By: Steven Mauss
Spending a lot of time and resources converting everything to its digital form may make you feel like you are up on technology and doing something, but it can diminish the value of the stories it can tell you.
I’ve often wondered why we spend so much time trying to convert our physical world, including our very thoughts, into data but far less time converting data into useful thoughts and actions.
I’m reminded of the old movie, “Tron” from the early 1980s. It’s about a computer programmer and video developer who is transported (against his will) inside the digital world of a mainframe computer where he interacts with various “programs” as he tries to escape. The mainframe is running a “Master Control Program,” or MCP. The MCP has become “sentient;” an early thought experiment in artificial intelligence (AI), it seems. Of course, like all good stories about AI, technology runs amok, and the humans have to figure out a way of reimposing some sort of moral compass. As plot lines go, it is kind of campy, but the story has no real purpose. The MCP wants to take over the world but there is no discernible reason for it.
While Tron is an amusing story on many levels, not the least of which is about our fears of technology and its virtually unknown potentials, it is also a story of a mankind’s fascination with datafication. In the movie, the main character, Kevin Flynn, is converted to ones and zeros and, accordingly, is able to enter a sentient digital world of some sort.
Our current efforts to convert our own realities into data and turn over decision making to algorithms is also amusing, but it is a false reality and belies the true value of human thought. It also misses the point.
Data is only useful if it tells a compelling, purposeful story, the “so what?”. The owned life experiences of every person on the planet is a compelling story about that person. Our collective experiences, when assembled into a narrative, can also be a compelling story when assembled properly and given appropriate context. Based upon those experiences we set policies and take actions to point us toward some greater good. So, too, should your collection of data. It should point you to a greater good, and any AI applied to your data should help you do so by creating a compelling, contextual story that results in a beneficial resolution of some sort.
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As we continue the thrust into digitizing and “datafying” everything, let’s throw in a few checks and balances along the way to validate whether or not the AI we are deploying will provide us with meaningful, compelling stories. Here’s an example of what I mean:
I recently visited the site “Toolbaz.com” and used their free AI story generator. In the prompt, “What do you want to write a story about?”, I typed, “A whole fleet of ships is dry-docked because maintenance services take too long. Everyone keeps telling management that they need to upgrade.” Here is the story the AI wrote back:
“A whole fleet of [naval] ships is dry-docked because maintenance services take too long. Everyone keeps telling management that they need to upgrade to better systems, but they refuse to listen. As a result, the company is losing money and customers. Finally, the [Admiral] listens to the advice of his [contract] employees and upgrades the system. The [Navy] is now back in business and making money.”
Of course, it’s an amusing exercise, but the story has no value because it lacks contextual data and is, therefore, incomplete. It (re)states the underlying problem but provides no supporting facts. There’s no “climax” to the story where a consequence unfolds and insight is gained. Accordingly, the resolution is simplistic and inadequate. The conclusions leave you wondering what, specifically, was done, and how it was accomplished. It loses the trust of the reader.
So, when someone pitches you the idea that you just need to accumulate everything you know into data, ask them to tell you (or let AI generate) a story of how, specifically, it will impact your business in a positive way. If the story lacks contextual data points that can be tied to an actionable resolution, you know what to do.