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Mapping project dialogues using IBIS – a paper preview
Work commitments have conspired to keep this post short. Well, short compared to my usual long-winded essays at any rate. Among other things, I’m currently helping get a biggish project started while also trying to finish my current writing commitments in whatever little free time I have. Fortunately, I have a ready-made topic to write about this week: my recently published paper on the use of dialogue mapping in project management. Instead of summarizing the paper, as I usually do in my paper reviews, I’ll simply present some background to the paper and describe, in brief, my rationale for writing it.
As regular readers of this blog will know, I am a fan of dialogue mapping, a conversation mapping technique pioneered by Jeff Conklin. Those unfamiliar with the technique will find a super-quick introduction here. Dialogue mapping uses a visual notation called issue based information system (IBIS) which I have described in detail in this post. IBIS was invented by Horst Rittel as a means to capture and clarify facets of wicked problems – problems that are hard to define, let alone solve. However, as I discuss in the paper, the technique also has utility in the much more mundane day-to-day business of managing projects.
In essence, IBIS provides a means to capture questions, responses to questions and arguments for and against those responses. This, coupled with the fact that it is easy to use, makes it eminently suited to capturing conversations in which issues are debated and resolved. Dialogue mapping is therefore a great way to surface options, debate them and reach a “best for group” decision in real-time. The technique thus has many applications in organizational settings. I have used it regularly in project meetings, particularly those in which critical decisions regarding design or approach are being discussed.
Early last year I used the technique to kick-start a data warehousing initiative within the organisation I work for. In the paper I use this experience as a case-study to illustrate some key aspects and features of dialogue mapping that make it useful in project discussions. For completeness I also discuss why other visual notations for decision and design rationale don’t work as well as IBIS for capturing conversations in real-time. However, the main rationale for the paper is to provide a short, self-contained introduction to the technique via a realistic case-study.
Most project managers would have had to confront questions such as “what approach should we take to solve this problem?” in situations where there is not enough information to make a sound decision. In such situations, the only recourse one has is to dialogue – to talk it over with the team, and thereby reach a shared understanding of the options available. More often than not, a consensus decision emerges from such dialogue. Such a decision would be based on the collective knowledge of the team, not just that of an individual. Dialogue mapping provides a means to get to such a collective decision.
From here to serendipity: gaining insights from project management research
Introduction
I’ve been blogging for over three years, during which I’ve written a number of reviews and summaries of research papers in project management and related areas. Research papers aren’t exactly riveting reads; at times it seems they are written to impress academics rather than inform laypeople. Nevertheless, it is useful to read papers because it can lead to insights that are directly applicable to professional practice. The main difficulty in doing this lies in finding papers that are relevant to one’s professional interests. In this post I discuss how one can go about finding the “right” papers to read. Further, as an illustration of how such random reading can lead to unexpected insights, I list some of the things I’ve learnt in the course of my rambles through project management research.
Prospecting for papers
My primary source for research papers is Google Scholar, which I browse in a somewhat haphazard fashion. I usually start a search with a couple of key phrases denoting a broad area – for example “risk analysis” and “project management”. I then browse the resulting list, short-listing titles that catch my attention. I read the abstracts of these, and make an even shorter list of those that I feel I’d like to read in full. Google Scholar has a “Related Articles” link below each result. I often follow this link for articles that I find particularly interesting. This usually yields a few more papers of interest.
Once I’ve determined the articles I want to read, I attempt to locate and download copies of these. This isn’t always straightforward: many papers cannot be accessed in full because they are copyrighted by journal publishers and are available only through paid subscriptions. However, many are available on author and university websites. Google Scholar helpfully highlights those that are available for download. Journal archives, such as JSTOR, are also good sources for copies of papers, but full access is usually available only to subscribers (check with your local university or public library system for more).
I then browse through the articles I’ve downloaded, printing out only those that that I think are worth a careful read. Yes, this choice is based on my subjective judgement, but then life’s too short waste time reading what others find interesting. That said, if one wants a more objective assessment of a paper’s worth, one can use the citation count (number of times a paper has been cited). Google Scholar displays citation count for most of the articles it displays. However, it should be noted that citation counts aren’t necessarily an indicator of quality.
