Socializing Big Data through BRPs

September 11, 2013

BTD

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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Business Exceptions Are Not Always What They Seem

April 30, 2013

factory

Common wisdom among thought leaders discussing learning in organizations notes that most of the learning that occurs happens informally, or socially. A previous Skilful Minds post, Social Flow and the Paradox of Exception Handling in ACM , asserted:

people learning at work rely on social, or informal learning, around 80% of the time. Interestingly, I noted in a former post, Social Learning and Exception Handling, that John Hagel and John Seeley Brown contend that “as much as two-thirds of headcount time in major enterprise functions like marketing, manufacturing and supply chain management is spent on exception handling.” It is not coincidence that the two numbers are aligned.

The most basic point to remember is that exceptions to formal business processes require efforts to design a scalable learning architecture that supports content co-creation needed to adapt to emergent challenges and manage the flow of that adaptation through an enterprise’s ecosystem. Whether judging an adaptation successful requires it to result in new formal learning content, i.e. content co-creation, or a new business process, i.e. organizational innovation, or both, remains an open question.

Informal, social learning is key to exception handling since both make up most of what people do in organizing work in enterprises.

Of course, for every generalization there is usually an exception. My posts on business exceptions to this point largely focus on Barely Repeatable Processes (BRP) where informal and social learning assists employees solve issues raised by the need to improvise and handle exceptions to maintain a good customer experience, or solve issues experienced by other stakeholders such as business partners, suppliers, etc.

Recently, while reading General Electric’s A Connected World blog, one case described there led me to think about informal learning and collaboration with a different twist. It caused me to reconsider exceptions and look at the way attempts to make processes better by using working knowledge learned informally also produces exceptions in some organizational contexts.

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Social Flow and the Paradox of Exception Handling in ACM

September 28, 2011
Compliments of Dave Gray’s photostream

There is nothing like an exception to the way things are done to highlight the need to increase knowledge sharing, especially if the exception is one instance of a pattern that results in bad experiences for customers. As Jay Cross recently noted, people learning at work rely on social, or informal learning, around 80% of the time. Interestingly, I noted in a former post, Social Learning and Exception Handling, that John Hagel and John Seeley Brown contend that “as much as two-thirds of headcount time in major enterprise functions like marketing, manufacturing and supply chain management is spent on exception handling.” It is not coincidence that the two numbers are aligned.

Social Learning and Exception Handling, discussed the organizational challenges involved in dealing with exceptions to business process and their relationship to the shared experience of people working together saying,

The most basic point to remember is that exceptions to formal business processes require efforts to design a scalable learning architecture that supports content co-creation needed to adapt to emergent challenges and manage the flow of that adaptation through an enterprise’s ecosystem. Whether judging an adaptation successful requires it to result in new formal learning content, i.e. content co-creation, or a new business process, i.e. organizational innovation, or both, remains an open question.

Informal, social learning is key to exception handling since both make up most of what people do in organizing work in enterprises. We know people face difficulty when drawing from shared experience, especially in distributed teams because fewer points of common reference exist. Leadership and management consultants often contend a common organizational culture pulls teams together, even though distributed teams frequently span national, regional, and global locations. However, the mere challenge of everyone on a team knowing who else is a member can prove daunting as enterprises grow.

One of the promises of social business is the capability to embed social networks into human relationships to organize business enterprise in a way that people can act together without centralized command and control. The discussions linking the capability involved with its organizational implications for group performance are far fewer. Dave Gray’s discussion of pods in The Connected Company is one notable effort in that direction. In my conception of it, the key challenge is one of organizing businesses for social flow.

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Social Flow and Collaboration in Gameful Design

June 27, 2011

Courtesy of wetwebwork photostream

In Social Flow in Gameful Design I made the point that social flow contrasts to Csikszentmihalyi’s original concept of individual, or solitary flow, in which a person’s engagement in actions is optimal when they lose a sense of time and awareness of self in an intrisincally rewarding feeling of accomplishment. Social flow implies a qualitatively different order of the flow experience, a group-level experience. To that extent, gameful designs that take social flow into consideration incorporate a different set of design principles to those involved in what most people currently refer to as gamification.

