Data quality is the backbone of every successful data strategy, yet often treated like broccoli on a kid's plate - pushed around, ignored, and only grudgingly dealt with.
Why? Simple: It's a Herculean effort with little instant gratification or glory.
That’s why instead of tackling data quality head-on, organizations often fall for these five cop-outs:
1️⃣ The "Everyone's Responsibility" Fallacy "Data quality should be everyone's job!" sounds great, doesn't it? But let's be real – when was the last time someone got even a thumbs up (let alone a bonus or promotion) for data cleanup? Or even sufficient time to do it? If we want it to happen, we need to make it worth people's while.
2️⃣ The "Tools Will Save Us" Fantasy We all love our shiny tools and AI's promise of solving any task that annoys us. But assessing and improving data quality still needs that human touch. Sure, algorithms can spot some (!) of the oddities, but only people can truly grasp context and nuance. Invest in smart humans, and give them the right responsibilities and means to act.
3️⃣ The "Data Quality Metric" Illusion. What gets measured, gets done, right? Unfortunately, measuring data quality isn't like grading a machine part for accuracy. It's not universal – your "messy" customer data might be a goldmine for marketing but a headache for finance. Context is key, so you can’t universally measure data quality on some scale between 1 and 10. Aim to understand your use cases and your data instead.
4️⃣ The "Bigger is Better" Myth More data rarely means that quality issues will somehow even out over time. A mountain of rubbish is still rubbish, no matter how impressively large. I’ll take a high-quality, relevant and accurate dataset over big messy data (almost) any time.
5️⃣ The "One-Time Fix" Daydream Oh, if only data quality were a "set it and forget it" deal! Sadly, it's more of an ongoing journey of assessment and improvement. Treat it like your health: regular check-ups can prevent a world of trouble down the road.
When it comes to data quality, there are no shortcuts. Data quality demands focused effort, clear accountability, and continuous attention. That’s why it’s hard.
Fixing data quality is not glamorous (yet??), but it's the foundation of every data-driven decision.
Tackle data quality head-on as if your business depends on it. Because it does.