Tuesday, 30 January 2018

Portfolio knowledge: models of learning and knowledge sharing in project based organisations


Written by Bryan Fenech, Director - PPM Intelligence.

Note: This article is summary of a paper I delivered at a PMI Asia-Pacific Conference. The original conference paper is available from PMI here.

Introduction

A knowledge management dimension to, and perspective upon, project management has become increasingly prevalent in the project management literature in recent years. Increasingly, it has been recognised that projects, comprising “…teams of individuals from diverse organisations with different specialist knowledge … [working] together under time and budget constraints to produce a new product, process or service” (Reich, 2007), involve significant knowledge processing and are “rich with significant personal learning opportunities” (Sense, 2003). Sense (2003) presents a conception of project teams as “learning generators” where the members of different communities of practice interact to achieve a common objective. It is reasoned that team members, individually and collectively, must learn new knowledge, create new shared understandings, and transfer their knowledge to others “at the right times and for the right cost” (Reich, 2007).

A Quick Explanation of Key Knowledge Management Concepts


Positivist and Constructivist Epistemologies

Hold on: what the hell is an epistemology?

What is Epistemology?

Epistemology, or the theory of knowledge, is concerned about issues having to do with the nature, creation, dissemination and limits of knowledge. Assumptions and beliefs (worldviews) strongly influence approaches to personal and organisational learning and knowledge management.

Positivism

Positivism is just one kind of world view. It assumes that knowledge exists in the real world just like everyday objects such as houses and cars; knowledge is “out there”, residing in books, independent of thinking beings. This knowledge is a reflection of a correspondence to reality. Knowledge represents a real world that is thought of as existing, separate and independent from acts of knowing and “knowers”, and knowledge should be considered as true only if it correctly reflects the independent world. Positivism well and truly pervades the project management profession.

Constructivism

Over the last century, however, philosophers, cultural anthropologists, sociologists and knowledge management researchers have influenced our thinking to appreciate that knowledge is not absolute, but relative to cultures and contexts (Jackson & Klobas, 2007). A constructivist epistemology (or world view) sees knowledge as something that is constructed by people rather than as something that has some objective reality; knowledge and reality do not have an objective or absolute value or, at the least, we have no way of knowing this reality, as the only tools available to a “knower” are the senses. It is only through seeing, hearing, touching, smelling, and tasting that individuals build (or construct) a picture of the world.

To illustrate this, a systems development project can be seen as a process of social construction by the project team: “the knowledge embodied in information systems emerges as we proceed in the analysis and design of a business system … It is the emergence and articulation of multiple, indeterminate, sometimes unconscious, sometimes ineffable realities and the negotiated achievement of a consensus of a new, agreed reality in an explicit form, such as a new business or data model, which is amenable to computerisation.” (Jackson and Klobas, 2007). However, most contemporary design methodologies use the language of engineering, a discipline that “builds lasting structures of steel and concrete from pre-existing elements”, which may explain the high rate of failure in such projects.

Cognitive and Situated Learning Approaches


Cognitive Learning Approaches

The predominant approach to learning, underpinned by a positivist epistemology, focuses upon the cognitive dimensions of learning – i.e., the internal processes of the mind. This focus “includes those fabled items of "mental models" (Senge, 1990), single and double-loop learning and Model I and Model 2 type people (Argyris & Schon, 1978), and experiential-learning cycles (Kolb, 1984; Lewin, 1951)” (Sense, 2003). The emphasis of this approach is upon the psychological dimension of learning and it assumes that knowledge can be transferred between people independently of any particular social context, experience or practice. Learners are encouraged to view objects, events and phenomena with an “objective” mind. The meaning that is produced through thought processes is external and determined by the structure of the real world.

Situated Learning Approaches

On the other hand, informed by a constructivist epistemology, a focus on “the situated aspects of learning is concerned with the practical and social aspects of learning, assuming that most learning occurs on the job in culturally embedded ways within a community of practice … Situated learning evolves from the participation of people and the negotiated construction of their identities and common meanings within this community of practice” (Sense, 2003, p 6). Learners are encouraged to make sense of what is taught by trying to fit it with their previous experience.

