Socializing Big Data through BRPs

September 11, 2013


To start let’s consider two distinctions about organizational processes. Following Sig over at Thingamy, two basic types of processes exist: easily repeatable processes (ERPs) and barely repeatable processes (BRPs).

ERPs: Processes that handle resources, from human (hiring, firing, payroll and more) to parts and products through supply chains, distribution and production.

BRPs: Typically exceptions to the ERPs, anything that involves people in non-rigid flows through education, health, support, government, consulting or the daily unplanned issues that happens in every organisation.

As I noted in Social Learning and Exception Handling, BRPs result in business exceptions and take up almost all of the time employees spend at work. Interestingly, much of the writing I see on Big Data is about making ERPs more efficient or making guesses about when to expect occurrences of a BRP. In other words, both goals are really about making coordination of organizational efforts more efficient and/or effective.

How organizations coordinate their activities is essential to the way they function. What makes sense for the organization’s internal processes may not make sense in its ecosystem, and vice versa. These are distinctions that analysts of Big Data sometimes fail to note and consider.

For example, in The Industrial Internet the Future is Healthy, Brian Courtney notes the following about the use of sensors in industrial equipment and the benefits derived from storing at big data scale.

Data science is the study of data. It brings together math, statistics, data engineering, machine learning, analytics and pattern matching to help us derive insights from data. Today, industrial data is used to help us determine the health of our assets and to understand if they are running optimally or if they are in an early stage of decay. We use analytics to predict future problems and we train machine learning algorithms to help us identify complex anomalies in large data sets that no human could interpret or understand on their own [my emphasis].

The rationale behind using data science to interpret equipment health is so we can avoid unplanned downtime. Reducing down time increases uptime, and increased uptime leads to increases in production, power, flight and transportation. It ensures higher return on assets, allowing companies to derive more value from investment, lowering total cost of ownership and maximizing longevity.

In other words, Courtney’s analysis of the big data generated from sensors that constantly measure key indicators about a piece of equipment assumes the data ensures a decrease in downtime and an increase in uptime resulting in increases in production, power, flight and transportation. Yet, the implied causal relationship doesn’t translate to all cases, especially those involving barely repeatable processes (BRPs) that produce business exceptions. It is in BRPs that the real usefulness of big data manifests itself, but not on its own. As Dana Boyd and Kate Crawford note in Critical Questions for Big Data, “Managing context in light of Big Data will be an ongoing challenge.”

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Podular Organization and Edge Businesses

May 9, 2013
Podulation -- From Dave Gray's Connected Company

Podular Design — From Dave Gray’s Connected Company

In Institutional Innovation and Podular Design I noted a number of insights from the Aspen Institute’s report, Institutional Innovation: Oxymoron or Imperative?, especially that “the most important innovation challenges are now in fact institutional in nature.” As an aside, let me just note that institutions typically change in dramatic ways only over long periods of time. Think of institutions such as religion, government, the economy, and then consider the various organizational forms in which these institutions took shape across cultures over time.

One insight I have not discussed in previous posts is relevant to understanding the changing way teams work together in organizations and, by implication, in a Connected Company — as outlined by Dave Gray. Richard Adler the Rapporteur for the Aspen sessions, noted that,

“New findings about the power of collective intelligence and about the most effective ways of organizing teams are providing practical insights about how to accelerate innovation.”

To start, let’s consider many companies organize teams and then turn to the “power of collective intelligence” mentioned by Adler to see how the two relate to podular organization. Several research projects in recent years noted the fuzzy boundaries of teams in large organizations. Skilful Minds first noted this phenomena in Who’s on Your Team? Enterprise 2.0 and Team Boundaries , and then a couple of years later in Social Learning, Collaboration, and Team Identity.

In fact, the phenomena of transitory team membership is so pervasive that some people propose we analyze “teaming” rather than teams when talking about how groups organize for cross-functional purposes within, or between, companies. Consider, for example the way, Mark Mortensen summarizes this trend in team dynamics,

First, organizations increasingly require collaborations to be fluid in their organization and composition, able to adapt to the rapid changes of the external environment. Second, collaborations increasingly overlap with one another, sharing resources — including people — as those resources become more limited due to increased competition. Third, collaborations must increasingly take into consideration the different contexts within which collaborators are embedded, including locations, time zones, cultures, and languages, structures, or organizations.

The liminality of such transitory teams results from several institutional challenges including the high degree of misunderstandings that initially occur due to team members rarely having the time to translate the different ways of thinking that people bring from their professional specializations into a mutual understanding of their shared business purpose. Developing mutual understanding requires shared experiences, getting to know who you are collaborating with, not just what they do or their skills profile. In addition, conflicting functional priorities, and often a lack of clear accountability, make it difficult for such teams to remain focused on the business purpose of their collaboration.

