Forty-two and the Art of Storytelling
Forty-two is the answer of the Ultimate Question of Life, the Universe and Everything, “everybody knows that”. It was calculated by an enormous supercomputer named Deep Thought over a period of 7.5 million years, told by Douglas Adams, and disseminated by millions of fans of “The Hitchhiker’s Guide to the Galaxy”. The problem is that no one remembers what the ultimate question of life, the universe and everything actually was.
This is not a story about 42. This is a story about a somewhat smaller achievement of a proportionally insignificant software dashboard used by our cities and governments.
In the late 90s and early noughties, we found a lot of joy in exploring how technology and Internet could improve life, business and governance. It was a brave new world and we studied, talked and envisioned e-everything. Governments, cities and generally all public institutions were set to benefit greatly. These are all learning organizations, and the “information superhighway” was a perfect learning accelerator.
We lived through a period of optimization (the internal efficiency and effectiveness pillar of e-Government), creation of digital services for citizens and, ultimately, transformation to a fully digital governance that includes accountability. The pandemic massively sped up this process, but it is still ongoing and it is tracked by the United Nations e-Government Survey .
Organizations and public institutions had already established good knowledge management practices: we organized in expert groups, or communities of practice, bringing together subject matter experts (aka influencers), policymakers and other relevant stakeholders to deliver policy objectives and serve citizens better. Technology and the Internet made this knowledge capturing process much easier and significantly faster.
Many beautiful things happened. We learned how to upgrade our street lighting with energy-efficient LEDs, how to measure and grade energy conservation in building, and how to improve public transport. Case studies were made, stories were told, and we got to data-driven decision-making to address burning problems. Think of it as knowledge + data = power.
Technological innovation has only accelerated since then, and the brave new world got taken over by brave new companies – innovative, fast-moving problem solvers. They approach government institutions and offer out-of-the-box solutions to the problems we have. But there are already two challenges:
They may be solving less-critical problems while stealing our focus.
They may be solving a problem in a way that affects the rest of the organization in unpredictable ways, also known as the bulldozer approach, because they don’t have a holistic view of the organization.
Some solutions are nothing short of a miracle, like the introduction of the Smart City. In the Smart City, light poles talk to each other to adjust color and brightness, connected traffic lights optimize traffic flows, and parking spot sensors signal drivers to just park instead of seek parking. City employees control all this from a dashboard, albeit human intervention is rarely needed as the AI is doing an awesome job.
The data on the dashboard is incredible. We have real-time access to the location of all buses, feeds from parking spot sensors, speed cameras, costs, deposits, citizen satisfaction … It’s data overload. And this uncovers the next challenge:
- Data-driven decision-making still requires asking the right questions first and capturing the right knowledge needed to answer those questions.
Smart Cities have great stories to tell. We have answers to many questions, especially those of technical nature. Great case studies. And our communities of practice are way bigger now: cities often make data feeds publicly available on their open data e-Government portals, enabling anyone interested to become an influencer. So, the ultimate challenge is:
- How do we avoid creating great stories that communicate great answers but to questions that no one can remember? How can we first capture the critical knowledge that we need?
Well, go back to the original knowledge management practices. Instead of being observers of unfiltered incoming data, organizations first have to identify what knowledge is of critical importance, ask appropriate questions, and filter only the data that matters. This is more difficult than finding random answers in the haystack of data but results in knowledge, instead of just information.