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.”

Read the rest of this entry »


Siloed Social Conversations Impede Shared Experience

June 19, 2013

screenshot-altimetersocialbusiness-2013

The Altimeter Group’s report from earlier this year, The Evolution of Social Business: Six Stages of Social Business Transformation, offers the above graphic to exemplify the way social networking develops as the social activities of businesses mature. I tend to feel skeptical about many developmental models in social business simply because markets differ, sometimes in fundamental ways, and businesses organize accordingly. However, since a previous post here summarized the currently dominant Hub and Spoke approach as falling short as a way to organize collaboration in relation to customer experience, I feel elaborating on that point is in order.

Shared experience, not just shared information, is fundamental to the social networks underlying collaboration and innovation. Many, if not most, employees don’t only need to get to know one another through reputation systems, like who people tag as possessing expertise. As Thomas Vander Wal continues to point out, comfort with one another is needed to develop a shared experience that encourages the open sharing of information.

Collaboration means getting to know that other employees possess expertise on this or that topic, but also developing comfort with one another by sharing significant symbols relating to self, family, friends, and social activities, thereby understanding one another as people. Shared experience with co-workers and customers is a key factor in innovative business practices. It is especially important to multichannel collaboration.

Shared experience is so important because, as Karl Weick so deftly noted almost twenty years ago, it provides the basis for mutual understanding or, to put it bluntly, how we understand one another when we do things together. Nancy Dixon recently offered a concise summary of this point which I recommend reading.

I’ve noted the importance of shared experience to collaboration in several posts. Michael Sampson summarized the points I’ve tried to make as aptly as anyone in his post Get to Know Your Virtual Colleagues as People – and Good Things Happen (to Important Things Like Productivity) and his perspective is much appreciated by me. He noted:

Trust between collaborators is an important factor related to collaboration effectiveness. Spending time talking to and learning about the people you work with provides the mechanism for trust to flourish – if they are trustworthy – or diminish – if they are not worthy of your trust…It makes sense that when people experience the same thing together – creating shared history and shared memories – it binds the group together in a much deeper way than merely having the same information.

So, you might say, what does this have to do with organizational silos?

The best way to begin answering the question is to look at an interesting insight offered by Mark Fidelman and Dion Hinchliffe regarding the cross-currents enterprises face in attempts to use social software to increase collaboration. In Rethinking the Customer Journey in a Social World they noted:

…it’s the mindset of the social world, where everyone knows what everyone else is doing, and perhaps even thinking, that may very well be the hardest to adapt to and instill in our corporate culture. It’s a world where those who know how to tap into global knowledge flows in social networks on the “edge” of our businesses will succeed. Thus, we need a new vocabulary for understanding not only our businesses, but how it will deeply affect the entire experience of our customers, from beginning to end. This transformation of thinking and working is required in order to access the significant benefits of truly remaking how we engage with the market.

Their thinking seems torn between insight into where the changes for business are headed and what they think likely to happen in the short-term. Dion in particular recognizes the fact that social business requires organizational transformation when, for instance, he asserts, ” social business is first and foremost a transformation involving people and the organizations they work with.” Yet, if you consider where he thinks the in-roads for social software (including social media) are for business over the next year or so, the contrast in perspective is pretty distinct. Dion says in another post that it is in the vertical space of enterprises where most of the innovation is set to occur for social software.

While general purpose social software platforms can certainly be used in all of these areas, high impact application of social media to the way we work often requires application-specific constraints on conversations and the resultant community activity (my emphasis). This means social customer care benefits from conversations organized around support, social supply chain focused on ERP transactions, and so on, along with software that supports these applied uses.

Yammer spread out over Sharepoint sites is a good example. The enterprise use-cases of social business implementation offered by Ray Wang support Dion’s assertion. Indeed, one of the recent findings by The Community Roundtable offered in their 2013 State of Community Management report is indicative. The report observes that community managers are most often “hubs” and, further, that:

Key factors, such as the amount of cross-functional interactions and size of community teams [with external or internal focus -- my point], are putting a resource strain on community managers, particularly in large organizations.

A key organizational point is worth making here because it relates directly to the burdens the hub-and-spoke model, whether  cross-functional or dandelion, places on collaboration between employees, customers, partners, and other stakeholders. Indeed, the “Tip” offered by Jeremiah Owyang of Altimeter regarding the “dandelion” hub-and spoke model is telling. He noted that,

the lines connecting the multiple hubs may be severed.  Tip: provide way for spokes to connect to each other, not just be funneled through a central group.

Just like social networks do not respect organizational boundaries, edge cases do not respect vertical (read, silo-oriented) organizational constraints on conversation. This is an important point when you consider that most of the time spent by employees involves dealing with edge cases, i.e. exceptions to core processes. I suggest that at least part of this outcome results from the fact that not enough employees in the enterprise develop shared experiences. If you agree with me, I guess we just need to think about how to make this happen. If not, then you probably need a bit more detail which, hopefully, you can spare the time for.

Read the rest of this entry »


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 »


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.

Read the rest of this entry »


Collaboration, Empathy, and Language in Global Teams

March 27, 2013
Panacousticon -- Athanasius Kircher (1650)

Panacousticon — Athanasius Kircher (1650)

The importance of empathy for design research, organizational collaboration, and language is one of my major focuses. The relationship between empathy and collaboration is a topic I’ve covered in a range of posts over the past few years. One post in particular, drawing from the Open Empathy Organization concept of Dev Patnaik’s Wired to Care, focused on how empathy improves the overall communication patterns in organizations.