Teams were not always organized this way. As Mortensen notes, teams in multi-divisional companies were, at one time, defined by bounded and stable team membership and common goals that interdependent work was required to meet. Cross-functional teams in such companies today are not typically defined by bounded and stable membership, and common goals are still too often related to divisional performance driven by scalable efficiency rather than a connection to the purpose of the business the team is serving.

As Brown and Hagel recently observed:

Over the last 40 years, the emergence of new digital infrastructures and a global liberalization of economic policy have increased the pace of change exponentially. Many companies that were extremely successful in earlier times of relative stability are now finding that their relationship architectures are fundamentally misaligned with the needs of their business today. As the pace of change increases, many executives focus on product and service innovations to stay afloat. However, there is a deeper and more fundamental opportunity for institutional innovation—redefining the rationale for institutions and developing new relationship architectures within and across institutions to break existing performance trade-offs and expand the realm of what is possible.

Institutional innovation requires embracing a new rationale of “scalable learning” with the goal of creating smarter institutions that can thrive in a world of exponential change.

The challenge then remains how to enable organizations to adapt to their ecosystems by enhancing access to flows of knowledge that are likely to result in learning. Leinwand and Mainardi recently observed that permanent cross-functional teams tend to fare better than transitory teams in engaging organizational ecosystems. As they note:

We’ve recently seen a more robust cross-functional construct emerge, one  with an overarching organizational structure, based on building and maintaining a distinctive capability. Members of these capabilities teams are assigned permanently to them, reporting there rather than through a functional hierarchy.

Permanent cross-functional teams provide an institutional basis for what Hagel and Brown refer to as edge businesses that develop within large-scale enterprises, noting that such companies “should resist the temptation to confront the core, and instead  focus on opportunities on the periphery or at the ‘edge’ of their businesses that can scale rapidly.” I suggest below that Dave Gray’s conception of podular organization affords an important insight regarding how the institutional innovation of edge case businesses can develop and organize. Read the rest of this entry »

On the Roots of Social Computing

November 17, 2011

I recently received an invitation from Mads Soegaard, Editor-in-Chief at to offer those who read this blog an early view of a new chapter on Social Computing in their encyclopedia. I’m a little late on this writing for you to get a pre-publication view of the chapter but I wanted to make sure and point it out for those who take topics like social computing seriously. Thomas Erickson wrote the chapter. To be candid, I didn’t really know much about Thomas until I read it. He seems like a very interesting person. Thomas’ chapter takes seriously the point of an early comment I made in a post here in 2008 on Social Software, Community, and Organization: Where Practice Meets Process, specifically my point that not enough of the influential discussion on the topic took seriously the roots of what it means to do social computing.

The distinctions involved are as old as the study of social interaction in organizations, especially the characteristics of routine work. However, we don’t need to go back to the 1950s when the distinction first emerged in the study of industrial organization to understand the significance of Ross’ point. Indeed, the early 1980s will do. Rob Kling discussed computing as social organization as early as 1982 in Marshall Yovits’ edited series on Advances In Computers. Drawing from the symbolic interactionist tradition, Rob distinguished between a line of work which, he contended, indicates what people actually do in computing work, compared to formal descriptions of that work, or what we might today refer to as business processes. Kling’s work was one precursor to the focus on computer supported collaborative work  (CSCW) in studies of group collaboration, most notably developed at Xerox PARC.

The social roots of social computing are important for influentials to keep in mind as they discuss current developments in Web 2.0 technologies, especially their use in the enterprise. The point is not a simple academic exercise of giving credit to what came before. Rather, it is to take note that the distinctions made explicit…regarding practice/process are as old as the modern, hierarchical organization and seem to survive regardless of the way communication technology is applied in it. Those who discuss tensions between social software and Enterprise 2.0, or learning management systems and eLearning 2.0, are pointing to persistent challenges in how organizations work.

Thomas’ chapter provides an excellent overview of the roots, history, and development of the concept of social computing as a concept that promises to stand the test of time regardless of the labels used to describe it, e.g. Web 2.0, Social Media, Social Business, Enterprise 2.0, etc. I recommend anyone involved in current discussions related to compound nouns like social media, social business, social “this” or “that” take a look at Thomas’ chapter as well as the encyclopedia which offers in-depth analysis of such topics.

Social Learning, Collaboration, and Team Identity

March 4, 2010

Harold Jarche recently offered a framework for social learning in the enterprise in which he draws from a range of colleagues (Jay Cross, Jane Hart, George Siemens, Charles Jennings, and Jon Husband, all members of the Internet Time Alliance) to outline how the concept of social learning relates to the large-scale changes facing organizations as they struggle to manage how people share and use knowledge.

