Diversity is Essential but not Sufficient

diversity-imageDiversity is a big word in business today. We are preached to continuously about how important having diverse leadership is to improving your performance. HBR in their article on ‘Why Diverse teams are Smarter”, identify studies showing that diversity based on both ethnicity and/or gender can lead to above average returns. In our own work with networks, research has shown that individuals with more diverse personal networks are more likely to be promoted and succeed in their occupations. Although I’ve always thought that my own personal network was quite diverse, I received a wake-up call from the recent US elections. I was not aware of any of my fairly extensive US citizen network that were voting for Trump! So it does take a conscious effort to build and sustain a diverse network of connections. It’s far too easy to fall back to the comfortable relationships with those just like us.

But diversity alone is only a pre-condition to high performance. One must be able to exploit the diversity in one’s network to actually deliver the superior results that it promises. In a previous post we introduced our network performance framework, which identifies a balance between Diversity and Cohesion in networks, for maximizing performance:

swoop-diversity

In this framework we identify that high performers are those that can effectively balance their diverse connections i.e. identifying high potential opportunities, with their close connections, with whom they can collaborate to exploit those opportunities. From our project consulting experiences these people are either recognised as organisational ‘ambassadors’ or are completely invisible i.e. the quiet achievers. The fact that we find so few people in this quadrant is testimony as to how hard achieving this balance can be.

UGM Consulting explores this tension in their recent article on Innovation and the Diversity Paradox. They nominate the following attributes for those diverse networks that can successfully exploit the opportunities that they identify:

They have a sense of shared common goals and purpose;

  1. They know how to genuinely listen to each other, seeking out elaboration and novel combinations;
  2. They have high levels of mutual trust, so speaking up and disagreements can be had, risk free;
  3. They have the skills to constructively explore alternatives and agree on a direction; and
  4. There exists a strong co-operative atmosphere at both the team and enterprise levels.

For leaders this will mean actively enabling or creating such conditions. For the individual it could boil down to simply developing a diverse network that you actively consult with.  At times you may leverage these relationships by enrolling others in selected joint activities, to bring about positive change in your own areas of influence.

Top Image credit: http://www.ispt-innovationacademy.eu/innovation-research.html

Are we Getting Closer to True Knowledge Sharing Systems?

knowledge-systems

(image credit: https://mariaalbatok.wordpress.com/2015/02/10/religious-knowledge-systems/)

First generation knowledge management (KM) systems were essentially re-labelled content stores. Labelling such content as ‘knowledge’ did much to discredit the whole Knowledge Management movement of the 1990s. During this time, I commonly referred to knowledge management systems as needing to comprise both “collections and connections”, but we had forgotten about the “connections”.  This shortcoming was addressed with the advent of Enterprise Social Networking (ESN) systems like Yammer, Jive, IBM Connect and now Workplace from Facebook. So now we do have both collections and connections. But do we now have true knowledge sharing?

Who do we Rely on for Knowledge Based Support?

A common occupation for KM professionals is to try and delineate a boundary between information, that can be effectively managed in an information store, and knowledge, which is implicitly and tacitly held by individuals. Tacit knowledge, arguably, can only be shared through direct human interaction. In our Social Network Analysis (SNA) consulting work we regularly surveyed staff on who they relied on to get their work done. We stumbled on the idea of asking them to qualify their selections by choosing only one of:

  • They review and approve my work (infers a line management connection)
  • They provide information that I need (infers an information brokering connection)
  • They provide advice to help me solve difficult problems (infers a knowledge based connection)

The forced choice was key. It proved to be a great way of delineating the information brokers from the true knowledge providers and the pure line managers. When we created our ‘top 10 lists’ for each role, there was regularly very little overlap. For organisations, the critical value in these nominations is that the knowledge providers are the hardest people to replace, and therefore it is critical to know who they are. And who they are, is not always apparent to line management!

So how do staff distribute their connections needs amongst line managers, information brokers and knowledge providers? We collated the results of several organisational surveys, comprising over 35,000 nominations, using this identical question, and came up with the following:

work-done

With 50% of the nominations, the results reinforce the perception that knowledge holders are critical to any organisation.

What do Knowledge Providers Look Like?

So what is special about these peer identified knowledge providers? Are they the ‘wise owls’ of the organisation, with long experiences spanning many different areas? Are they technical specialists with deep knowledge about fairly narrow areas? We took one organisation’s results and assessed the leaders of each of the categories of Approve/review, Information and Knowledge/Advice looking for their breadth or diversity of influence. We measured this by calculating the % of connections, nominating them as an important resource, that came from outside their home business unit. Here are the results:

external-links

As we might anticipate, the inferred line management had the broadest diversity of influence. The lowest % being for the knowledge providers, suggests that it’s not the broadly experienced wise old owls, but those specialising in relatively narrow areas, where people are looking for knowledge/advice from.