Belline and Conico (2007, p 2) state that the “creation, diffusion and application of knowledge is situated and thus heavily influenced by the context of practice …the concept of doing is not only the element that characterises knowledge, but it becomes the way through which knowledge is manifested, converted and transferred”.

Communities of Practice


What is a Community of Practice?

Communities of practice have been defined as “groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise in this area by interacting on an ongoing basis” (Wenger et al, 2002, pp 4-5). Three interacting elements define a community of practice – a domain of knowledge, a community of people who care about this domain and the shared practice that they develop over time to be effective in their domain (Wenger et al, 2002, p 27). A domain sets the common ground and creates a sense of common identity for all community of practice participants. A community consists of a group of people who interact, build relationships, learn together, and in the process develop competence, a sense of belonging and mutual commitment and accountability. A practice embodies the history of the community and the knowledge it has developed over time, including historical or social resources and frameworks, ideas, tools, information, styles, language, stories, routines, and documents that the community develops, shares and maintains. Thus a community of practice represents a shared knowledge and a shared discourse that reflects a particular perspective on the world (Sense, 2003).

Project Teams and Communities of Practice

The PMBOK Guide™ defines a project as “[a] temporary endeavour undertaken to create a unique product or service”. Projects are non-routine processes and are ad hoc in nature. This combination of temporality, uniqueness (or specificity around predefined tasks and objectives) and non-routine processes contrasts a project team with a community of practice, where the emphasis is upon an ongoing domain and a shared practice. Additionally, the identities of project team members are forged externally to the project team. Garrety et al (2004, p 352) note that “projects are more clearly instrumental than communities of practice”.

On the other hand, while a project team is a very different creature to a community of practice, it is usually the case that project teams develop a common perspective on the world and negotiate shared meanings over time. It also follows that some of the social processes that lead to new knowledge in communities of practice are bound to exist in project teams, but will rarely if ever have the time dimension required to develop into a practice. This has lead Sense (2003) to posit that project teams should be considered as a type of “embryonic community of practice”.

Social and Morale Capital


The processes of learning and knowing depend heavily upon the availability of intangible forms of capital that are generated and leveraged 'in community' – in particular, social and morale capital (Dovey & Fenech, 2007). Social capital is a form of capital that is collectively owned by members of a 'network' characterized by strong relationship bonds and multiplex connections to other 'networks', and involves resources such as trust and voluntary cooperation between all those who hold a stake in the mission of the organization (Nahapiet & Ghoshal, 1998). Morale capital refers to resources such as passionate identification with, and commitment to, the purpose of the organization (Dovey & Singhota, 2005).

The most critical of these social capital resources is trust, as it underpins the capacity to leverage many of the other resources potentially available to a network either through its members or through its partner networks (connections) (Dovey & Fenech, 2007).

However, while trust is such an important resource in a knowledge economy, it is for many reasons rarely available in abundance in organizations (Putnam, 1993; 1995).

Applied Knowledge Management Problems in Project Management


Barriers to Learning within Projects


As observed earlier projects have been described as learning generators and are widely seen as the powerhouse of learning within organisations. However, the very characteristics that underpin this learning potential also create barriers to harnessing it. As Garrety et al (2004, p 353) state: “The problem for project managers is to overcome barriers to communication created by the existence of groups with quite different skills, languages, expectations and assumptions”, and they are under time and budget constraints to do so.

One of the key processes by which project teams integrate their divergent knowledge is through a process of “brokering”. Garrety (2004, pp 353-357) – citing Wenger (1991) – describes the process of “brokering” as “the use of multi-membership to transfer some element of one practice to another …it involves processes of translation, coordination, and alignment between perspectives. It requires enough legitimacy to influence the development of a practice, mobilise attention and address conflicting interests”. Such integration of knowledge between different communities requires a great deal of work in creating and maintaining social relationships. Projects need persons to act as brokers, transferring and translating knowledge and aligning and productively organising different interests and perspectives as projects move through phases of differentiation and integration.

