Enterprise Collaboration Maturity Frameworks

It’s popular for organisations adopting a new technology platform e.g. MS Office 365, or management practice e.g. agile working; to think of their progress in terms of maturity. Maturity frameworks have been a popular means for organisations to be able to assess their progress with a significant change initiative. When the change is related to an industry wide adoption theme, it may also be possible to compare levels of maturity between organisations.

In this article we identify two maturity frameworks developed for use with Enterprise Social Networking (ESN) platforms and show how we have combined them to come up with an ‘end to end’ journey, from simple technology use to facilitating a fully agile and innovation driven organisation.

The maturity framework draws explicitly from two previously published Enterprise Social Maturity frameworks. The first is Siemens’ ICUP (Impact, Connectedness, User engagement, Platform adoption) Model, and the second one is from enterprise social business consultant and evangelist Simon Terry.

Siemens Maturity Model

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The Siemens model starts from ‘Platform Adoption’ where a new digital platform is being launched and where staff are initially just being encouraged to use the platform. The model then progresses to ‘User Engagement’, usually around published content.

Up to this point the traditional activity analytics such as number of users, number of posts and number of monthly active users is commonly found in the ‘out of the box’ analytics, provided by the ESN platform suppliers. These are all about measuring activity, but not about measuring the interactions between people. This is not surprising, since ESNs were originally inspired by consumer social networking sites in the first place.  The economic model for consumer social networking platforms is referral marketing through your friends and contacts. Consumer analytics are targeted at bringing qualified eyeballs to your advertising content.

The final stages in the ICUP model is the generation of business impact from the newly formed networks of collaborating staff through the stages of ‘connectedness’ and ‘impact’.

Simon Terry’s Maturity Model
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Having had first-hand experience as a CEO in a financial services organisation, and a champion of a Yammer network, Simon Terry has now become an evangelist for the business value that can be gained from enterprise social platforms.  Simon Terry Maturity Model looks beyond simple adoption to full business value creation. As Terry notes,  “Adoption is a tool of value creation. It is not the result”. The model moves through the stages of Connect, Share, Solve and finally Innovate. 

The Combined Maturity Model

Both models are excellent for what they have been designed to do, but by combining the best pieces of both models we can represent the complete journey from the very earliest technology adoption challenges, right through to enabling the agile innovating enterprise. We’ve chosen to replace the ‘Connectedness’ and ‘Impact’ stages from ICUP with Simon Terry’s maturity model, which provides more distinct stages, where higher levels of business value start to materialize.

 

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We have purposely drawn a dotted line between the ‘User Engagement’ stage and the ‘Connect’ stage. This is the point where ‘Social’ becomes less about content and more about relationships. The analytics therefore need to be different, since the economic model for Enterprise social networking is different. Enterprise value is generated through collaborating staff solving problems and creating new products and services. Activity-based analytics alone are not sufficient to support true Enterprise business value generation. This is the point that separates those organisations simply looking to drive staff to using a platform, to those looking to drive true business value, facilitated by the platform.

In future articles we will build on this maturity framework by showing how each of the SWOOP analytic widgets identify with each stage of the maturity journey; and also how our benchmarking activities are being used to assess organisations using this new combined ESN maturity framework.

Smart Collaboration = Smart Money

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Smart Collaboration’ is the title of Harvard’s Heidi Gardner’s latest book. The book builds and expands on her well cited HBR article  “When Senior Managers Won’t Collaborate” , smart-collab-1where she presents some compelling data demonstrating that collaboration does pay, big time. Her network representation comparing the networks of two lawyers, with Lawyer 2 responsible for generating much higher revenues from her larger and more diverse network, may seem quite logical. Additionally, she shows that greater peer-to-peer collaboration does indeed generate much higher revenue levels; the key measure of success for most advisory firms. But those whom have worked in Partner led advisory firms, will understand the tribal norm of ‘Eat what you Kill’, can actively work against cross-enterprise collaboration. Gardner’s research will hopefully go a long way toward convincing the leaders of advisory organisations that it is time to abandon this tradition. But in the book she acknowledges and addresses head on, the challenges ahead.

In a decade of conducting survey based Organisational Network Analyses (ONA) projects around the world and across many industry sectors, we have found that it is the partner led organisations that fall most strongly into the ‘tribal’ area (High cohesion/Low Diversity) of our Network Performance framework.

