By Neil Raden
Decision-making is not, strictly speaking, a business process. Attacking the speed problem for decision-making, which is mostly a collaborative and iterative effort, requires looking at the problem as a team phenomenon. This is especially true where decision-making requires analysis of data. Numeracy, a facility for working with numbers and programs that manipulates numbers, exists at varying levels in an organization. Domain expertise similarly exists at multiple levels, and most interesting problems require contributions and input from more than one domain. Pricing, for example, is a joint exercise of marketing, sales, engineering, production, finance and overall strategy. If there are partners involved, their input is needed as well. The killers of speed are handoffs, uncertainty and lack of consensus. In today’s world, an assembly line process of incremental analysis and input cannot provide the throughput to be competitive. Team speed requires that organizations break down the barriers between functions and enable information to be re-purposed for multiple uses and users. Engineers want to make financially informed technical decisions and financial analysts want to make technically informed economic decisions.
That requires analytical software and an organizational approach that is designed for collaboration between people of different backgrounds and abilities.
All participants need to see the answer and the path to the answer in the context of their particular roles. Most analytical tools in the market cannot support this kind of problem-solving. The urgency, complexity and volume of data needed overwhelms them, but more importantly, they cannot provide the collaborative and iterative environment that is needed. Useful, interactive and shareable analytics can, with some management assistance, directly affect decision-making cycle times.
When analysis can be shared, especially through software agents called guides that allow others to view and interact with a stream of analysis, instead of a static report or spreadsheet,. time-eating meetings and conferences can be shortened or eliminated. Questions and doubts can be resolved without the latency of scheduling meetings. In fact, guides can even eliminate some of the presentation time in meetings as everyone can satisfy themselves beforehand by evaluating the analysis in context, not just pouring over results and summarizations.
Decision making is iterative. Problems or opportunities that require decisions often aren’t resolved completely, but return, often slightly reframed. Karl Popper taught that in all matters of knowledge, truth cannot be verified by testing, it can only be falsified. As a result, “science,” which we can broadly interpret to include the subject of organizational decision-making, is an evolutionary process without a distinct end point. He uses the simple model below:
PS(1) -> TT(1) -> EE(1) -> PS(2)
Popper’s premise was that ideas passed through a constant set of manipulations that yielded solutions with better fit but not necessarily final solutions. While the initial problem specification PS(1) yielded a number of Tentative Theories TT(1), Error Elimination EE(1) generates a solution, PS(2), and the process repeats. The TT and EE steps are clearly collaborative.
The overly-simplified model that is prevalent in the Business Intelligence industry is that getting better information to people will yield better decisions. Popper’s simple formulation highlights that this is inadequate – every step from problem formulation, to posing tentative theories to error elimination in assumptions and, finally, reformulated problem specifications requires sharing of information and ideas, revision and testing. One-way report writers and dashboards cannot provide this needed functionality. Alternatively, building a one-off solution to solve a single problem, typically with spreadsheets, is a recurring cost each time it comes around.