What happens when 33 practitioners from 17 organizations descend upon the quiet German city of Bonn to discuss knowledge flow, data and technology? Quite a lot, as I recently had the chance to see.
The Global Delivery Initiative organized the second Working Group Meeting for DeCODE, an evidence-based system that helps development practitioners identify, prioritize, and address delivery challenges. The two-day workshop of GDI partners hosted rich discussions on diverse topics from technology platforms, to behavior change, to knowledge management and how to use machine learning techniques. While I think that everyone learned a lot, and there was a great deal of rich food for thought, two things in particular stuck with me.
Similar challenges, similar solutions. If one thing was clear, then it was this. Despite the differences in the business models of the participating organizations, all of them grapple with similar sets of knowledge management challenges, like having a plethora of documents but no clear path to effective use; needing to improve the relevance of search results; effectively using lessons learned from past projects; managing repositories in multiple languages; and many other complex tasks such as breaking sectoral/regional silos. Fortunately, this common ground is not limited to the pain-points.
Much like convergent evolution whereby different organisms independently evolve similar traits, many of the innovative solutions our partners presented were centered around similar principles: experimenting with video and other vivid, concise formats to convey knowledge; leveraging machine learning techniques; following a user-centric and agile design approach.
This fact clearly shows the value of collaborative development, and for two important reasons. First, while recognition of these problems is common across organizations, the approaches to solve them require skills that are not necessarily found in abundance in all the participating organizations. Collaboration can help fill those gaps and help develop solutions better than what one organization might develop single-handedly. Secondly, and more importantly, inclusion of data from other organizations can help improve the breadth and depth of information across individual organizations and ultimately impact development outcomes. Truly, in common pain lies common gain.
The double trouble of technology and culture. While we are attracted to technology solutions, we often see that these solutions run into a love-hate relationship with culture. Technological solutions to knowledge challenges are no different. The solutions that we discussed in Bonn were aided as much as they were impeded by technology and cultural change (read: behavior and incentives). For instance, no technology can help surface relevant knowledge from documents if the underlying documents don’t contain quality information. Ensuring quality information in these documents however entails creating the right incentives for practitioners – a task far beyond the reach of technology by itself.
Luckily, there are ways to avoid this classic ‘either-or’ trap. At our workshop, participants almost unanimously agreed that the solutions to KM challenges need to be a good balance of technology and culture. A suggested approach to achieve this was to take the ‘minimum viable product’ route. Rather than investing upfront in resource-intensive technology overhauls, our discussions emphasized the need to test smaller pilots and tweak them based on user feedback to develop solutions that truly address practitioner needs.
Personally, the most fulfilling aspect of the workshop was to learn that organizations have started to use technology, data analytics, and similar tools in a big way to improve the usage of knowledge from past projects. Although several impediments that prevent a full-fledged rollout persist – curation of quality content to avoid a ‘garbage-in-garbage-out’ scenario, developing and expanding in-house capacity to deliver on technology solutions, and most importantly helping uptake by introducing the right incentives – it is an encouraging beginning for technological solutions to use past data for future projects.