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The above graph plots a representative set of results from client projects undertaken over a decade. In our surveys we use a common question of “Who do you rely on to get your work done?”; to identify people to people relationships. We then look at the proportion of reciprocated (two-way) relationships to devise a cohesion score. The y-axis diversity score is determined by the proportion of cross-departmental activity, similar to Gardner’s ‘cross practice’ measures for consulting organisations. The bottom right region (High Cohesion/Low Diversity) is populated by advisory firms i.e. consulting/engineering etc.. Our advice to these firms mimics that of Gardner’s, to grow the diversity of their work teams, without sacrificing the existing levels of cohesion. This is easier to say than do, as you can see from the above data; diversity and cohesion are often traded off against each other, yet this doesn’t have to be the case.

So How can Partner-Led firms be Disrupted by Smart Collaboration?

In her book, Gardner uses role archetypes to characterise the different behavioural dimensions typically found in partner led organisations. Interestingly, we find a strong correspondence to our own ONA characterizations, and more recently our on-line collaboration personas.

Gardner also identifies the increasing use of collaborative software platforms by professional services firms to help ‘break down the silos’, to better facilitate ‘smart collaboration’. While we agree with the principle, the devil can be in the detail. Without a supporting collaborative culture, these platforms can be used to actually reinforce existing silos. We have seen many instances of teams creating private groups on the pretense of ‘competitive sensitivities’; sometimes warranted, but more often not.

The following chart identifies the synergies between Gardner’s archetypes overlaid onto our Personal Networking Performance framework, to provide a link to our network centric perspective. Additionally, we also overlay our online networking personas to extend the view further to the online collaboration environment within which SWOOP’s analytics operate.

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As we can see, there is a direct mapping between Gardner’s archetypes and our Networking archetypes. The ‘Seasoned Collaborator’ is Gardner’s key role supporting ‘Smart Collaboration’. Likewise, our ‘Ambassador’ role plays the key brokering role in networks, bridging the diversity/cohesion divide. The ‘Solo Specialists’, like our ‘Specialists’ have strong, cohesive, yet localised networks. The ‘Ring Master’, like our ‘Agent’ are playing an oversight role. They have diverse networks, but not the power to necessarily drive positive actions to the same degree as the ‘Seasoned Collaborators/Ambassadors’. Finally, the ‘Contributor’ / ‘Practitioner’ have both limited diversity and cohesion in their networks. They are more regularly younger or new to the organisation staff; or staff that are comfortable to ‘do their bit’, without trying to ‘push the envelope’.

By extending this framework to the online world, we are escalating the analytics from ‘snapshot’ project based analyses, to the real-time online analytics that SWOOP provides.  Online analytics can measure and monitor ‘Smart Collaboration’ in process. We have now benchmarked close to 50 organisations on a series of collaboration indices, which include the behavioural personas indicated. The correspondence is not one-to-one, but nevertheless still informative.

The ‘Engager’ maps closely to the ‘Seasoned Collaborator / Ambassador’ archetype, by identifying those participants whose online networks are both diverse and cohesive. The important ‘Catalyst’ persona instigates interactions. They are key to growing online communities, but are not always those that broker connections. Hence they are located between the ‘Agents/Ringmaster’ and ‘Ambassadors/Seasoned Collaborator’. The ‘Responder’ persona will regularly have a diverse, yet less cohesive online network. There is not a correspondence with the pro-active ‘Agent/Ringmaster’ role, as the role is more re-active, than pro-active. The ‘Broadcaster’ tends to prioritise ‘telling’ over ‘discussing’. In this sense the behaviour is similar to the solo specialist, but ‘Broadcasters’ do not have highly cohesive networks; hence their positioning toward the ‘Contributor’/’Practitioner’. Finally, the ‘Observer’ persona has minimal participation in the online platform and therefore has low or non-existing online diversity and cohesion. The ‘Observer’ is an artefact of online platforms and there is little, if any, correspondence to the ‘Contributor / Practitioner’ archetype.

So can digital ‘Smart Collaboration’ disrupt the current status quo of the big end consulting companies? Well Harvard Professor Clayton Christensen thinks so. He wrote an article on Consulting on the cusp of Disruption back in 2013, citing the clients’ drive for more transparency and also the increased availability of big data analytics and predictive analytics. More recently, this article on Big Four Firms face tsunami of threats from Digital Groups’ also explores the digital disruption potential. And of course Heidi Gardner’s ‘Smart Collaboration’ might be framed as a helpful guide to partner led advisory firms, but could also be read as a ‘if you don’t, someone else will’ warning to the incumbents.