Knowledge Sharing between Projects and from Project to Organisation


Ayas and Zeniuk (2007, p 61) ask the question: “Can projects enable or facilitate the creation and diffusion of knowledge and innovative practices beyond individuals, specific teams or projects?” Schindler & Eppler (2003, p 219) have recently reported that while “projects are especially suited to learning …our research with various project teams (in product development, controlling, consulting and financial services) over the course of three years shows that knowledge and experiences gathered in different projects are not being systematically integrated into the organisational knowledge base”. Similarly, Scarborough et al (2004, p 492) state that “despite increasingly systematic efforts to capture learning from projects and make it available to other parts of the organisation, the evidence to date suggests limited success for such initiatives (Chaston, 1998; Sahlin-Andersson, 2002) suggesting a sharp contrast between the abundant generation of learning within projects and the more limited prospects for the diffusion of such learning across the wider organisational context (Ayas & Zeniuk, 2001).”

Challenges stem from the relatively self contained, idiosyncratic and finite nature of project tasks – inevitable discontinuities occur in the flow of resources (especially personnel and information) from one project to the next (Bresnen et al, 2003). Kasvri et al (2003) observe that “ …projects are temporally limited, and the people involved and the lessons learned are dispersed when the project ends. Often people change even during a project”.

Bresnen et al (2003) argue that in addition to the problem of the fragmentation of knowledge, the ability to develop shared meaning and understandings is also undermined in a project setting. Groups are temporally, spatially and culturally differentiated in ways that militate against the diffusion of knowledge via the development of well established communities of practice. The immediacy of project objectives and the finite lifespan of project activity may act as a focus for innovative activity, but they also militate against the emergence of networks of actors who are able to construct a community based upon shared understandings. Attempts to develop informal networks for the spread of knowledge and learning inevitably cut across strong institutional, professional and contractual boundaries and demarcations.

Scarborough et al (2002, p 492) describe how the “absorptive capacity” of an organisation – i.e., the ability to recognise the value of new information, assimilate it and then apply it to commercial ends – is determined in large measure by the prior distribution of knowledge and the extent to which there is a shared common stock of knowledge, both technical and organisational.

A particularly interesting idea in this context is the concept that in a multi-project environment, which characterises most organisations where change and innovation are imperative (Fenech and Dovey, 2005), a community may develop around the “practice” of serial and ongoing participation in tightly knit project teams. The domain in this case is the “multi-project environment”, in which “most of the work of businesses is organized as projects, formalised project management is the predominant business process for managing change, and multiple projects are executed simultaneously” (Fenech and Dovey, 2005). The community comprises those people who work and thrive in this dynamic business context, regardless of their disciplinary background, including project managers, business analysts, solutions architects, test managers, deployment managers, and so on. The shared practice involves that combination of skills and knowledge required to collaboratively solve problems and autonomously make decisions framed by agreed objectives by diverse participants, under time and budgetary constraints. On this last point, Gee et al (1996, p 58) state that “in the new capitalism it is not really important what individuals know on their own, but rather what they can do with others in a collaborative way to effectively add ‘value’ to the enterprise”.

There is some support for such a conception in the literature. For example, Sense (2003, p 8) states that “a project-based or matrix organisation may have people constantly moving between and interacting frequently on different projects, and a focus on learning across all those projects may constitute a type of mobile COP” (2003, p 8). Through this Sense posits the “development of a mobile practice that can constitute learning between projects” (2003, p 9). Similarly, Ayes and Zeniuk (2007, p 71-72) note that:

It is not only the nature of single projects that supports learning but also the web of relationships that are created in organisations that manage by projects …Project-based organisations may grow into constellations of interrelated communities of practice, offering a web of mutual support for cultivating reflective practices. When projects share members, they are bound together and become embedded in the same social network (Grantovetter, 1973). The recursive interaction among projects creates social networks of mutual assistance.

Such a mechanism would need to be supported by appropriate organisational structures and power management practices. Project Portfolio Management offers some promise in this respect, providing the basis of an organisational configuration, and formal and informal practices, in which such project knowledge can become embedded. As well as that it provides a structure of legitimacy that promises to limit inappropriate management intrusions and power management practices by providing project teams with autonomy through formal project selection and the approval of charters and budgets. Consequently, this may assist to facilitate the development of social capital resources such as trust that are sensitive to power management practices.

