Data Quality & Standardization: The Unsung Heroes of Business Success

We get it. When people talk about digital transformation or AI strategy, everyone’s mind jumps to sleek dashboards, machine learning, and algorithms with fancy names like Falcon or Titan. But here’s a plot twist: none of that matters if your data initiatives are built on quicksand of low-quality data. Every movement gets you deeper in trouble.

In fact, Forrester reports more than a quarter of global data and analytics employees estimate their organizations lose over $5 million annually due to poor data quality. That’s a whole lot of money for data that can’t even agree with itself.

Imagine your analytics team pulls customer data from five systems… and each one lists the same person with a slightly different name, email, and birthday. Congratulations! You’ve just turned “Customer 360” into “Customer Choose-Your-Own-Adventure.” And the impact isn’t just financial – it’s strategic, reputational, and operational. You can’t make smart decisions on dumb data.

Real-World Implications

Consider this: a Wall Street Journal article highlighted how data quality issues are hampering companies' sustainability efforts, with executives acknowledging significant challenges in their environmental, social, and governance (ESG) initiatives due to unreliable data . If data quality can impact global sustainability goals, imagine its effect on your quarterly reports.

Think about who’s done it right. Netflix doesn’t just have strong original content, it also has clean, standardized, well-governed data. Its success in personalizing recommendations, optimizing content production, and even predicting churn relies heavily on a robust data infrastructure. It’s why your "Because you watched..." section is eerily spot-on.

Capital One’s slogan could be “What’s in your data?” Their AI-driven fraud detection and customer engagement tools wouldn’t work without consistent, clean data pipelines. Who hasn’t received an extra cash-back incentive for someplace you wanted to shop?

Now think about who hasn’t. A large retail chain recently overestimated customer demand due to poor data interpretation and forecasting, resulting in more than $4 billion in unsold inventory. That’s a warehouse full of regret caused by siloed data with inconsistent metrics that led to terrible supply chain decisions.

The Secret Sauce Is…Standardization

Imagine trying to assemble IKEA furniture with instructions in multiple languages and missing tools. That's your data without standardization. Consistent formats, definitions, and structures are the Allen wrenches of the data world – essential for building something sturdy.

Data quality and standardization relies on auditing your data. You need to know what you have and where it lives. No, spreadsheets called “Final_v9_REAL_useTHIS.xlsx” don’t count, especially when there are six of them, and one is a pdf for some reason.

Without naming conventions, formats, and data dictionaries, “customer_name,” “custname,” “full name” and “CN” could be interpreted as different things, causing everyone to play “Guess That Field Name” and your reports to interpret Robert Smith, Rob Smith, Bob Smith, and Sm1th, R as three different customers and a bot.

Final Thought

Be brave enough to ask: “Wait, why are we pulling revenue numbers from the marketing system?” The answer shouldn’t be a shoulder shrug.

Data quality and standardization may not be glamorous, but they are the bedrock upon which successful business decisions are built. Neglect them, and you risk costly errors and misguided results. Embrace them, and you pave the way for accurate insights and informed actions. Get out of the quicksand. Quickly.

If you are ready to start talking about it, click here to get started now.

Jules Trono, Chief Growth Officer, Vivanti