools.

There’s a considerable amount of research available on information retrieval — even within a comparatively new discipline like HCIR, focused on the human to system interaction aspects of IR — but I think it’s the attempt to define an activity centered grammar for interacting with information that makes our approach worth examining.  The HCIR events in the U.S. (and now Europe) blend academic and practitioner perspectives, so are an appropriate audience for our proposed vocabulary of discovery activity ‘modes’ that’s based on a substantial body of data collected and analyzed during solution design and deployment engagements.

I’ll post the paper itself once the proceedings are available.

Endeca invites customers, partners and leading members of the broader search and discovery technology and solutions communities to meet annually, and showcase the most interesting and exciting work in the field of discovery.  As lead for the UX team that designs Endeca’s discovery products, I shared some of our recent work on patterns in the structure of discovery applications, as well as best practices in information design and visualization that we use to drive product definition and design for Endeca’s Latitude Discovery Framework.

This material is useful for program and project managers and business analysts defining requirements for discovery solutions and applications, UX and system architects crafting high-level structures and addressing long-term growth, interaction designers and technical developers defining and building information workspaces at a fine grain, and

There are three major sections: the first presents some of our tools for identifying and understanding people’s needs and goals for discovery in terms of activity (the Language of Discovery as we call it), the second brings together screen-level, application level, and user scenario / use-case level patterns we’ve observed in the applications created to meet those needs, and the final section shares condensed best practices and fundamental principles for information design and visualization based on academic research disciplines such as cognitive science and information retrieval.

It’s no coincidence that these sections reflect the application of the core UX disciplines of user research, information architecture, and interaction design to the question of “who will need to encounter information for some end, and in what kind of experience will they encounter it”.  This flow and ordering is deliberate; it demonstrates on two levels the results of our own efforts applying the UX perspective to the questions inherent in creating discovery tools, and shares some of the tools, insights, templates, and resources we use to shape the platform used to create discovery experiences across diverse industries.

Session outline

Session description

“How can you harness the power and flexibility of Latitude to create useful, usable, and compelling discovery applications for enterprise discovery workers? This session goes beyond the technology to explore how you can apply fundamental principles of information design and visualization, analytics best practices and user interface design patterns to compose effective and compelling discovery applications that optimize user discovery, success, engagement, & adoption.”

The patterns are product specific in that they show how to compose screens and applications using the predefined components in the Discovery Framework library.  However, many of the product-specific components are built to address common or recurring needs for interaction with information via well-known mechanisms such as search, filtering, navigation, visualization, and presentation of data.  In other words, even if you’re not using the literal Discovery Framework component library to compose your specific information analysis workspace, you’ll find these patterns relevant at workspace and application levels of scale.

The deeper story of these patterns is in demonstrating the evolution of discovery and analysis applications over time.  Typically, discovery applications begin by offering users a general-purpose workspace that satisfies a wide range of interaction tasks in an approximate fashion.  Over time, via successive expansions in the the scope and variety of data they present, and the discovery and analysis capabilities they provide, discovery applications grow to include several different types of workspaces that individually address distinct sets of needs for visualization and sense making by using very different combinations of components.  As a composite, these functional and informationally diverse workspaces span the full range of interaction needs for differing types of users.

I hope you find this toolkit and collection of patterns and information design principles useful.  What are some of the resources you’re using to take on these challenges?

I just posted slides from my talk at the recent Enterprise Search Summit in NY “Search Me: Designing Information Retrieval Experience”

Here’s the abstract from the session:

This case study reviews the methods and insights that emerged from an 18-month effort to coordinate and enhance the scattered user experiences of a suite of information retrieval tools sold as services by a major investment ratings agency. The session will share a method for understanding audience needs in diverse information access contexts; review a collection of information retrieval patterns, look at conceptual design methods for user experiences, and review a set of longer term patterns in customer behavior called lifecycles, and consider the impact of organizational and cultural factors on design decisions.

This session will presents reusable experience design tools and findings relevant for contexts such as enterprise search and information access, service design, and product and platform management.

Thanks to everyone who came by!

Readers active in the enterprise, intranet, portal, and syndicated content & functionality spaces might be interested in The Building Blocks of Effective Portals that appears in the November / December issue of Intranets Today.

Intranets is one of the leading publications focused on these topics, with regular contributions from the likes of Rachel Alexander, Jane McConnell, and James Roberston.
You will need a log-in to read the complete article on-line, but perhaps you were thinking of subscribing, and this will pull you in.

In case you couldn’t make it to Amsterdam for EuroIA 2008, or if you were in town but preferred to stay outside in the warmth of a sunny September Saturday than venture into the marvelous Tsuchinski theater, I’ve posted the slides from my talk Frameworks are the Future of Design.
Enjoy!

Reading about the recent release of SocialText 3 I was struck by the strong parallels between the defining characteristics of enterprise environments in 2003/2004, and the emerging public Web 2.0 landscape. The essential characteristics of many enterprise environments are:

• Syndication: streams of modular content and functionality broadcast widely to subscribers within the firewall, such as enterprise data feeds, ERP, BI capabilities, CRM, custom capabilities shared via SOA
• Services (e.g. environmental, like the bees we used to have for pollination): identity, security, publication, data management, cloud storage, imap email, etc.
• Social Structures: tangible networks & communities of like-minded people, oriented around a common practice, purpose, process, or pain; think of all the matrixed, horizontal org structures and ad-hoc networks encoded via internal email lists, IM, sprawling intranets, corporate directories, etc.

