Crumbs From Cakes or Cakes From Crumbs
Projects can’t wait for exhaustive, inductive research, analysis, and planning
Consultants have always wanted scads of well-groomed data to fuel their research and analyses. It’s a luxury that seems more affordable with the big data analytic tools available now. And with Artificial Intelligence to complement the genuine intelligence of consultants, the dream of exhaustive, inductive analyses seems more possible than ever before.
Image taken from Unsplash
All of this would be true if the study process didn’t depend on people for structure, direction, data, interpretation, and implementation plans. People are not neutral, objective, reliable, or indifferent. Clients, consultants, and other stakeholders influence our work in ways that aren’t obvious.
In our practice, what limits the depth of analysis isn’t data quality or quantity, it’s time. Our projects require inputs from teams of people, sometimes from different organizations. Each delivers information in form their organization produces it, provides it at their own pace and, intentionally or not, with a perspective of their own.
Collecting and harmonizing information often takes longer than it should. Proposed work plans cannot presuppose the client will cause delays when the pace of work is a selection criterion for consultants bidding on the work. Nor is it helpful to hurry clients through deliberations that require deep understanding and consensus between stakeholders.
This can leave the consultant with a mixed bag of data for analysis and a critical role in facilitation of client decision-making. For feasibility studies, for example, until quite late in the study process in can remain uncertain what precisely is being studied. It is not enough to know that it is a facility being acquired and renovated if it remains uncertain whether the facility will be leased or purchased, in a prime location or on cheap land, new construction or adaptive reuse, etc.
In short, achieving agreement on specific project elements, with radically different cost consequences, can be a more valuable use of the consultant’s time than proceeding with data analysis of project outcomes that the client may later disavow. It matters later for implementation too. If the people responsible for implementation of change aren’t committed to the envisaged outcome, why should they be accountable for the actual result? It only makes sense to make sure that the object under analysis is tied to a generally desired outcome.
When the study period is eaten up by the resolution of internecine differences, or when stakeholder engagement and alignment work threatens to undermine the project’s ultimate success, it’s necessary to lower the standard of data integrity and work with the remaining time to test the unified vision that has emerged. In fact it’s helpful in building consensus to depict a hypothetical outcome using somewhat scanty data so that everyone gains a quick understanding of cost consequences at an early stage of planning.
To be truly helpful, consultants must be transparent about the need to flip their analysis on its head to keep pace. What might have been laboriously inductive, from granular data to empirical answers must become highly deductive, providing informed guesses, within tolerable ranges of probability. Although this can be stressful for a process bound analyst, it liberates the various client factions, fuelling more informed discussion about the specifics of their project and its implications for their individual and organizational work.
The alternative is a trap in which nothing can be said with certainty until everything is known. This discourages stakeholder engagement and stifles discussion. Resulting delays can be fatal to time-sensitive projects, dissipating the energy needed to propel them forward, missing critical deadlines and squandering opportunity.
In very practical ways, this arises in our practice. When responding to a request for proposals, the most reliable method of analysis is offered, using the best available sources, according to a schedule and budget that provides sufficient time for discussion and deliberation. However it’s impossible to anticipate the degree of agreement and alignment within the client organization, and the time it may take to arrive at a consensus about what exactly should be studied, planned, and implemented. It’s simply not obvious, not even to the client.
A highly deductive process is obviously less empirically reliable than a highly inductive process. The ratio of hard evidence to mere surmise makes one result more compelling than the other. Yet the feasibility study conclusion is not a determinant of project success, it is only an indicator. The greatest determinant is collective passion and will of the client organization’s members and supporters. Plans don’t fail on paper, they fail in implementation. Analytic rigor should be subordinate to the priority of internal agreement and alignments; methods and sources being adapted to need.