Responsibility for Knowledge Management in Project Based Organisations


Project Managers’ Responsibilities

Reich (2007, p 13) expresses the view that it is the responsibility of project managers “to establish a climate of trust, where it is safe to make mistakes (Grant, 2006), where sharing knowledge is the norm and helping others is promoted (Adenfelt & Lagerstr├Âm, 2006). This nurturing and safe climate is essential for implementing even simple activities such as collecting accurate and meaningful lessons learned”. Project managers can implement 5 practices to build a climate for learning:
Engage the team when building the risk register
Communicate that mistakes are a natural part of the team’s growth and understanding
Reward behaviour that supports a learning climate
Practice using desired team behaviours on minor issues
Speak the truth

Similarly, Jackon & Klobas (2007) state that “if knowledge is socially constructed then managers of projects need to attend to the elements of an environment which influence the construction of the knowledge required to get things done in projects”.

Senior Management and Leadership Responsibilities

However, while there is no doubt that project managers have an important role to play, particularly with respect to learning within projects, there are obvious limits to their power. There is little that a project manager can do to address organisational factors that inhibit both project learning and organisational learning from projects. Cultural and political factors often lead to inappropriate power management practices that can destroy the nascent culture of trust established by a project manager within (or through) some project initiative, which makes it essential that organisational leaders not only support the efforts of project managers but drive the transformation and structural change necessary to ensure the success of learning and knowledge management initiatives (see Dovey & Fenech, 2007). Eskerod & Skriver (2007, p 118) note that their findings from a detailed case study “suggests that to promote knowledge transfer, top management must focus on basic assumptions embedded in the organisational culture at hand and not solely on direct knowledge transfer between project managers”.

Dovey & Fenech (2007) argue for leaders to develop “a new form of enterprise logic – one characterized by emergent structures, shared ownership, and broadly distributed ‘non-authoritarian’ power bases – through which the creativity and learning capabilities of all staff can be built and leveraged”. The key implication of their findings is the need to broaden the concept of leadership in organisations to incorporate the role of 'structural architect'. They go on to argue that:

With suitable frames of reference regarding the relationship between structure and mission accomplishment, leaders need to explore the range of organizational forms that are emerging as appropriate alternatives to the functional hierarchy. Such forms include cellular (Miles et al, 1997); federal (Handy, 1994); hypertext (Nonaka & Takeuchi, 1995); communities of practice (Wenger, 1999) and network (Lipnack et al, 1994) structures (Fenech & Dovey, 2007, p 587).

This argument is supported by a case study at Fokker Aircraft described by Ayus and Zeniuk (2007, 68-70) in which project learning was facilitated by the implementation of a project network structure – “an organic network of self managing teams …a dynamic approach to [organisational] design derived from the principles of organisational learning …constituted by teams within teams” – and changes to reward systems. Their findings support the need for organisational leaders to establish conditions of “psychological safety” and a “learning infrastructure”.

Conclusion


In this paper we have attempted to provide project managers with the benefit of practical access to concepts from knowledge management literature in order to assist their practice as project managers. For example, Garrety et al (2004, p 351) state that “[a] comunities of practice perspective can help project managers to maximise the fruitfulness of the relationships that are crucial to knowledge exchange in complex projects … [drawing] attention to the social processes that produce differentiation, as well as the processes that facilitate productive integration … [and recognising that while] it is important to specify and pursue technical goals, complex projects are also social enterprises”. It is hoped that this assistance extends to helping project managers to influence organisational leaders more effectively. We have also sought to highlight some of the areas where organisational leaders need to drive transformation in order to support learning cultures within their organisations. It is not all up to project managers.

