In the course of their work, OD practitioners and consultants will be engaged by business leaders to solve problems. These leaders may come to the table with facts, their perceptions, data from internal sources, and, inevitably, their own prejudices about how a business, and the people driving the business, should work. In what, then, will the practitioner’s solutions be grounded?
Solutions to the organizational opportunities that arise from people must be found within the people themselves. No solution can be successful or sustainable if it is not grounded in the needs, behaviors, and circumstances that form the foundation of the work environment. At all costs, the practitioner must avoid using stereotypes or depersonalized constructions of the users for whom the solution is being designed. Shortcuts like mere numbers or market segmentation data won’t allow the practitioner to get to the heart of the problem to be solved.
My experience in problem solving has led me to talking to people, in interviews and round tables, to help me understand exactly what they are thinking, and most importantly, doing. By talking to people, and observing how they work and what they themselves experience, I have been able to experience their challenges through their eyes. Developing empathy for end users leads to a deeper understanding of how and why they work the way they do. Experiencing work as others do opens the door to genuinely insightful problem-solving.
This doesn’t, however, need to mean that there is no room for hard data in the problem-solving process. I have used “big data” in the form of function-wide learning reports, to help understand where learning gaps exist that I can explore through interviews and observations. Bringing together insights based on empathy and data analytics lays the foundation for sustainable solutions that meet the needs of all stakeholders.
Mark, great post. I find that going to where the User is working, be it the back of the restaurant or the factory floor, can lead to a multitude of opportunities that may be missed by the big data.
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