In the past 5 years, something interesting and different happened in the Bitcoin universe. The mainstream media and large chunks of the population, who were not traditionally part of the early adopter subset, took notice. When the price shot up north of $19K in mid-December, millions of people who had historically been skeptical about cryptocurrency, suddenly became crypto-traders and fashionably knowledgeable about Satoshis, Ethereum and blockchains – blockchain was the way of the future after all.
Every company had to have it. Consequently, self-styled crypto-experts were in high demand given. It seemed that if you could explain the difference between proof-of-work and proof-of-stake and name at least 5 different TLAs you could get a lucrative job at a corporation as their resident crypto-wizard. Today, almost all of these blockchain initiatives and crypto-wizards, have faded if not disappeared all-together and companies are left with a bit of novel tech that no one really understands well enough to leverage towards economic gain for the enterprise.
Similarly, something peculiar happened in the strategic planning cycles of most major corporations – the word “blockchain” sprouted up everywhere. On the list of strategic initiatives, it seems everything from insurance underwriting to inventory management and even human resources could be improved by adding the word “blockchain” to key projects. Strategic planners were including “something-something blockchain” without clearly defining what it was, why it was needed, or who would benefit from its use.
We see the same thing happen time and time again with Data Science and AI. Well meaning, intelligent executives with strong business sense read about it, recognize the transformative potential and wisely seek to bring it into their organizations with the best hopes of realizing that potential. Unfortunately, just like blockchain, Data Science and AI are not some universal spice that can just be sprinkled upon the enterprise and then magically the whole thing improves.
Data Science and AI are much more like Tinkerbell’s (from Peter Pan) Fairy Dust. For the analogy to work, you must remember the most important part of Fairy Dust – the Happy Thoughts. Pixie dust is basically the same from fairy to fairy they just exist with different names or look a bit different, but the basic rules and applications are more or less the same:
- Obtain a fairy
- He/she needs to flap their wings or wave their wand and sprinkle the stuff
- The child thinks their “happy thought”
- Boom! “You’re flying!”
But there’s a subtle catch – the Happy Thought. The rest of the process is simple, interoperable, and substitutable, but the Happy Thought is not. The Fairy Dust can make anything fly, but that flying depends on capturing the right intrinsic idea from the child – their Happy Thought.
That’s what most companies get wrong about Data Science and AI.
At Analytics2Go, we think of your Knowledge Worker as the child who wants to fly and the Business Processes which they own as their Happy Thoughts. The secret to getting the most out of a data science project is not changing those processes to accommodate the tech or teaching the Knowledge Worker new processes, it’s building adaptable technical solutions and platforms that can be rapidly configured to make any business process fly.