Organizations, for-profit or not-for-profit, which ignore the benefits of using empathy as an organizing principle do so to their own detriment. The point is especially relevant to global companies that mandate a lingua franca.  Companies currently mandating English as their lingua franca (ELF) include Daimler AG, Kone Elevators, SAP, Siemens, Philips, Nokia, Alcatel-Lucent, Nissan, Technicolor, Rakuten, and Microsoft in Beijing, among others.

The trade-offs in deciding whether to implement ELF are pretty well known. Pressure from other global players such as suppliers, customers, partners, and competitors who increasingly use English is one. Diversification of organizational tasks across departments in different countries creates bottlenecks without a lingua franca, increasing inefficiencies. A third reason relates to making mergers and acquisitions among global companies smoother in organizational terms.

Actual research into how ELF affects collaboration within distributed teams with members from different mother toungues and national cultures is less abundant. The following discussion looks at some recent research into the way ELF actually affects distributed team members of global companies.

However, before looking at the research, a brief review of the debate about ELF is useful to put the research into a broader context. Most of the points (pro lingua france and con lingua franca) below are drawn from a debate between Maury Peipert and Karsten Jonsen of IMD.

If I had known about Jankaki Kumar when I wrote this post she would have been my point of reference for how these concerns apply to what employees in global enterprises think, feel, and do while participating across national cultures and peoples.

Read the rest of this entry »



Revisiting the Great Innovation Debate

January 2, 2013
Courtesy of Wonderfully Complex's photostream on flickr.

Courtesy of Wonderfully Complex’s photostream on flickr.

An early Skilful Minds post introduced The Great Innovation Debate, focusing on the distinctions between Tom Friedland’s conception that when it comes to innovation the world is flat, and the alternative point of view espoused by Richard Florida that the world is spiky. Meaning that the aggregation of creative people in cities, in proximity to one another, largely drives innovation and economic growth. As our previous post noted, John Hagel added an interesting vantage point on the debate by observing that, “Even though you can participate in innovation from more remote locations, if you want to develop your talent more rapidly than others, you are more likely to be able to do that in a major urban area.” In other words, the debate about innovation is largely a difference of viewpoints on the feasibility of effective collaboration across distributed people who work together to get jobs done. These collective efforts typically exist as cross-functional teams working with business partners, or customers.

The innovation debate was raised again recently when John Hagel and John Seely Brown added substantially to the questions behind it in a post titled, Friedmand vs. Florida and offered some key insights that coincide with key points from the McKinsey survey. The gist of Hagel and Brown’s position goes as follows:

It’s true that globalization has led to increased competition; however, there is also a significant opportunity for companies to access the talent gathering in different spike cities and then connect those people around the world using digital technology infrastructure so that they might leverage the skills of, and learn from, one another. Such a model does not develop overnight; to move from competitors to collaborators, participants must form long-term, trust-based relationships with one another.  When these relationships develop, then firms can connect capabilities across spikes, and ultimately, pursue opportunities for innovation and capability building across spikes.

Consider the following observations from recent research on the importance of proximity in how team members relate to one another. A recent Forrestor report, Making Collaboration Work for the 21st Century’s Distributed Workforce (registration required) noted that most information workers (including Gen Yers) prefer email, telephone conversations, and face-to-face meetings. These preferences appear to result as much from limitations in the available collaboration tools as anything else. The Forrestor recommendations are three-fold:

  1. create the sense of a “shared office” among distributed employees
  2. use tools that follow distributed employees on the go
  3. provide collaboration tools that make the work easier, i.e. are integrated into the work.

I’ll get back to the major challenge among the three outlined in the Forrestor report (creating the sense of a shared office) in a following post. First though it is important to note that the Forrestor report’s findings indicate fundamental differences between the opposing points of view in the debate over innovation by Friedland and Florida, especially as they relate to distributed employees (i.e. people who are not colocated). For example, a recent McKinsey Global Survey of 2,927 executives, Making Innovation Structures Work (registration required), offered two key insights dealing with innovation that merit attention in relation to the topic.

  1. “Companies cannot rely on a single innovation function alone to create successful outcomes, it must be integrated with the entire organization.”
  2. “The functions located near talent or target markets have more market success and meet objectives more effectively than others, though they are less likely than the functions at or near HQ to engage regularly with company leaders.”

The first conclusion relates to the McKinsey report’s overall insight that organizations are more likely to succeed with innovation efforts when those initiatives are integrated with corporate strategy as well as benefiting from the engagement and support of company leadership. It implicitly recognizes the ineffectiveness of organizing innovation efforts that occur in corporate silos, such as innovation centers or research & development labs.

On the other hand, the second conclusion recognizes the constraints faced in organizing innovation efforts among distributed employees. Creating a sense of a shared office, or workspace, is fundamental to efforts attempting to integrate innovation and corporate strategy, especially if the corporate strategy involves social business.

In my thinking, the key to Hagel and Brown’s point is that, as Gunter Sonnenfeld recently observed in a post called Relationship Economics, “relationships are the foundation of the social web, and the basis for the flat, seemingly infinite distribution plane that is the Internet.”  Rather than focus on whether the world is flat or spiky, serious attention is better paid to how enterprises organize collaboration and what limitations place and cultural context impose on that organizational effort to create innovation capabilities. How to organize distributed collaboration and manage the social interactions involved is the topic that requires discussion when these concerns are brought into focus.


Follow

Get every new post delivered to your Inbox.

Join 45 other followers