Organizations are living in an Elf-sized fantasy world where AI solves everything.
We’re all starring in the wrong holiday movie. Companies want their Miracle on 34th Street moment with advanced analytics before they’ve proven data quality even exists. They’re racing to the North Pole of automation while their foundation melts faster than Frosty in July.
The box office bomb? A $12.9 million annual loss per organization from poor data quality alone.
Buddy the Elf said it best: “The best way to spread Christmas cheer is singing loud for all to hear.” But nobody’s singing about data quality controls.
Home Alone (With Your Data Problems)
Data-driven decision-making doesn’t start with shiny algorithms. It starts with controls that ensure data is secure and high quality.
Most leaders are Kevin McCallister, left home alone with their data strategy while everyone else rushed to the airport of AI adoption.
Skip quality controls and you’re setting booby traps for your own organization. The data could be wrong. Misleading insights follow like paint cans swinging from the stairs. Incorrect decisions compound.
It’s A Wonderful Life (When Data And Business Teams Actually Talk)
Translating data into actionable insight requires two experts working together. A data expert and a business expert. Both angles must be covered or you get a Bedford Falls without George Bailey.
Without this partnership, predictable failures emerge.
Data people create technically perfect insights that solve no business problems, like giving someone a lasso the moon when they needed practical help. Business people can’t articulate what data could do for them.
Lack of effective collaboration causes 86% of workplace failures, including in data science departments. That’s a lot of Potter-ville outcomes.
Communication breakdowns are common. Misunderstandings multiply. The duo only works when both experts can translate between each other’s expertise.
A Christmas Story (About Data Literacy Nobody Wants To Hear)
Most people aren’t data literate. They’re Ralphie asking for a Red Ryder BB gun dashboard without understanding they’ll shoot their eye out.
They can’t interpret basic visualizations. They need someone to tell them the “so what?” instead of discovering it themselves.
Data quality can be poor. Critical information goes missing like a decoder ring message. Anomalies slip past unnoticed. Visualizations confuse rather than clarify.
Better dashboards won’t fix this. The problem lives in the gap between what data shows and what people understand. You’ll shoot your strategy out with bad interpretations.
National Lampoon’s Data Vacation
AI usage jumped from 55% to 75% among business leaders in just one year. Everyone’s driving cross-country to Walley World without checking if it’s even open.
Yet few organizations experience meaningful bottom-line impacts. They’re Clark Griswold, convinced the Christmas bonus of AI will solve everything while the foundation crumbles.
The actual sequence organizations need looks nothing like their vacation itinerary. Start with data quality controls and security. Add literacy programs that teach interpretation. Build cross-functional duos who speak both languages.
Only then can you build toward sophisticated analytics. Only then do you actually arrive at a destination worth visiting.
The Polar Express (To Data Success)
Success requires believing in the right sequence. Each capability depends on everything that came before it.
Skip a step and the whole train derails like a poorly maintained locomotive. You need your ticket punched at every station or you never reach the North Pole.
We keep wanting to arrive at the grand finale with algorithms humming and predictions flowing. But the journey matters more than the destination.
Start with the boring work of data quality. The work nobody makes movies about but everyone needs to reach their goal.