References


Ayus, K. & Zeniuk, N. (2001) Project-based learning: building communities of reflective practitioners Management Learning32(1), 65-76
Bellin, E. & Canonico (2007, In Print) Knowing communities in project driven organisations: analysing the strategic impact of socially constructed HRM practices International Journal of Project Management
Bresnen, M, Edelman, L., Newell, S. Scarborough, H. & Swan, J. (2003) Social practices and the management of knowledge in project environments International Journal of Project Management 21, 157-166
Dovey, K. A. & Fenech, B. J. (2007, November) The role of enterprise logic in the failure of organisations to learn and transform Management Learning 38(5), 573-590
Eskerod, P. & Skriver, H.J. (2007, March) Organisational culture restraining in-house knowledge transfer between project managers – a case study Project Management Journal 38(1), 110-122
Faraj, S. & Sproull, L. (2000) Coordinating expertise in software development teams Management Science 46(12), 1554-1568
Fenech, B. & Dovey, K. (2005) Evaluating project management maturity models: an analysis of business needs 2005 PMI Global Conference – Asia Pacific, Singapore.
Garrety, K., Robertson, P.L. & Badham, R. (2004) Integrating communities of practice in technology development projects International Journal of Project Management 22, 351-358
Gee, J., Hull, G., & Lankshear, C. (1996) The new work order: behind the language of the new capitalism. Sydney: Allen & Unwin.
Jackson, P. & Klobas, J. (2007, In Print) Building knowledge in projects: A practical application of social constructivism to information systems development International Journal of Project Management
Kasvi, J.J.J., Vartiainen, M & Hailikari (2003) Managing knowledge and knowledge competencies in projects and project organisations International Journal of Project Management 21, 571-582
Reich, B. H. (2007, June) Managing knowledge and learning in IT projects: a conceptual framework and guidelines for practice Project Management Journal 38(2), 5-17
Schindler, M. & Eppler, M.J. (2003) Harvesting project knowledge: a review of project learning methods and success factors International Journal of Project Management 21, 219-228
Sense, A. (2003) Learning generators: project teams reconceptualised Project Management Journal 34(3), 4-12
Tiwana, A., Bharadwaj, A. & Sambamurhty, V. (2003) The antecedents of IS development capability: a knowledge integration perspective Proceedings of the 24th International Conference on Information Systems Seattle, USA
Yoo, Y. & Kanawattanachai, P. (2001) Developments of transactive memory systems and collective mind in virtual teams The International Journal of Organisational Analysis 9(2), 187-208
Wenger, E., McDermott, R. & Snyder, W. (2002) Communities of practice: learning, meaning and identity, Cambridge, UK: Cambridge University Press



Monday, 29 January 2018

AI, robotics and automation, IoT, block chain, big data: why portfolio management is mission critical in an age of disruption and innovation?

Written by Bryan Fenech, Director - PPM Intelligence.


Disruptive digital technologies, including IoT, AI, robotic automation, block chain, crypto currencies and big data, are impacting all industries and no organisation is immune.

Business and government leaders face increasing demand to respond effectively to the threats and opportunities presented by these developments. This response requires innovation in products, services, internal processes and systems as well as, in many cases, whole-of-organisation transformation.

The more disruption there is in the marketplace the greater the demand for change in organisations, both in terms of volume and complexity.

Finding and articulating these opportunities and threats, and formulating a well considered response, isn’t the hard part. Most leaders understand how:

  • AI can be used to tame complex organisational problems cost effectively
  • Robotics can be used to automate the supply chain to make it more secure, less prone to defects and cheaper to run
  • Product designs can be enhanced through the incorporation of internet connectivity into all of our buildings and infrastructure, automobiles, appliances and gadgets
  • Block chain can be used to digitally automate smart contracts in a way that is secure and reliable,
  • Big data can be used to identify what customers want and how much they are willing to pay for it, and
  • Information security is under increasing threat.

The possibilities are not understood just on a theoretical level. There are working examples of all manner of application, often developed by startups with limited funding, and business and government leaders know that they will get left behind if they don’t act quickly. One revealing example is how Fintech startups are hastening the arrival of open banking, one of the more significant disruptions to the industry ever.

Most organisations have a digital strategy or at the very least a long wish list of initiatives that they would like to pursue.

The greater difficulty lies in executing all of these initiatives successfully. The main challenge is that there are so many change initiatives being put forward, each of which is seen as in some way mission critical, but undertaking them all is well beyond the available human and financial resources of the organisation.

Choices need to be made regarding the prioritisation, sequencing and risk-appropriateness of change initiatives. Organisations that can marshal and deploy their people and assets most effectively to prioritise and execute multiple change initiatives simultaneously have a distinct competitive advantage and are more likely to succeed.

This requires “portfolio thinking”. Portfolio management techniques were originally purpose-developed to help investment fund managers make sense of multiple value drivers, realisable over different timeframes and subject to complex and changing assumptions, constraints, dependencies and opportunity costs, for different classes of financial instrument. Such portfolio management techniques, used by the managers of investment and superannuation funds for selecting and managing portfolios of stocks, options, futures and derivatives for decades, have been found to be highly applicable to selecting and managing portfolios of projects and programs.