Final Comments

In this article we aimed to draw linkages between Heidi Gardner’s recent work on ‘Smart Collaboration’ and firstly, our own organisational network analysis consulting work. We both used survey techniques to elicit our insights, though Gardner also drew from personal interviews and observations. The extension of these insights toward insights that can be drawn from online interactions is still ‘work in progress’. Unlike surveys, interviews and observation; online analytics has to create its insights through more indirect means. That said, the wealth and volume of data available online swamps the data that can be gained from traditional surveys and interviews. At SWOOP we have now collected and analysed collaboration data from more organisations in less than two years, than from a decade of consulting projects. While consulting projects are often necessarily constrained to a limited scope, the online analysis, drawing its data from the collaborative online platforms, covers the full breadth of these organisations.

We are excited by the potential for online analytics to facilitate ‘Smart Collaboration’ in real-time. Watch this space for updates on our collaboration benchmarking research.

 

 

 

 

 

 

 

Data-Driven Collaboration Part 3: Sustaining Performance through Continuous Value Delivery

In Part 1 of our series on Data-Driven Collaboration, “How Rich Data Can Improve Your Communication,” we identified how to plan for collaboration by ensuring that goals were established and aligned with our organizational strategy. We then moved on to Part 2, “Recognizing Personas and Behaviors to Improve Engagement,” to explain how you can build engagement by managing behaviors. In this, the final post in our series, co-authored by Swoop Analytics and Carpool Agency, we will identify how to sustain the momentum to ensure that value is continuously delivered as a matter of course.

Previously, we identified the importance of migrating from simple activity measures to those that signify when collaborative relationships are being formed. It is through these relationships that tangible outcomes are achieved. Therefore, it is not surprising that analytics—as applied to sustained relationship-building—plays an important role in continuous value delivery from collaboration.

For example, a CEO from one of Carpool’s clients had been using Yammer to receive questions for a regular Q&A session, but they’d grown concerned that the CEO’s infrequent posts in the group were creating an echo chamber among the same small group of contributors. Careful analysis showed that this was more perception than reality, and the group showed a great deal of variety in cross-organization conversation. As this was precisely the executive’s goal in forming the group, the team doubled down on their investment in this executive-to-company relationship.

Monitoring Maturation Using Analytics

At SWOOP, we have been benchmarking Yammer Installations from start-up to ‘normal operations’ for some time. With Yammer, the typical pattern of start-up is a bottom-up use of ‘Free’ Yammer, which for some, lasts for many years. Without exception, however, sustained usage only occurred after a formal launch and the tacit approval of senior management. We observed different patterns of start-ups from the ‘big-bang’ public launch, through to more organic, yet managed approaches. Whatever strategy is used, organizations always reach a stage of steady-state operations or, at worst, a slow decline.

CLASSIC YAMMER

For an Enterprise Social Network (ESN) like Yammer, we have found that the average engagement rate of the 35+ organizations in our benchmark set is around 29% (i.e., non-observers) with the best at around 75%. It is evident from our benchmarking that for larger organizations—for example, more than say 5,000 participants—it can be hard to achieve engagement levels above 30%. However, this doesn’t mean that staff aren’t collaborating.

We are seeing a proliferation of offerings that make up the digital office. For a small organization, Yammer may be their main collaboration tool, where team level activities take place. For larger organizations, however, Yammer may be seen as a place to explore opportunities and build capabilities, rather than as an execution space. Increasingly, tools like Slack, HipChat, and now Microsoft Teams are being used to fill this space for some teams that depend on real-time conversations as their primary mode of communication.

A Collaboration Performance Framework

As organizations mature with their use of collaboration tools, it is critical not to be caught in the ‘collaboration for collaboration sake’ cycle. As we indicated in “How Rich Data Can Improve Your Communication,” collaboration must happen with a purpose and goals in mind. The path to achieving strategic goals is rarely linear. More regularly, we need to adopt a framework of continuous improvement toward our stated goals. For many organizations, this will take the form of a ‘Plan, Do, Check, Act’ cycle of continuous improvement. However, in this age of digital disruptions and transformations, we need a framework that can also accommodate transformational, as well as incremental innovation.

At SWOOP, we have developed a collaboration performance framework drawn from Network Science.

DIVERSITY GRAPHIC

The framework balances two important dimensions for collaborative performance: diversity and cohesion. It identifies a continuous cycle of value delivery, whether it be radical or incremental. Let’s consider an innovation example, with an organizational goal of growing revenue by 200%:

Individuals may have their own ideas for how this radical target could be achieved. By ‘Exploring’ these ideas with others, we can start to get a sense of how feasible our ideas might be, but also have the opportunity to combine ideas to improve their prospects. The important ‘Engaging’ phase would see the ideas brokered between the originators and stakeholders. These stakeholders may be the key beneficiaries and/or providers of the resources needed to exploit a highly prospective idea. Finally, the ‘Exploiting’ phase requires the focus and strong cooperation of a smaller group of participants operating as a team to deliver on the idea.