Implications for Knowledge Sharing Systems

We have previously written about our Network Performance Framework, where performance is judged based on how individuals, groups, or even full organisations balance diversity and cohesion in their internal networks:

personal-networking

The above framework identifies ‘Specialists’ as those who have limited diversity but a strong following i.e. many nominations as a key resource. These appear to be the people identifying as critical knowledge providers.

The question now is to whether online systems are identifying and supporting specialists to share their knowledge? At SWOOP we have aimed to explore this question initially by using a modification of this performance framework on interactions data drawn from Microsoft Yammer installations:

performance

We measured each individual’s diversity of connections (y-axis) from their activities across multiple Yammer groups. The x-axis identifies the number of reciprocated connections an individual has i.e. stronger ties, together with the size of their personal network, identified by the size of the bubble representing them. We can see here that we have been able to identify those selected few ‘Specialists’ in the lower diversity/stronger cohesion quadrant, from their Yammer activities. These specialists all have relatively large networks of influence.

What we might infer from the above analysis is that an ESN like Yammer can identify those most prospective knowledge providers that staff are seeking out for knowledge transfer. But the bigger question is whether actual knowledge transfer can happen solely through an ESN like Yammer?

Is Having Systems that Provide Connections and Collections Enough to Ensure Effective Knowledge Sharing?

The knowledge management and social networking research is rich with studies addressing the question of how social network structure impacts on effective knowledge sharing. While an exhaustive literature review is beyond the scope of this article, for those inclined, this article on Network Structure and Knowledge Transfer: The Effects of Cohesion and Range is representative. Essentially this research suggests that ‘codified’ knowledge is best transferred through weak ties, but tacit knowledge sharing requires strong tie relationships. Codified knowledge commonly relates to stored artefacts like best practice procedural documents, lessons learned libraries, cases studies and perhaps even archived online Q&A forums. Tacit knowledge by definition cannot be codified, and therefore can only be shared through direct personal interactions.

I would contend that relationships formed solely through ESN interactions, or in fact any electronic systems like chat, email, etc. would be substantially weaker than those generated through regular face to face interactions. Complex tacit knowledge would need frequent and regular human interactions. It is unlikely that the strength of tie required, to effectively share complex knowledge, can be achieved solely through commonly available digital systems. What the ESN’s can do effectively is to help identify who you should be targeting as a knowledge sharing partner. Of course this situation is changing rapidly, as more immersive collaboration experiences are developed. But right now for codified knowledge, yes; for tacit knowledge, not yet

 

Getting “Liked”: Is Content Overrated?

We are regularly bombarded with the message that “Content is King”, quickly followed by a plethora of methods, tips and even tricks on how to make our content more attractive i.e. being “Liked” by many. Social media has introduced the “Like” button so we can more explicitly signal our appreciation of the content that we are exposed to. But how much is appreciation directed by the “content” of that message and how much is that appreciation directed by the messenger? We have some recent analytics that provides some new insights on this.

Content or Messenger?

content-image

Doubt about the true value of content was first flagged by Canadian Philosopher Marshall McLuhan, with his often quoted “the medium is the message” statement in the 1960s. In the age of social media, this has now morphed into “the messenger is the message”, with the rise to prominence of the “Influencer”. Influencers are those rare individuals that can influence the buying behaviours of many, simply through the power of their personal recommendation. Think about your own “liking” behaviour on Facebook. How often would you “like” a passive Facebook advertising page, as opposed to “liking” a posting made by a human influencer, linking back to that very same page? This is a clear example of the power of the messenger, being more important than the message itself.

 

Enterprise “Liking”

I have recently written  about how the “Like Economy” we experience in consumer social networks may not map well when social networks move inside the enterprise in the form of Enterprise Social Networks (ESN). Unlike consumer social networks, we are unlikely to see advertisements tolerated in the ESN. But Enterprises often do want to send messages to “all staff”, particularly for major change initiatives they want staff to “buy into”. Regularly, corporate communications staff are keen to look at statistics on how often the message is read and even ‘liked’. But is this a true reflection of engagement with a message?

