May 17, 2017
Knowing the limits of innovation that a company will embrace, support and execute is critical to the design process. Innovation that is too small won’t add value. Innovation that is too big isn’t actionable.
The design community tends to hyper-focus on the user and the technology but become fuzzy on how to customize a solution to the current business context. Understanding the user and technology is the core competency of design. However, design problems don’t stop there. Innovation needs inputs from the broader context – desirability (the user), feasibility (the technology) and viability (the business).
Designing for viability begins by asking questions like:
• Where are they in the innovation journey?
• What are the resources needed to execute the design?
• How do they make decisions?
The Business Journey Map
I believe a journey map can be used to visualize where a business is on their innovation journey. And through this work, designers can begin to understanding the project sponsor, what a a viable solution means to them, and how they frame the problem.
Analytics at Work: Smarter Decisions Better Results defines a model which is like a business journey map. It outlines stages a company goes through on their analytical innovation journey. The framework uses observable traits to create a baseline when comparing companies. Using observable traits allows us to map the innovation journey based on tangible inputs – rather than business rhetoric.
Note: In the book, the term analytics appears in a context of quantifiable data. However, I see a parallel with the human-centered design process using qualitative data for decision-making. For my purposes, it is not about analytics but rather understanding how a business evolves. Then scale the innovation to business stage.
Stage 1: Analytically Impaired
The organization lacks one or several prerequisites for serious analytical work. (senior management interest, internal skill sets, etc.) Do they have general buy-in to design? Or is this one time event? Innovation should focus on quick wins.
Stage 2: Localized Analytics
There are pockets of analytical activity within the organization, but they are not coordinated or focused on strategic targets. A few design champions are scattered around the company, but alignment is missing. Innovation should empower the loyalist.
Stage 3: Analytical Aspirations
The organization envisions a more analytical future, has established analytical capabilities, and has significant initiatives underway. Design processes, resources and projects are active. Innovation should challenge existing internal assumptions.
Stage 4: Analytical Companies
The organization has the needed human and technological resources, applies analytics regularly and realizes benefits across the business. But analytics have not become a competitive advantage. Design is functional but not memorable. Innovation should be grounded in context of market conditions.
Stage 5: Analytical Competitors
The organization routinely uses analytics as a distinctive business capability. Portrays itself both internally and externally as an analytical competitor. The company integrates design into all their touchpoints. Innovation should explore future scenarios.
Solving a design problem not only means solving for the user, but also the company. If we only focus on the user (desirable) and technology (feasible) then we create a business (viability) gap that will prevent ideas from coming to reality.