Alan Morrison, contributor at Data Science Central, recently integrated two of my blogs (one recent and many moons ago) into an interesting perspective that he shared on the Data Science Central email distribution list (get on it if you are not already). Alan’s key points are this:
- 80 percent of enterprises will get distracted by off-the-shelf technologies and neglect the hard work of improving their own data.
- The 80/20 rule still applies: One out of five organizations will hunker down and do the work necessary to make their data pay off. The other 80% either make excuses or license something and declare “mission accomplished.”
Sorry, but there are no shortcuts if you really want to transform your company…and society.
Thanks Alan!
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Data passivity and the current obsession with off-the-shelf chatbots
Last September, Bill Schmarzo (“Point – Counterpoint on Why Organizations Suck at AI”) listed a few common excuses enterprises use to explain why they aren’t doing more with AI:
We Don’t Have the Right Talent. “We can’t hire the right talent and don’t have bottomless budgets to spend on complex technologies and massive cloud costs to train the AI / ML models.”
We Don’t Have Big Data. “Our company lacks the massive amounts of big data out of which we can mine customer, product, and operational insights.”
Can’t Scale or Operationalize our AI / ML Efforts. “We don’t have the organizational skills and experience to sustain our data and analytic efforts. Instead, we end up with unrelated data projects, data silos, and orphaned analytics.”
Bill’s counterpoint to each of these excuses underscored what companies actually lack: A data-centric culture and attitude. To paraphrase Bill, everyone in the organization has to think like a data scientist to make the best use of data. You don’t need to have big data to develop intelligence. To make AI effective organizations have to understand user intent, a point Bill elaborated on in a February post titled “AI Effectiveness Starts by Understanding User Intent.” Intent has everything to do with an articulation of what the purpose of the modeling effort is and what constitutes a successful model.
In 2023, there has been even more buzz around generative AI and prompt interfaces due to the iterative ask-to-answer capabilities such as Open.ai’s ChatGPT, Google Bard and tech like it. Here’s a data science prediction: 80 percent of enterprises will get distracted by off-the-shelf and embedded chatbots. As a result, they will avoid the hard work of improving their own data.
The 80/20 rule still applies: One out of five organizations hunkers down and does the work necessary to make data pay off. The rest either make excuses or pretend that what they’ve licensed will do the job for them.