Our benchmarking of ESNs  has identified that “Likes” make up well over 50% of all activities undertaken on ESNs. In the absence of carefully crafted advertising sites, just what is driving our “liking” behaviour in the Enterprise? We decided to explored this by not looking at every message posted (for privacy reasons Swoop does not access message content), but by looking at patterns of who “Likes” were directed at. We aggregated the “Likes” from 3 organisations, from our benchmarking partners, for individuals who had posted more than 500  “Likes” over a 12 month period. Collectively, there were over 4,000 individuals that met the criteria. We then categorised their “Likes” according to:

“Like” Characteristic Interpretation
One-off (‘Like’ recipient was a once only occurrence) Attraction is largely based on the content of the message alone.
Repeat Recipient (‘Like’ recipient was a repeat recipient from this individual) Recipients are potentially ‘influencers’, so the motivation may come from the person, more so than the message content.
Reciprocated (‘Like’ recipient has also been a ‘Like’ provider for this individual) Recipients have a ‘relationship’ with the ‘Liker’, which drives this behaviour


‘Like’ Analysis Results

The results of our analysis is shown below:

like-analysis

The results show clearly that in the Enterprise context, the driver for ‘liking’ behaviour is the relationship. The data suggests that you are nearly 3 times as likely to attract a ‘like’ to your message from someone, if you had previously ‘liked’ a posting of theirs.

So what is the implications for the Enterprise?

If indeed an Enterprise is relying on counting ‘likes’ as a measure of staff engagement, one needs to encourage the formation of relationships through reciprocated actions as a priority, over spending time ‘crafting the perfect message’, or even on relying on influencers to build engagement. Specifically, one could:

  • Acknowledge a “Like”, in particular, if you have never responded to this person before.
  • Craft your important messages as a means to start a conversation, more so than a statement of opinion. Explicitly frame your statement as a question or explicitly ask for feedback.
  • Start to think about ‘engagement’ as more than a ‘read’ or a ‘like’ and more from a relationship perspective. How deep and broadly is your issue being discussed?
  • When you read advice from social media experts on “how to generate more ‘Likes’ for you content”, replace this with “how to generate more ‘relationships’ using your content”.

As I am writing this post I’m painfully reminded of the need to ‘eat your own dog food’. So I’m making a commitment that if you respond or ‘like’ this article, I will at least try to respond in kind!

likeimage

 

How do these results map with your own experiences?

What can we Learn from Artificial Intelligence?

This might seem strange, suggesting that a science dedicated to learning from how we humans operate, could actually return the favour by teaching us about ourselves? As strange as this may sound, this is precisely what I am suggesting.

Having spent a good deal of my early career in the “first wave of AI” I had developed a healthy scepticism of many of the capability claims for AI. From the decade or more I spent as an AI researcher and developer I had come to the conclusion that AI worked best when the domains of endeavour were contained within discrete and well bounded ‘solution spaces’. In other words, despite the sophistication of mathematical techniques developed for dealing with uncertainty, AI was simply not that good in the “grey” areas.

AI’s Second Wave

alphago

The “second wave of AI” received a big boost when Google company Deep Mind managed to up the ante on IBM’s chess playing Deep Blue  by defeating the world Go champion Lee Sedol. According to Founder and CEO of Deep Mind Demis Hassabisis,  the success of their program AlphaGo could be attributed to the deeper learning capabilities built into the program, as opposed to Deep Blue’s largely brute force searching approach. Hassabisis emphasizes the ‘greyness’ in the game of Go, as compared to Chess. For those familiar with this ancient Chinese game, unlike chess, it has almost a spiritual dimension. I can vividly recall a research colleague of mine, who happened to be a Go master, teaching a novice colleague the game in a lunchtime session, and chastising him for what he called a “disrespectful move”. So AplhaGo’s success is indeed a leap forward for AI in conquering “grey”.

So what is this “deep learning” all about? You can certainly get tied up in a lot of academic rhetoric if you Google this, but for me it’s simply about learning from examples. The two critical requirements are the availability of lots of examples to learn from, and the development of what we call an “evaluation function”, i.e. something that can assess and rate an action we are considering on taking. The ‘secret sauce’ in AlphaGo is definitely the evaluation function. It has to be sophisticated enough be able to look many moves ahead and assess many competitive scenarios before evaluating its own next move. But this evaluation function, which takes the form of a neural network, has the benefit of being trained on thousands of examples drawn from online Go gaming sites, where the final result is known.

Deep Learning in Business

books

We can see many similarities to this context in business. For example, the law profession is founded on precedents, where there are libraries of cases available, for which the final result is known.  Our business schools regularly educate their students by working through case studies and connecting them to the underlying theories. Business improvement programs are founded on prior experience or business cases from which to learn. AI researchers have taken a lead from this and built machine learning techniques into their algorithms. An early technique that we had some success with is called “Case Based Reasoning”. Using this approach, it wasn’t necessary to articulate all the possible solution paths, which in most business scenarios, is infeasible.  All we needed to have was sufficient prior example cases to search through, to provide the cases that most matched the current context, leaving the human user to fill any gaps.