In essence I look for interesting papers using keywords that describe things I’m thinking about. Fortunately I’m not doing research for a living so I can afford to read what I want, when I want – providing, of course, it doesn’t get in the way of my regular day job!
When reading papers, I usually keep a highlighter and pencil handy for making notes in the margins (very useful for when I’m writing reviews). If I’m reading a pdf document, the commenting and highlighting features of Acrobat are very useful.
Finally, it should be clear that what turns up depends very much on the keywords and phrases one uses. This choice is dictated by ones interests. My professional interests tend towards foundational and philosophical aspects of project management, so my searches and many of my reviews reflect this.
Serendipity at work
I’ve used this technique for about as long as I have been blogging. Along the way I have come across some truly exceptional papers which have influenced the way I think about and do my job. Below are some posts that were inspired by such papers:
Project management in the post-bureaucratic organisation: A critique on the use of project management as a means to direct creative and innovative work. Based on: Hodgson, D.E. , Project Work: The Legacy of Bureaucratic Control in the Post-Bureaucratic Organization , Organization, Volume 11, pages 81-100 (2004).
A memetic view of project management: Wherein project management is viewed as a collection of ideas that self-propagate. Based on: Whitty, S. J., A memetic paradigm of project management, International Journal of Project Management, Volume 23, pages 575-583 (2005).
Cox’s risk matrix theorem and its implications for project risk management: Describes some logical flaws in the way risk matrices are commonly used. Based on: Cox, L. A., What’s wrong with risk matrices?, Risk Analysis, Volume 28, pages 497-512 (2008)
The user who wasn’t there: on the role of the imagined user in project discourse: Highlights the use of imagined (as opposed to real) users to justify specific design views and/or decisions in projects. Based on: Ivory, C. and Alderman, N., The imagined user in projects: Articulating competing discourses of space and knowledge work, Ephemera, Volume 9, pages 131-148 (2009).
The myth of the lonely project: Discusses why project managers need to be aware of the history, culture, strategic imperatives and social dynamics of the organisations within which they work. Based on: Engwall, M., No project is an island: linking projects to history and context, Research Policy,Volume 32, pages 789-808 (2003).
The papers referenced above are just a small selection of the interesting ones I have stumbled on in my random rambles through Google Scholar.
…and so, to conclude
Professional project managers rarely have the time (or inclination) to read research papers related to their field. If you don’t browse research papers often, I hope this piece has convinced you to give it a try. Although the time you invest may not get you that new job or promotion, I guarantee that it will give you fresh insights into your profession by leading you from here to serendipity.
The politics of data warehousing revisited
Introduction
An enterprise IT initiative generally affects a range of stakeholders groups, each with their own take on why the project is being undertaken and what the result should look like. This diversity of views is no surprise: an organisation-wide effort affects many divisions and departments, so there are bound to be differing – even conflicting – views regarding the initiative and its expected outcome.
The existence of many irreconcilable viewpoints is one of the main symptoms of a wicked problem – a problem that is hard to define, let alone solve. Paul Culmsee has written about the inherent wickedness of projects that involve collaborative platforms such as SharePoint. In this post I discuss how another class of enterprise scale initiatives – efforts to consolidate and harmonise organizational data for analytical and reporting purposes (so-called data warehouse projects) – display characteristics of wickedness. I also briefly discuss a couple of approaches that can be used to manage this issue.
As some of my readers may not be familiar with the terms data warehouse or wicked problem, I’ll start with a short introduction to the two terms in order to set the stage for the main topic.