The performance framework can be deployed at all levels, from enterprise-wide to individual business units, informal groups, teams, and right down to the individual. In a typical Carpool engagement, we work with smaller teams to demonstrate this cycle and then use the success stories to replicate the pattern more broadly. A current client started with a smaller community of interest of 400 people, and is now expanding the pattern to their global, 4,000-member division.

Deploying Analytics and the Performance Framework

Like any performance framework, it can’t operate without data. While the traditional outcome measures need to be present, the important predictors of collaborative success are relationship-centered measures. For example, your personal network can be assessed on its diversity by profiling the members of your network. Your personal network’s cohesiveness can be measured, firstly, by how many of your connections are connected to each other; and secondly, by how many of these connections are two-way (reciprocated). We can then add layers provided from HR systems such as gender, geography, organizational roles, age, ethnicity, etc. to provide a complete picture of diversity beyond typical dimensions.

In the example below, we show the collaboration performance of participants in a large Yammer network over a 12-month period. You can see how challenging it might be to become an ‘Engager’, maximizing both diversity and cohesion.

BUBBLE GRAPH

We profiled their personal networks for their diversity, cohesion, and size, and plotted them on the performance framework. Interestingly the data exposed that the nature of this Yammer network is a place for exploring and, for some, engaging. There is a gap, however, in the Exploiting region. This is not to say that these individuals were poor at putting projects into motion. More likely, at least in this organization, the ESN is not the usual place to collaborate as a team. If there is no easy transition from the ESN to a team environment, then we have a problem that many ESNs experience: lots of activity but a perception of few tangible results directly from the ESN. Carpool’s approach puts this data together with data from other services and sources to create a holistic picture of the results and impact of the organization’s collaboration evolution.

Continuous Monitoring

For many organizations, continuous monitoring simply means monitoring activity on digital platforms. As we indicated in “Recognizing Personas and Behaviors to Improve Engagement,” activity monitoring can be a poor predictor of performance. At SWOOP, we look at activity that establishes or strengthens a relationship. In the screenshot below, you can see measures such as the number of two-way reciprocated relationships; the degree to which relationships are forming between the formal organizational departments; and who is influential, based on the size of their network, not how frequently they contributed. We identify key player risk by looking at how polarized a network may be among a selected few leaders. Even the Activity/User measure inside groups predicts how cohesive that group may be. By providing this data in real-time, we have the best opportunity for both leaders and individuals to adapt their patterns of collaboration as they see fit.

COLLABORATION CHART

At Carpool, our engagements use a set of such dashboards to regularly check in on all the various channels and stakeholders, and make recommendations on an ongoing basis that accounts for the holistic communication picture.

Final Thoughts

In this series, we have taken you on a journey from planning for, launching, and productively operating a digital office. At the very beginning we emphasized the need to collaborate for a purpose. We then emphasized the need to ‘engage’ through relationships and adopting appropriate behavioral personas. Finally, we have explained the importance of adopting a collaboration performance framework that can facilitate continuous delivery of value.

In order to do all of this effectively, we not only need analytics, but interventions triggered by such analytics to improve the way we work. Analytics on their own don’t create change. But in the hands of skilled facilitators, analytics and rich data provide a platform for productive change. Collaboration is not simply about how to get better results for your organization, but also to get better results for yourself, by helping you to be a better collaborator.

Want More?

We hope these insights into data-driven collaboration will give you new ideas to innovate your own approach to internal communication. If you have any questions, or would like to learn how to establish, nurture, and grow deep internal communities, Carpool and SWOOP has a team who are ready to help you grow your business and drive collaboration today.

Yammer Benchmarking Insights #3 – Collaboration at the Personal Level

 In this episode we drill down to the most detailed level. That’s you, the individual collaborator.

At SWOOP we have designed behavioural personas to characterise individual collaboration patterns based on your pattern of activity.For example, if you are a Catalyst, you are good at getting responses to your posts. Catalysts are important for energizing a community and driving the engagement of others. If you are a Responder, you are good at responding to other people’s posts. Responders are important for sustaining a community and extending the discussions. An Engager is able to balance their Catalyst and Responder behaviour and is seen as the Persona to aspire to, as the Engager effectively balances what they give to others in the form of posts, replies, likes etc. and those that they receive from others. Therefore they are well placed to broker new relationships. Broadcasters tend to post without engaging in conversations. Observers are simply not very active, with less than a single activity every 2 weeks. We see Broadcasting and Observing as negative personas.

behavioural-personasWhat does an organisation’s portfolio of Personas typically look like? The results below are generated from our benchmarking results from close to 40 organisations. The lines indicate the minimum-maximum range and the blue square is the average score.

