Deloitte Report on Social SW in Business

Deloitte has released an interesting report on the ongoing effort to discover how social SW can create real value in an enterprise.

It's a helpful reminder that social SW in business requires much more than "build it and they will come", since a business's stakeholder community has neither the size nor the scope of the Internet.

And, it emphasizes the need to focus on outputs (eg, value-added) instead of inputs (eg, adoption rates).

There are several challenges discussed:

1. Employees must gain some basic familiarity with the tool. While some Internet-based knowledge will transfer, business-specific tools will have their own learning curve, even if they're based on common open source SW.

2. Employees must discover how the tool can help them do their job better. There's no recipe for this, and I suspect that existing roles & processes will have to remain largely intact during the initial adoption phase. One employee at Alcoa is quoted as saying "One day it just clicked for me. ... That's when I started seeing Traction help me do my job better."

3. There's a bit of an ontological mismatch between the formal teleological structures of a workplace (processes, roles, organizations, etc) and the unstructured (mostly) telos-free landscape of social SW. A "deer in the headlights" response is entirely appropriate. Add in employee workload and a relative low level of employee engagement, and you're looking at a daunting task to (a) find potential high-value, low-cost bridges between "as-is" and "to-be", and (b) build critical mass once promising bridges are identified. This mismatch seems to grow exponentially as the size & bureaucracy of the business grows.

None of this is Simple or Complicated; it's definitely "managing the emergence of beneficial coherence within attractors within boundaries" (Snowden).

Deloitte asserts that the need for these tools is becoming critical as work is increasingly characterized by activities with significant Complex (exploratory) aspects (page 7).

Figure 3 lists five "unique" capabilities of social SW:
1. Identify expertise
2. Facilitate cross-boundary communication & conversation
3. Preserve institutional memory
4. Harness distributed knowledge
5. Discover emerging opportunities

Unfortunately, there seems to be little, if any, awareness that these tools are unlikely to be used effectively for these purposes unless there's good social connectivity across the business. There's a "chicken-egg" problem here that folks like Dave Snowden have addressed with such activities as Social Network Stimulation. I'm not sure I see much awareness in this report of how critical this issue is.

The discussion of metrics (pp. 9-11) made me slightly uneasy (eg. "Recipe for Success"); an understanding that there's a risk that a metric will become a goal (thereby ceasing to be a metric) seems to be missing. A boundaries/attractors approach would seem to be more effective; maybe this was done for the OSIsoft example, but if so, it's not clear. It seems more of a "build it and they will come", though that may just be retrospective coherence.

Figure 10, a "Social software capability and tool heatmap", may be the most interesting part of the report. My initial reaction was that the ratings (eg, a Prediction Market is rated as Low for "Preserve institutional memory") might be a bit simplistic, but they're still interesting. I suppose you could try to envision scenarios where a rating is completely wrong if you find the table offensive.

Bottom line: the overall approach feels more Complicated than Complex; I kept thinking that a sensemaking approach (eg, construction of a Cynefin map) would be much more effective in identifying and exploring how to best use social SW in a business context. Although there are hints that the authors understand this (pp. 16-17), I would have thought that they (John Hagel and John Seely Brown) would have spent more time discussing Complex needs and approaches.

A Flat World is Noisy

Much (negative and positive) has been written about Thomas Friedman's 2005 discussion of globalization ("The World Is Flat"). A key enabler of globalization has been transportation, communications, and information technologies.

The current issue of the Sloan Management Review has an article discussing leadership in this context ("Flat World, Hard Boundaries - How To Lead Across Them", Ernst & Chrobot-Mason).

A few snap reactions (which are equally applicable to the use of social media inside an organization):

1. A flat world is noisy. Although the opportunity for amazing new "signal" emerges in a flat world, the risk of increased noise drowning it out is real. Brokers of various types used to manage this risk in the physical world. Increasingly connected and transparent individuals and organizations are almost bereft of tools to manage this risk in the virtual world.

2. Focus becomes much more important in a noisy world. At the same time, leaders are much more constrained in their ability to control & command focus. They are left with a largely complex task; in Dave Snowden's memorable phrase "managing the emergence of beneficial coherence within attractors within boundaries." At the same time, they are largely unprepared to either recognize or effectively engage complex contexts.

3. As a result, leaders must simultaneously address at two difficult and novel issues: increased noise, and managing complexity.

The six boundary-spanning practices the authors discuss are not inherently bad. I'd feel better, however, if they emphasized the need for these practices to be introduced in the context of very specific customer-driven tasks and goals. Without that focus and discipline I'm afraid the authors' advice could easily be warped into some sort of "happy-clappy" initiative where generic "do-good" activities do more to breed cynicism than success.

Misc. Sensemaking Items

• Figure 5 of this AF report has sensemaking woven all through it
• A human-intensive approach to defeating IED attacks shows promise
• Our understanding of how to weave the mindful and the mindless (i.e., unordered and ordered) is immature at best; see, for example, experiments in easing traffic-related "boundaries"
• "Self-evident computing"? ... something about "self-evident" grates, but the basic idea reflects the trend toward weaving technology and sensemaking
• Sensemaking always has a teleological aspect ... a good, if rambling reminder, from one social media commentator
• A key driver for sensemaking is the explosion of IT-based solution spaces and the associated need to constrain them enough for useful innovation to occur
• More evidence of just how utopian the dreams of singularity are ... and of the vast gulf between IT and neurological structures/processes
• As someone who has taught Statistics, I liked this discussion of the scientific method and statistical analysis. It highlights a basic fact: getting really good data is the hard part; the numbers are easy. And, despite a few exceptions, it shows that any statistical discussion of a complex system (and all biological systems are complex) is very vulnerable to mis-interpretation.
  