Data warehouse
A data warehouse is a repository of data that a business deems important for reporting and analysis. Ideally, a data warehouse integrates data from multiple sources – for example, CRM and financial systems – thereby serving as an authoritative source for management reports (often referred to as a “single point of truth”). There are at least a couple of different design philosophies for data warehouses, but I won’t go into these as they are not relevant to the discussion. What’s interesting is that most of the literature on data warehousing deals with its technical aspects – things such as data modelling and extract-transform-load processes – yet, as anyone who has been involved in an enterprise-scale data warehousing effort will tell you, the biggest challenges are political, not technical. To be fair, this was recognized a while ago – Marc Demarest wrote an article on the politics of data warehousing in 1997. However, it is worth revisiting this issue because there are techniques to handle it that weren’t widely known at the time Demarest wrote his article. I discuss these briefly later, but first let’s look at what wickedness means and its relevance to data warehouse projects.
Wicked problems
The term wicked problem was coined by Horst Rittel and Melvin Webber in a now-classic paper entitled Dilemmas in a General Theory of Planning. The paper is essentially a critique of the traditional approach to social planning, wherein decisions are made by experts who, by virtue of their specialist knowledge and training, are assumed to know best. Such an approach often doesn’t work because it ends up alienating stakeholders who are adversely affected by the “solution.” This is a symptom of social complexity – messiness and conflict arising from diverse opinions as to what the problem is and how it should be solved. Those involved in enterprise-scale IT initiatives – whether as users, managers or technical specialists – would have had first-hand experience of this social complexity.
How do we know that a problem is socially complex (or wicked)? That’s easy: In the paper, Rittel and Webber describe ten criteria for wickedness – so a problem is wicked if it satisfies some or all of the Rittel-Webber criteria. We’ll take a look at the criteria and their relevance to data warehousing next.
The wickedness of data warehouse initiatives
To support my claim about the wickedness of data warehousing initiatives, I’ll simply list the ten Rittel-Webber criteria (in their original form) along with a brief commentary on how they can crop up in data warehouse projects. Here we go:
- There is no definitive formulation of a wicked problem: Those who have worked on organisation-wide efforts at integrating data will know that the first problem is to decide “what’s in and what’s out” – that is, what data sources are considered in scope for integration. The problem arises because different business stakeholders have different views on what is important. For example, data that is critical to HR may not be a priority for the marketing function.
- Wicked problems have no stopping rule: Data warehouse initiatives are never definitively completed: there are always new data sources that need to be integrated; old ones to be turned off; business rules to be changed and so on. Any stopping rule that one might define will need to be revised as new business requirements come up and new data sources are revealed.
- Solutions to wicked problems are not true or false, but better or worse: This is simply an expression of the truism that there is no right or wrong way to build a data warehouse. There are a range of different architectures and approaches that can be chosen, each with their pros and cons (see this paper for a comparison of the two most popular approaches). The problem is that one often cannot tell beforehand which approach is going to be best for a particular situation.
- There is no immediate or ultimate test of a solution to a wicked problem: This is a statement of the fact that one cannot tell whether or not a particular implementation can completely solve the problem of data integration. As Rittel and Webber put it, “…any solution, after being implemented, will generate waves of consequences over an extended – virtually unbounded – period of time. Moreover, the next day’s consequences of the solution may yield utterly undesirable consequences…” Although these words are somewhat over-the-top, the message isn’t: for example, I have seen situations where programming errors that have remained undetected for years (yes, years) have lead to incorrect data being used in reports.
- Every solution to a wicked problem is a “one-shot” operation; because there is no opportunity to learn by trial and error, every attempt counts significantly: Because of the high costs of implementation, enterprise-scale IT initiatives tend to be one-shot affairs. Another limiting factor is that there is usually a very short window of time in which the project must be completed – as the cliché goes, “users need these reports yesterday.” Among other things, this precludes the option of learning by trial and error.
- Wicked problems do not have an enumerable (or exhaustively describable) set of potential solutions, nor is there a set of well-describable options that may be incorporated into the plan: This point may seem like it doesn’t apply to data warehousing initiatives – all data warehousing projects have a plan, right? Nevertheless, those who have worked on such projects will attest to the fact that the plan – such as it is – needs frequent revision because of surprises that crop up along the way. Iterative/incremental development approaches can address these issues to some extent, but cannot eliminate them completely. Because of time constraints, it is inevitable that solutions to unexpected roadblocks occur through improvisation rather than planning.