Portfolio management provides evidence-based decision-making techniques for making prioritisation, sequencing and risk versus reward choices. The following examples illustrate this point:

  • Financial return models such as NPV and Payback Period can be used to identify what subset of initiatives is the most financially rewarding long term or best supports short term cashflow.
  • Multi-attribute scoring models can be used to identify which initiatives best align with the organisation’s strategy.
  • Critical Chain Method can be used to sequence the execution of initiatives optimally around resource bottlenecks.
  • Monte Carlo Simulation can be used to identify “efficient frontiers” in order to optimally balance risk versus reward.
  • Dependency analysis can reveal the true impact and significance of risks by identifying "domino effects" on downstream dependent change initiatives.
  • Benefits projections can help identify what needs to be done to ensure benefits are realised.


Leaders need to be knowledgeable of and be able to apply portfolio management techniques within a structured process. It is therefore an increasingly critical leadership competency and organisational capability in an age of disruption and innovation.

Just as an investment manager understands the language of stocks, options and futures so too 21st century business and government leaders need to understand the language of portfolio analytics, decision-making and governance in order to ensure that their digital strategies don't end up in the too hard basket or, worse still, use up a significant amount of investment without result.

Saturday, 27 January 2018

Visualising portfolio risk and performance reviews - presentation delivered at the AIPM 2017 National Conference

Written by Bryan Fenech, Director - PPM Intelligence.

Note: This article is a summary of a presentation that I gave at the recent AIPM 2017 Conference in Melbourne. A link to the full presentation is provided at the end.

Traditional approaches to portfolio risk and performance review

More often than not the implementations of portfolio management that I come across do not deliver on their promised benefits. The reason for this is almost always the same: portfolio governance boards and portfolio management offices (PMOs) do not make sufficient use of the plethora of data available to them to support decision making about portfolio selection, monitoring and controlling.





Organisations are particularly weak in the areas of portfolio performance, risk and benefits management. The most common approach followed when portfolio governance boards review their inflight portfolio is to run through a list of projects and programs with health ratings for a set of simple performance metrics along with some basic commentary. The health ratings are usually represented as traffic lights – red, amber, green. The performance metrics usually include overall, budget, schedule, resources, risk, benefits and overall health.If you are doing this you are wasting your time!

Firstly, it is duplication of the governance practices that occur at project and program Steering Committee level and adds little if any value. If something is going wrong with one of the projects or programs in the list the best that the portfolio governance board can do is authorise actions to be taken with respect to the project or program concerned. The portfolio governance board is doing little more than delegating back (with interest) what the Sponsor has escalated up to them and is already doing their best to deal with.

Secondly, and worse still, it is ineffective as it does not make it easy to identify and resolve systemic issues that impact across the portfolio, for eg resource bottlenecks in certain skill categories causing delays in multiple projects, or common risk types and estimation errors. This misses the opportunity for creating learning and facilitating evidence-based decision making that improves structural and systemic problems and inefficiencies regarding the organisation’s portfolio management capability and capacity. This situation is not a good use of anyone’s time and energy.




Portfolio data analytics

An alternative approach is to give your portfolio data to a data analyst with project management knowledge and ask them to turn it into valuable information that tells you something about portfolio performance, risk profile and benefits position as a whole, rather than project by project.




Here are a few examples of what you might ask them to provide you:
  1. What types of risk are the most common across the portfolio? Is there a particular type of resource bottleneck that presents regularly? Is vendor management a recurring problem? Is there evidence that project managers are consistently demonstrating forms of cognitive bias in their estimation, such as being overly optimistic?
  2. Are there particular “break points” in the portfolio (ie, projects or programs upon which there is a high degree of dependency by other projects and programs)? What is the health of the most significant break points? What is the combined value of 'at risk' investment dollars or benefits dependent on these break points?
  3. How does the health of tier 1 projects and programs compare with less critical projects and programs? Is there a point of increasing complexity at which portfolio performance and throughput tends to 'fall off a cliff'? Is the organisation allowing sufficient schedule and budget contingency in its tier 1 projects for these realities?
  4. What is the combined value of investment dollars or benefits by overall traffic light rating? Is the position improving over time?



