Guide to Data Quality
- gezinascimento3
- 1 day ago
- 3 min read
Data quality is crucial for business success, and poor data quality can be disastrous, costing organizations millions. An astonishing 90% of the world's data has been generated in just the last two years , underscoring the rapid pace of data creation and the urgent need to manage it effectively. Insights extracted from data are only as good as the data itself.
Poor data quality makes it impossible to generate trustworthy insights, leading to poor decision-making. Organizations must ensure that high-quality data is available to everyone who needs it.
The High Cost of Bad Data
Poor data quality costs organizations an average of $12.8 million annually, according to Gartner.
With the exponential growth of data, the cost of poor data quality will also grow exponentially if not addressed quickly.
A proactive approach to fixing bad data is essential:
It's 10 times cheaper to fix data quality issues at the beginning of the chain than at the end.
The potential cost once bad data is acted upon (e.g., used for decisions or sent to customers) can be $100 or more, compared to only $1 to fix it at the point of entry.
Poor data can be spotted using a common framework with five critical dimensions: Completeness, Accuracy, Consistency, Accessibility, and Timeliness.
Debunking Data Quality Myths
The guide addresses several common misconceptions about data quality:
MYTH 1: "Data quality is just for traditional data warehouses." Modern data quality tools handle any dataset type, format, and source, including cloud, traditional systems, and IoT devices, and can fix bad data at multiple points, not just in a data warehouse.
MYTH 2: "Once you solve your data quality, you're done." Improving data quality is an always-on, continuous, and iterative process.
MYTH 3: "Data quality is IT's responsibility." Data should be a shared responsibility and a whole company priority, involving business users, managers, and data stewards.
MYTH 4: "Data quality software is complicated." Many modern data quality solutions are designed as self-service applications with simple interfaces, allowing non-technical business users to cleanse and enrich data without IT help.
MYTH 5: "Investing in data quality improvements is too expensive and time-consuming." The costs of poor data quality (missed opportunities, increased risk) can far outweigh the investment in data quality improvement.
Six Steps for Better Data Quality
The process for better data quality is consistent, regardless of the data source.
The six key steps are:
OBSERVE: Continuously track data sources and evaluate their quality and integrity.
DISCOVER: Search, find, and profile data to understand its structure and content.
GOVERN: Define data owners, roles, and data-related policies.
STANDARDIZE: Establish the application of consistent formats, definitions, and structures.
CONSOLIDATE: Eliminate data redundancies and inconsistencies, and create a single source of truth.
OPERATIONALIZE: Integrate data quality and governance practices into day-to-day workflows.
Ready to Unlock Your Data's Full Potential?
Don't let bad data kill your business strategy or cost you millions! Learn how to transform your data landscape into trusted, actionable insights by prioritizing data quality.
Download the complete "Definitive Guide to Data Quality" to get a deeper understanding of the factors that contribute to quality data, guidance on ensuring your data meets high standards, and real-world examples of how organizations maximize business efficiency with high-quality data.
Click here to download the full guide and start building your foundation for trusted data today!
If you have more questions about improving your data quality or implementing a modern data management solution, don't hesitate to contact IPC Global's experts for personalized guidance.