The Student Becomes the Teacher

Now back to my question; what can AI now teach us about ourselves? Perhaps the most vivid learnings are contained in the reflections of the Go champions that AlphaGo had defeated. The common theme was that AlphaGo was making many unconventional moves, that only appeared sensible in hindsight. Lee Sedol has stated his personal learning from his 4-1 defeat by AlphaGo in these comments: “My thoughts have become more flexible after the game with AlphaGo, I have a lot of ideas, so I expect good results” and “I decided to more accurately predict the next move instead of depending on my intuition”. So the teacher has now become the student!

It is common for us as human beings to become subjects of unconscious bias. We see what is being promoted as a “best practice”, perhaps reinforced by a selected few of our own personal experiences, and are then willing to swear by it as the “right” thing to do. We forget that there may be hundreds or even thousands of contrary cases that could prove us wrong, but we stubbornly stick to our original theses. Computers don’t suffer from these very human traits. What’s more they have the patience to trawl through thousands of cases to fine tune their learnings. So in summary, what can we learn from AI?

  • Remember that a handful of cases is not a justification for developing hard and fast rules;
  • Before you discount a ‘left field’ suggestion, try to understand the experience base that it is coming from. Do they have experiences and insights that are beyond those of your own close network?
  • Don’t be afraid to “push the envelope” on your own decision making, but be sure to treat each result, good or bad, as contributing to your own growing expertise; and
  • Push yourself to work in increasingly greyer areas. Despite the success of AlphaGo, it is still a game, with artificial rules and boundaries. Humans are still better at doing the grey stuff!

 

 

 

 

We’ve Disrupted the Formal Organisation: But what does it look like now?

Digital disruption, Holocracies, Wirearchies are attacking the formal hierarchy as we had come to know it. While we might accept that the formal hierarchy is becoming less reflective of how work is getting done, it still reflects how senior executives are designing for work to be done. For most organisations, senior executives still agonise over appropriate formal hierarchical structures. And the published organisational chart is usually the first port of call for those wanting to understand the inner workings of an organisation. Is it distributed or centralised?; Sales driven or product driven? Operations, Technology centric or Financial centric?

If the formal organisation chart were to truly disappear, what could we replace it with? Where would the external stakeholders go to understand how the Holocracy, Wirearchy or Networked Organisation is operating? Where are the core capabilities in such environments? What about the disconnected workgroups? 

The good news is that formal methods for mapping informal organisational structure have been around for some time. Social Network Analysis (SNA) has provided us with a means for mapping the connections between individuals based on their relationships. With the advent of informal organisational groups, be they part of an Organisational Social Networking platform like Yammer or Jive, an email group or a team site in Slack or Skype, there is a need to understand how these informally created entities are connected to each other. Without this facility it can be hard to see the ‘big picture’ of what may be really happening, leaving the organisational executive flying blind. 

One of the easiest methods for creating an organisational wide map is to use a simple ‘shared membership’ approach. Commonly called ‘affinity mapping’, it is the same technique that has been used to uncover board of director interlocks, which have provided insights into largely invisible connections between publicly listed companies. It also happens to be the way that Amazon promotes new books to you, by inferring that you have an affinity relationship with those that have read the same books. 

Here is an example map we have created using an organisation’s Yammer group membership (group names have been changed to protect privacy): 

At the start of this clip we can see that all the groups are formed into one large cluster, as invariably most groups are inter-connected to some degree with other groups. But you will see as we increase the relationship ‘strength’ filter to only include overlapping memberships of a certain size, the informal group structure starts to materialise in front of our eyes. When taken to the extreme, we are left with the two groups with the greatest level of overlap, being Enterprise Communications and Customer Delivery. The number of common members is shown next to the strength filter. As we move the strength filter back from this point we gradually see other connected groups become exposed. We see the regional stores cluster emerge, suggesting perhaps some common regional issues. We also see a number of non-work groups emerge, interestingly connected to a sponsored diversity group, with all being strongly connected to the enterprise communications hub. This is good news, as these groups are doing their job of connecting staff who would normally not be connected in other ways.  

By using this simple relationship strength filter, we can start to explore the emerging structures formed from the voluntary, ‘bottom up’ actions of mainstream staff. The highly connected groups could be seen larger nodes representing the core interest/capability areas that are developing. The enterprise leaders that ‘own’ the formal organisation chart can now ask questions like ‘how well is our informal structure reinforcing our formal structures, or not?’; ‘Are there key capability areas that are not developing and may need more nurturing?’; ‘Are we encouraging a diversity of interests in our staff and if so, how are they helping to reinforce our mainstream businesses?”. 

We regularly see analytics provided for activity levels inside groups, but rarely between them. The power for the enterprise now comes from being able to overlay the formal and the informal, as the formal hierarchy starts to give way to the more adaptive and flexible informal structures, being increasingly exposed by Enterprise Social Networking platforms and the like.