The information derived can be a snapshot at a point in time or represent historical trends.


Portfolio data visualisation

However, it is critical that portfolio analytics are presented in a way that decision makers can make immediate sense of them. Data visualisation techniques can help to present an incredibly rich picture of your project portfolio. Bringing together graphical representations of portfolio information in a portfolio info-graphic facilitates immediate practical application by senior decision makers. This approach delivers vastly superior outcomes in terms of the quality of learning and decision making.





Charles Minard’s famous graphical depiction of Napoleon’s campaign against Russia helps us to think about how we might represent portfolio information graphically. The Minard diagram shows the losses suffered by Napoleon's army in the 1812–1813 period. Six variables are plotted: the size of the army, its location on a two-dimensional surface (x and y), time, direction of movement, and temperature. The line width illustrates a comparison (size of the army at points in time) while the temperature axis suggests a cause of the change in army size. This multivariate display on a two dimensional surface tells a story that can be grasped immediately while identifying the source data to build credibility.




Types of visualisation

Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. I’ve adapted his typology so that it applies to portfolio analytics:



  1. Time-series: A single variable is captured over a period of time, such variances in planned portfolio spend or benefits over a 3-year period. A line chart may be used to demonstrate the trend.
  2. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of cost/benefit or contribution to strategy (the measure) by projects in the portfolio (the category, with each sales person a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the projects.
  3. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the incidence of risk types across the portfolio, or the total portfolio spend or expected benefits by overall health indicator.
  4. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for enterprise, divisional or department portfolios for a given time period. A bar chart can show comparison of the actual versus the baseline amount.
  5. Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A histogram, a type of bar chart, may be used for this analysis. A boxplot helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.
  6. Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting project size (X) and the incidence of cost and schedule overruns (Y) for a sample of months. A scatter plot is typically used for this message.
  7. Nominal comparison: Comparing categorical subdivisions in no particular order, such as project performance by type, size or business division. A bar chart may be used for this comparison.
  8. Geographic or geospatial: Comparison of a variable across a map or layout, such as the portfolio performance by office location. A cartogram is a typical graphic used.




Using portfolio infographics

Portfolio infographics can demonstrate critical insights in an instant that are otherwise buried in the data:


  • What types of risk are the most common across the portfolio?
  • Is there a particular type of resource bottleneck that presents regularly? 
  • Is vendor management a recurring problem?
  • Is there evidence that project managers are consistently demonstrating forms of cognitive bias in their estimation, such as being overly optimistic?
  • Are there particular “break points” in the portfolio (ie, projects or programs upon which there is a high degree of dependency by other projects and programs)? What is the health of the most significant break points? What is the combined value of 'at risk' investment dollars or benefits dependent on these break points? 
  • How does the health of tier 1 projects and programs compare with less critical projects and programs?
  • Is there a point of increasing complexity at which portfolio performance and throughput tends to 'fall off a cliff'? Is the organisation allowing sufficient schedule and budget contingency in its tier 1 projects for these realities? 
  • What is the combined value of investment dollars or benefits by overall traffic light rating? Is the position improving over time? 





Benefits of portfolio intelligence

This information is far more valuable than a list of projects and programs with health indicators. It facilitates evidence-based decision making for steering the portfolio and resolving problems relating to capability and capacity, and provides the business case for action to address systemic problems. This is a more appropriate focus for portfolio governance boards.


The power of this information in the hands of senior management is significant. It can be applied to everything from which risk categories to address first, to where to focus recruitment efforts, to how to tailor project management training needs to get the most value for the organisation.

Roles

This has implications for PMOs. In particular, the role of the portfolio analyst needs to become more analytical and versed in data visualisation techniques to be able to surface key messages and trends out of the detail of portfolio data. In most cases, PMO personnel come from a delivery background, and adopting portfolio analysis and visualisation techniques and approaches requires a significant degree of upskilling.


This in turn highlights the importance of the role of independent portfolio assurance. Assurance, through health checks and other reviews, of your most important projects and programs remains essential. However, a portfolio intelligence expert can give you immediate access to the relevant analytics and visualisation skills needed to bring your portfolio information to life. Such services are a highly cost effective use of your assurance investment, resulting in improved portfolio performance and knowledge transfer.

You can get the full presentation here.