- Every wicked problem is essentially unique: This one is easy to see: every organisation is unique, and so are its data integration requirements. Methodologists and consultants may try to convince you otherwise, and tempt you into following generic approaches – but don’t be fooled, generic approaches will come unstuck. Your data is unique, treat it with the respect and seriousness it deserves.
- Every wicked problem can be considered to be a symptom of another problem: One of the key drivers of data warehouse projects is that organizations tend to have the same (or similar) data residing in multiple databases. As a consequence there are several different “sources of truth” for reports. These different sources of truth arise because systems used in different departments may have different definitions of the same business entity. For example, a customer might be defined in one way within the financial system but in another way in a CRM system. Seen in this light, the problem of multiple sources of truth is actually a symptom of lack of communication between different departments, what is sometimes called silo mentality.
- The existence of a discrepancy representing a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem’s resolution: As discussed in the previous point, the discrepancy in the case of a data integration problem is the lack of congruency between different data sources. There can be a range of explanations for the discrepancy. For example, one explanation may be that the data is actually different – a customer in the CRM system is not the same as the customer in the finance system; another explanation may be that the two entities are the same but their definitions differ because the systems were developed independently of each other. The data integration solution in the two cases will differ –in other words, the solution to the problem depends on which explanation is seen as the correct one.
- The planner has no right to be wrong: The data warehouse designer is in a difficult position: he or she may have to reconcile contradictory requirements. Following from the example of the previous point, whatever design decisions the designer makes regarding the definition of a customer, there will be some parties that will not be happy: if she goes with the finance definition, sales will be ticked off; if she chooses the sales definition, finance will not be happy; if she chooses to define a single common entity, neither will be pleased. Yet, her mandate is to satisfy all business requirements. This criterion is essentially an expression of the political aspect of data warehouse projects.
I find it quite amazing that criteria that were framed in the context of social planning problems can apply word-for-word to data consolidation initiatives.
Managing wickedness in data warehousing
As should be evident from the above, wicked problems can’t be solved in the usual sense of the word, but they can be managed. Although there are many techniques to manage wickedness, they all focus on the same end: to help all stakeholder groups reach a shared understanding of the problem and make a shared commitment to action. Such a shared understanding is absolutely critical because business and IT folks often have differing views on what a data warehouse ought to be.
One approach that I have used to help stakeholders get to a shared understanding in data warehouse projects is dialogue mapping, a facilitation technique that maps out the conversation between stakeholders as it occurs. Dialogue mapping uses the Issue-Based Information System (IBIS) notation which was invented by Rittel as a means to document the different facets of a wicked problem. See this post for a data warehouse related example of dialogue mapping and this one for more on the IBIS notation.
Shared understanding and commitment to action is well and good, but in the end success is measured by deliverables: the data warehouse and accompanying reports must be built. One of the challenges with a data warehouse initiative is that customers have to wait a long (very long!) time before they see any tangible benefits. Agile approaches to data warehousing offer a way to address this issue. For those interested in the nuts and bolts of agile data warehousing, I recommend Ralph Hughes’ book, which discusses how Scrum can be adapted for data warehousing projects.
Although the juxtaposition of the terms “agile” and “data warehouse” may sound oxymoronic to some, there is evidence that it works (see this case study, for example). Of course, no approach is a silver bullet; those who want to read about potential problems may want to look at this thesis for a research-based view of the pros and cons of an agile approach to data warehousing.
In the end, though, one has to keep in mind that no development technique – agile or otherwise – will succeed unless all stakeholders have a shared understanding of what the data warehouse is intended to achieve. The biggest issues are organisational rather than technical.
Conclusion
As we have seen, corporate data integration problems satisfy many – if not all – of the criteria for wickedness. The main implication of this is that data consolidation at an enterprise level is not just a difficult technical problem it is also a socially complex one. Although tackling this requires skills and techniques that are outside of the standard repertoire of technical staff and managers, these skills can be learnt. What’s more, they are critical for success: those who undertake data warehouse projects without an understanding of the conflicting agendas of stakeholder groups may fail for reasons that have nothing to do with technology.
Eight to Late 