Harold’s overall framework comes down to the following insight,

Individual learning in organizations is basically irrelevant because work is almost never done by one person. All organizational value is created by teams and networks. Furthermore, learning may be generated in teams but even this type of knowledge comes and goes. Learning really spreads through social networks. Social networks are the primary conduit for effective organizational performance…Social learning is how groups work and share knowledge to become better practitioners. Organizations should focus on enabling practitioners to produce results by supporting learning through social networks.

Indeed, Jay Cross suggests that the whole discussion needs framing in terms of collaboration, and I tend to agree. Yet, saying social learning occurs largely through collaboration means delving into the subtleties of how social networks relate to the organizing work of project teams as well as to their performance. After all, much of the work done in Enterprises involves multidisciplinary teams, often spread across departments, operating units, and locations.

One of my earlier posts posed the question Who’s on Your Team? to highlight the importance of social networking to establishing team identity and enhancing knowledge sharing across distributed, multidisciplinary teams. Its focus was on the importance of social software applications in the Enterprise to the ability of distributed project team members to recognize who is on their team at any point in time, and who isn’t. Organizational analysts refer to the challenge of establishing team identity as a boundary definition problem for teams, when members are spread across large distances whether geographic or cultural in nature.

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Experience Design and the Intelligibility of Interfaces

February 16, 2010

Created by Timo Arnall and Jack Schulze

As I noted in a post on Peter Morville’s Findability several years ago,

“Interfaces are not what they used to be. The computer-human interface is both more and less than it was a few years ago. Interfaces are not only, or even primarily, a screen anymore. Yet, screens remain important to most design efforts, even though interfaces are increasingly part of the environment itself. As John Thackara and Malcolm McCullough both recently pointed out, entire cities are developing into user interfaces as ubiquitous computing environments expand.”

Caleb, over at MobileBehavior, recently observed that mobile phones do not yet provide users with a graphic language for touch interactions. Caleb’s post points to an early visualization of a standard graphic language offered by Timo Arnall of the Touch project, which researches near field communication. Caleb makes his point by talking about the confusion that consumers experience when faced with a visual tag (v-Tag), or 2D Barcode, and does so with the following Weather Channel forecast that offers viewers an opportunity to interact with a visual tag using their mobile phones (wait until about 45 seconds into the video). The forecast fails to indicate to viewers what the v-tag does. 

The user experience team that developed the v-tag for that particular forecast must have assumed viewers would know it represented an invitation to interact. A search on the Weather Channel website fails to return any information on the use of v-tags in their media programming though.

In a previous discussion of Dan Saffer’s book, Designing Gestural Interfaces, I made a similar point about mundane gestural interfaces in public bathrooms, a setting with fairly established graphic language conventions. Yet, even such mundane gestural interfaces can pose difficulty for users. As I noted,

I remember the first time, a few years ago, when I tried to get water flowing through a faucet in a public restroom that used sensor detection. Initially, it was not obvious to me how the faucet worked, and I suspect others continue to experience the same problem based on the photo I took during a recent visit to a physician’s office.


Among other observations, it is important here to note that these examples provide clear instruction for why experience design encompasses user experience. Specifically, people only experience a user interaction if the interactive capability of an artifact is intelligible, if they recognize the artifact as an instance of that kind of thing, i.e. an invitation to interact with media or machinery. Who knows how many people noticed the Adidas logo embedded in a v-Tag on their running shorts, or shoes, and failed to see it as an invitation to a user experience?

People can’t use an interface if it is not recognizable as such or, as the Palcom team coined it, palpable to their use. Otherwise, the invitation to experience, what Dan Saffer calls the attraction affordance, fails. Consider the more telling example of the symbol at the top of this post. It represents an RFID signal environment for devices using the Near Field Communication (NFC) standard. Indeed, Timo Arnall and Jack Schulze’s recent work for the Touch project demonstrates the spatial qualities of an RFID device’s signal, the shape of its readable volume.

Dan Saffer, in Designing Gestural Interfaces, touches on the fact that we are currently missing common symbols for indicating when an interactive system “is present in a space when it would otherwise be invisible,” or when we just wouldn’t recognize it as such. Adam Greenfield’s Everyware made a similar point a half decade ago.

Posted by Larry R. Irons

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Social Media is a Compound Noun

September 4, 2009

People who discuss the importance of social media, and actually social computing in general (Enterprise 2.0 included), continue to insist that the innovations involved will become as much a part of the tacit knowledge and expertise of ordinary people as email. I think that assessment is in fact correct. However, I want to add an insight that no one yet, to my knowledge, has offered.

Social media is not a noun (media) accompanied by an adjective (social). In fact, as long as we think of it that way social media can only fail to achieve what the thought leaders who advocate its use believe it capable of doing. Social media is, in fact, a compound noun, a noun made up of two or more words. Neither term is sufficient to describe what is done by those using it unless we consider it as part of the other.

Posted by Larry R. Irons

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