SW Development Archetypes

Forrester analyst Jeffrey Hammond has published a report on Software Development Archetypes. In it, he describes 3 archetypes: Solid Utility, Trusted Supplier, Partner Player. Based on his description, these seem to map to the Simple, Complicated, and Complex domains of Cynefin (using Cynefin as a taxonomy).

Since the report is behind a paywall, see slide 16 of this recent presentation by Hammond on open source software and lean development for a summary of the archetypes.

Smart Machine, Smart Human

At the risk of being simplistic, I'm starting to wonder whether the evolution of information tools might not be something like the following:

1. Dumb machine, dumb human - hammers, shovels, etc. (caveat: I'm not dissing the craftsmanship of a Michelangelo...I'm talking about information)

2. Smart machine, dumb human - monolithic systems (e.g., mainframes) with a priesthood of operators that tend to them. "human on the loop"

3. Dumb machine, smart human - distributed systems (e.g. networked PCs, pre-mobile Internet) with humans using the machine as a "speed of need" information tool in a fixed location. "human in the loop"

4. Smart machine, smart human - adaptive agent-like chunks of IT that we weave into our everyday sensemaking activities everywhere we go. "machine on the loop"

The last stage is just emerging...Apple's amazing market cap is evidence of the potential value of such a combination. However, it is a complete paradigm shift (in the strong Kuhnian sense of the word) from machine-centric to human-centric

It raises a fundamental issue: how do you enable humans to easily create, monitor, and manage automated micro-models/narratives? And, do all this within the daily flow of sensemaking?

I mentioned one possible approach a couple of years ago...it seems awfully crude in retrospect and it does not begin to address the create/monitor/manage challenge (which is the real "magic" in the ecosystems that Apple has created).

The only widely used general user IT *modeling* tool I can think of is a spreadsheet (Project is a specialist tool IMO). However, spreadsheet modeling is not done "on-the-fly", so it provides no insight into how to weave micro-modeling into sensemaking flow.

The challenge posed by a smart human, smart machine capability is daunting; the potential is incredible. The good news is that the necessary pre-conditions are largely in place, the needed tools are emerging, and we have some good frameworks for thinking about how to go about architecting this kind of IT (e.g., Klein's Data-Frame, Cynefin).

Meshing Exploration and Exploitation

Earlier this year I posted a couple of items discussing how information technology seems to cycle through complex-complicated-simple-chaotic as a new capability moves from (a) a one-of-a-kind monolithic structure to (b) a partially decoupled structure to (c) a commoditized fully decoupled structure which then (d) provides a chaotic swirl of standardized components that are then used to create a brand new capability with a monolithic structure (Techno-Apocalypse and Business Models, IT, Architecture, Cynefin). I also alluded to the possibility that the time it takes IT to traverse this cycle is rapidly dropping...which may be one reason the whole Explore-Exploit contrast is getting more attention.

Anyway, I just ran across an OSCON (July 2010) presentation by Simon Wardley entitled "Situation Normal, Everything Must Change") that covers much of the same ground...though in a far more entertaining fashion. Highly recommended (even though I'm not a big fan of the "flashing/slashing graphics" style of presentation).

Explore and Exploit - some background

Although it's been several years since this compare-contrast first hit me, I suppose I should make a note of a few of the resources that helped me see that this view/pattern is probably widespread.

1. This first came from my thinking about the Cynefin framework (as a taxonomy, in this case) in conjunction with business processes and innovation...I first saw this as Discovery and Execution. This morphed into Exploration and Execution.

When I went searching for literature with these terms, I came across a number of items, including the following:

2. John Hagel and John Seely Brown's disucussion of Push Programs and Pull Platforms.

3. "When Learning and Performance are at Odds: Confronting the Tension", Singer and Edmondson, Harvard Business School Working Paper. See especially Figure 3.

4. Various HBS working papers by Tushman, et. al. Two that I found useful were:
"Organizational Designs and Innovation Streams"
"Ambidexterity as a Dynamic Capability"

5. "Strategy and Your Stronger Hand" by Geoffrey Moore (Harvard Business Review, December 2005.

6. "Organizing for Innovation in the 21st Century" by Deborah Dougherty (Rutgers Business School September 2004) - this has a nice summary of various approaches to innovation

7. "Planning: Complex Endeavors" by Alberts and Hayes - one in a series of publications about complexity and NCW that was published by the DoD's CCRP. This publication is the follow-on to Alberts and Hayes' "Understanding Command and Control", also recommended. If you're completely unfamiliar with how complexity science relates to organizations, Czerwinski's "Coping with the Bounds" is not a bad intro.

I'm sure there are lots of other (and much earlier) writers who've discussed this...these just happen to be some of the folks that helped me see what seems to be a basic pattern. Now that I think about it, Klein's Data Frame model covers this space well (i.e., "elaborate" is Exploit, "question" and "reframe" are Explore).