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The large range of % Observers, between less than 10% to over 70%, may reflect the large variation in maturity amongst the organisations we have benchmarked. It may not only be the case of maturity though, as it is fair to say that the smaller organisations have an easier time engaging a higher proportion of their staff with the Enterprise Social network (ESN).  We show the break-up of the active (non-observer) Personas, which shows that Catalysts lead the way with just over 40%, followed by Responders at just under 30%, Engagers just over 20% and Broadcasters at 10%. This would indicate that in general, ESNs are relying on Catalysts to continue to drive participation and then Responders to sustain it.

Personas within Groups

Given that groups are the space where most of the intense collaboration is likely to happen, we were interested in what the Persona patterns were for the leaders of the best performing groups. We used a combination of two-way connection scores and activity scores to identify the strongest groups. We then applied the same measures to the group members to identify the group leaders. In other words, a group leader is someone who has a high number of two-way connections with other group members, and meets a threshold level of overall activity.

Firstly, we plotted all members on a graph, locating them by the size of their network (y-axis) within the group and the number of 2-way connections they have in the group (x-axis). The bubble is sized by their relative levels of interactions (activity). As you can see, the group leaders are clearly identified in the top right hand corner of the graph as different coloured nodes.

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Secondly, we then plotted the top 5 leader’s Persona movements in 1 week intervals, over a 6-month period. In the example above you can see that the leaders played the Catalyst, Engager and Responder roles primarily. The size of the bubbles reflects their relative number of connections made (breadth of influence), for that week. Not all leaders were active every week. What becomes interesting is that we find some leaders have preferred Personas that are sustained over time. Leaders 1 and 4 in this case have a preference for Catalysing and Engaging. Leader 5 prefers Responding. Leaders 2 and 3 appear to be comfortable switching between Personas.

What appears to be important here is that high performing groups need leaders that can cover the spectrum of positive Personas i.e. Catalyst, Engager, Responder. While it’s fine to have leaders who have a preference for a certain behavioural Persona, it is useful to have leaders who can adapt their Persona to the situation or context at hand.

Personal Networking Performance

At SWOOP we use a fundamental network performance framework, which measures performance against the complementary dimensions of cohesion and diversity. We have indicated that individuals with a large number of two-way connections are likely to have more closed and cohesive networks. Cohesive networks are good for getting things done (executing/implementing). From an innovation perspective however, closed networks can be impervious to new ideas. The best ideas come from more open and diverse networks. In our view therefore, maximum network performance occurs by optimising diversity and cohesion. In other words, it’s good to be part of a strong cohesive network, but this should not be at the expense of maintaining a healthy suite of more diverse connections.

In the graphic below we have plotted the members of one large group on the Network Performance graph. In this case the diversity is measured by the number of different groups that an individual has participated in. The size of the bubbles reflects the size of the individual’s network (breadth of influence).

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We have labelled regions in the graph according to our Explore/Engage/Exploit model of innovation through networks. We can see that the majority of group members exist in the ‘High Diversity/Low Cohesion’ Explore region. This is consistent with what many people give for their reasons for joining a group. The ‘Engage’ region shows those members who are optimising their diversity/cohesion balance. These are the most important leaders in the group. In an innovation context, these people are best placed to broker the connections required to take a good idea into implementation. The bottom right corner is the Exploit region, which for this group is fairly vacant. This might suggest that this group would have difficulty organically deploying an innovation. They would need to take explicit steps to engage an implementation team to execute on the new products, services or practices that they initiate.

The Innovation Cycle – Create New Value for Your Organisation

We conclude this third edition of Yammer Benchmarking insights be reinforcing the role that individuals can play in creating new value for their organisations. For many organisations, the ESNs like Yammer are seen as a means for accelerating the level of innovation that is often stagnating within the formal lines of business.

As individual’s we may have a preference for a given style of working, as characterised by our Personas. Your personal networks may be large, open and diverse; or smaller, closed and cohesive; or indeed somewhere in between. It is important however to see how your collaboration behaviours contribute to the innovation performance of your organisation. Innovation is a collaborative activity, and therefore we recommend that in your groups you:

  1. Avoid lone work (Observing/Broadcasting) and look to explore new ideas and opportunities collaboratively, online (Catalysing/Engaging/Responding).
  2. Recognise that implementing good ideas needs resources, and those resources are owned by the formal lines of business. Use your network to engage with the resource holders. Make the connections. Influence on-line and off-line.
  3. When you have organisational resources behind you, it’s time to go into exploit mode. Build the cohesive focussed teams to execute/implement, avoiding distractions until the job is done.