These same attributes are emerging as the hallmarks of the public Web 2.0 landscape. This is how the three S’s manifest for Web 2.0:

• Syndication: A literal and figurative torrent of content in the form of blogs, RSS, feeds, streams, APIs, for social objects of all types, as well as catalogs of rentable content
• Services: This layer is growing rapidly for the public internet, with OpenID / OAuth, mapping, visualization, backup, calendaring – the list is nearly infinite, and still expanding
• Social Structures: The Web (and soon the mobile universe) is profoundly social now, and will continue to become ever more so.

I think you can easily see the strong parallels. It’s this similarity between the older enterprise environments and the emerging Web 2.0 environment that user experience practitioners, — and especially anyone practicing information architecture — should note.
Why? As I’ve written before, modularity is everywhere in this new environment, it’s apparent at all layers of the information world, from utilities like processing power, to services, to the elements that make up the user experience. The effects of modularity in syndication, services, and social structures on developers and IT have been profound; practices, processes, organizational structures, and business models have all shifted in response.

This wave of change first affected the developers who build and work directly with code and systems. But inevitably, disciplines further up the stack are feeling the impact of this shift, though many of us (and I’m putting user experience in this class) may not know it yet.

How will we feel that impact? One obvious way is in the pressure to adopt agile and other modular product construction practices created by and for developers as the preferred way to structure user experience and design efforts. This is a mistake that confuses the different stages of software / digital product creation (as Alan Cooper explained well at Agile2008). Design is not construction, and shouldn’t be treated as if it is. And one size fits all does not work when choosing the process and toolkit used for creating complex digital products, services, or experiences.

One result of this modularity rules all approach to user experience is the erosion of bounded or well-structured design processes that balance risk effectively for the various stages of design, and were meant to ensure the quality and relevance of the resulting products and experiences. Erosion is visible the trends toward compression or elimination of recognizable design concept exploration and usability verification activities in many design methods.

More immediately – in fact staring us right in the face, though I haven’t seen mention of it yet in m/any user experience forums – is the growing number of situations wherein there’s “No designer required”.

Examples of this abound, but just consider this feature list for the Social Text 3 Dashboard:

• You decide what matters
• Create your dashboard in minutes
• Include 3rd party information and applications
• Track & attend to what’s most important to you
• Status updates flow automatically, as you work

If that’s not specific enough, here’s what comes out of the box, in the form of pre-built widgets:

• My Conversations – changes others have made to any Socialtext workspace page you authored, edited, or commented on
• My Colleagues – recent updates made by people you are subscribed to
• Workspaces – workspaces you have access to and their activity metrics
• Workspace Page – any page from any of your Socialtext workspaces
• RSS Viewer – results of an RSS feed you configure
• Workspace Tags – a tag cloud of all tags in a particular workspace
• All People Tags – a tag cloud of all tags on people in Socialtext People

No architect required for most people here… and this trend is everywhere.

And then there’s the awesome spectre ofcommoditization. Listening to a friend describe the confusing experience of trying to select a short list of design firms for inclusion in an RFP made the linkage clear to me. I’ll quote Weil’s definition of commoditization from the paper referenced above, to make the point explicit.

Please recall that commoditization denotes the development of a competitive environment where:

• Product differentiation is very difficult;
• Customer loyalty and brand values are low;
• Competition is based primarily on price; and
• Sustainable advantage comes from cost (and sometimes quality) leadership.
• Commoditization is driven by excess capacity.

Please note that I’m not implying user experience practitioners face overnight obsoletion.

But I am saying that I doubt our current disciplinary worldview and toolkit adequately prepare us for the realities of the new environment emerging so rapidly. Code, by contrast, is and always will be modular. (After all, that is the defining attribute of our alphabets.)

But user experience is holistic, and has to learn to build in its own way from these smaller pieces like a writer combining words and phrases. Eventually, you can create works of tremendous depth, richness, and sophistication; think of Ulysses by James Joyce, or the Mahabharata. These are richly nuanced experiences that are the result of working with modular elements.

My suggestion for one response to the oncoming wave of modularity and commoditization is to focus our value proposition in the creation of tools that other people use to define their individual experiences. In other words, shift our professional focus to higher layers of abstraction, and get into the business of defining and designing frameworks, networks, and systems of experience components. Practically, this will mean things like observing and defining the most valuable patterns arising in the use of systems of modular elements we design, and then advising on their use to solve problems. This is the direction common within enterprise environments, and in light of the appearance of public pattern libraries (Yahoo’s UI), I think I see it happening within parts of the user experience community. I’m not sure it’s happening fast enough, though.

I hoped to communicate some of these ideas in my talk on why frameworks are the future (at least for anyone practicing Experience Architecture) for the 2008 EuroIA Summit that just took place here in lovely Amsterdam. I’ll post the slides shortly. In the meantime, what do you think? Is user experience ready for the modularized, enterprise-like environment of Web 2.0? How are you responding to these changes? Is commoditization even on your radar?

I’ve posted slides for my recent Effective IA For Enterprise Portals presentation at the IA Summit in Miami. Portals are not a traditional space for user experience practitioners, so many thanks to the packed house that turned out, and stayed as we both started late to accommodate the crowd, and then ran long.

These slides include a substantial amount of case study and example material that I didn’t cover directly in the talk. For the repeat session on Sunday, I showed additional examples beyond those included here in the starting slides.

Stay tuned for a more detailed writeup of both published and unpublished example material – one that shows the building blocks in action at all levels of a multi-year portal effort from initial strategy through design and into governance / evolution – in part six of the Building Blocks series running in Boxes and Arrows, due out once the post-summit flurry settles down.

I’ve just started a new ‘Organizational Architecture‘ group on Slideshare, to explore links to user experience, and questions like these:

• What is organizational architecture?
• How does organizational architecture relate to user experience?
• What can user experience practitioners borrow from OA to become more effective?

Join now!