In a similar vein, Simon Wiscombe recently observed , “Gamification is inherently flawed because it focuses on rewarding players for the end-state.” He adds that designs that gamify are best when they focus on the journey rather than the outcome, especially if the aim is to evoke the voluntary, ongoing engagement of participants. I emphasize the importance of voluntary experience because if you can’t quit playing when you want to the experience is not a gameful one. Recent social psychological research supports Simon’s point.

Walker recently offered a series of relevant social psychological studies on social flow:

Flow in a social context may be a qualitatively different phenomenon than flow experienced in isolation. Classic research in social psychology has amply demonstrated that people act, think, and feel qualitatively differently within a group than by themselves…Social contexts introduce additional variables that may inhibit, facilitate, or transform flow experiences. Social contexts can be enormously complex. They range from ‘mere presence’ situations where individuals perform in the midst of passive others…, to co-active situations where people perform side-by-side but do not interact, to highly interdependent interactive situations where people must cooperate and coordinate their performances within established groups…In highly interdependent situations, people may serve as agents of flow for each other. This form of social flow is mutual and reciprocal, a form that is likely to be qualitatively different than solitary flow (my emphasis). In mere presence and some co-active social situations, a form of solitary flow is probable because the unit of performance is the individual, however when the unit of performance is a group, especially a team that must do tasks requiring interdependence and cooperation, social flow should be more likely. Social flow should be easily seen in highly cohesive teams in which there is agreement on goals, procedures, roles, and patterns of interpersonal relations and the competency of team members is uniformly high… (see original text for references, my emphasis added).

The main thing to note from Walker’s research is that it confirms Csikszentmihalyi’s point (p. 158) that flow experiences occur most frequently in work settings, yet qualifies it by noting that “social flow is more joyful than solitary flow.” Moreover,  interactive situations compared to co-active ones scored highest in social flow in Walker’s research.

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Social Learning and Exception Handling

December 9, 2010

We know that most learning in the workplace is informal. Most observers put it at around 80%. Recently, John Hagel and John Seeley Brown contended that “as much as two-thirds of headcount time in major enterprise functions like marketing, manufacturing and supply chain management is spent on exception handling.” Of course, that fact is a result of the successes of process automation over the past few decades. Yet, still, The Barely Repeatable Process (BRP) persists as an organizational challenge for business.

Earlier discussions here focused on the importance of exceptions, to business process and formal learning. I examined the implications of the Kirkpatrick Evaluation model to the use of social media in learning experience design, while addressing the challenges facing learning leaders. Leading the Business-Centered Learning Experience noted that evaluating formal learning is as much about organizational learning and change management as it is about individual learning, largely because much of the learning, and performance, that matters today occurs at the group level. Marc Rosenberg recently echoed the point in an article in Learning Solutions Magazine, The Special Sauce of Social Learning. Marc noted that social learning is largely a change management challenge for organizations.

 The most basic point to remember is that exceptions to formal business processes require efforts to design a scalable learning architecture that supports content co-creation needed to adapt to emergent challenges and manage the flow of that adaptation through an enterprise’s ecosystem. Whether judging an adaptation successful requires it to result in new formal learning content, i.e. content co-creation, or a new business process, i.e. organizational innovation, or both, remains an open question.

Echoing John Hagel, John Seeley Brown, and Lang Davison’s focus on The Big Shift, Tim Young recently noted the following about  social networking and exception handling,

When an exception happens, we have to step away from our PowerPoint, stop typing an email, or exit a meeting in order to take care of it. Routine work stops. And, our modern reliance on technology to find, aggregate, and alert us to these exceptions has made the task of managing them more burdensome than ever before. Systems that manage exceptions provide the enterprise with vast amounts of data points that have become overwhelming for employees to handle. The applications that we rely on for managing exceptions still rely on process owners to make decisions and respond to the issues. The result is a workforce that isn’t dealing with exceptions well at all. (my emphasis)

The importance of social networking to increasing the effective handling of exceptions is a major focus for those interested in social learning.

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