Data Management: Data Quality Can Sink Business Success

Data quality is a hidden danger in many businesses. Its impact is subtle yet profound, affecting decisions, operations, and ultimately, the bottom line. But how can businesses effectively combat it?

Data quality adapts to its context. It encompasses accuracy, completeness, consistency, reliability, and relevance. Yet, it defies easy categorization. Integrating data and managing its quality remain top challenges for customer intelligence professionals. But data quality isn’t just about customers; it permeates every organizational layer. High-quality data should be “fit for use in operations, decision-making, and planning”.

The data landscape has exploded, with companies juggling over 200 apps and 400 data sources. Data engineers grapple with a huge range of tools—Snowflake, dbt, Looker, Salesforce, Marketo—each adding complexity and risk. The more tools, the greater the chance of errors creeping into data pipelines. Paradoxically, some think that the solution lies in more tools: data observability tools that detect, investigate, and solve quality issues before they reach business users.

Humans, unwittingly, introduce data flaws. Picture the medical receptionist abbreviating “California” as “Calif.”—a seemingly innocuous choice that jeopardizes data quality. In healthcare, such inaccuracies ripple through diagnoses and treatments. The challenge lies in educating data creators across the organization, emphasizing the impact of their entries.

Poor data quality extracts a toll. Externally, missed opportunities, revenue loss, and inefficiencies plague businesses. Internally, supply chains falter, leading to headlines and the Great Resignation. HR departments, grapple with poor data and struggle to retain talent. The stakes are high, and the price is paid in missed profits and operational chaos.

Amidst the chaos, some companies emerge as data quality champions. They invest in robust data governance, enforce standards, and empower data stewards. Amazon, for instance, relentlessly polishes its data, ensuring seamless customer experiences. Netflix, too, thrives on high-quality data, personalizing recommendations, and captivating audiences. These pioneers recognize that data quality isn’t a luxury; it’s a strategic asset.

As digital transformation accelerates, breaking down data silos becomes paramount. Organizations must embrace data quality as a collective responsibility. Automation, observability, and education will fortify the data fortress. And the future? Imagine AI-driven data quality bots patrolling pipelines, flagging anomalies, and self-healing.  Businesses that confront data quality head-on, armed with tools and a culture of vigilance, will thrive.