Organizations across various industries capitalize on business opportunities with the help of advanced analytics that work to capture and convert data into value-creating insights. Capturing value from data needs a disciplined focus on data quality management to ensure the organization only harnesses good data for analysis to inform decisions and drive positive outcomes. However, failure to manage data quality will impose unwanted costs, risks, and reputation damage.
Benefits of good quality data for your business
Regardless of how simple or complex your analytical needs are, how your data is processed determines the integrity of all data outcomes. Undoubtedly, data is imperfect and incomplete in its raw form. However, with the right tools and processes in place, data can deliver deep insights to help businesses better optimize operations, manage marketing efforts, improve customer service, increase customer retention, and even predict business or customer outcomes.
Otherwise, you might get inelegant, wasteful, and even dangerous data—resulting in false facts, inconsistencies, and erroneous decisions. It’s a simple concept: Garbage in, garbage out.
Data quality management is key to ensuring the quality of analytical outcomes.
But what is the cost of poor data quality? Gartner research reported that poor data quality can cost companies as much as $14.2 million annually. For example, incorrect customer data can poorly impact customer service, marketing efforts, and sales predictions leading to missed opportunities, loss in revenue, and reputational damage.
Related: The key to business survival: A Big Data strategy for risk management
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Ensuring high-quality data
So when is data good enough for analysis? Well, data quality is a benchmark that is related to the overall analytical objective. In other words, if the data is able to meet quality levels that will help accomplish a project’s goals and requirements, it is suitable for that particular purpose.
Must-Read: Data Collection, Preparation, and Cleaning: A guide
But let’s get into the specifics of quality assessment. In order to measure data quality, there are metrics you should use as a reference. These metrics will provide a solid foundation for data governance and help you track the effectiveness of your efforts.
- Accurate – Data accurately represents reality and comes from valid sources.
- Complete – There are no missing, incomplete, or null values.
- Consistent – Data is consistent across the data warehouse and has no conflicts.
- Timely – Data represents reality from the required point in time.
- Reliable – Data is a consistent measure and produces stable results.
- Valuable – Data adds value to the analysis.
- Interpretable – It is easy to derive insights from the data.
Use statistical metrics and visualizations to check for the metrics above. You can also use key statistical measures such as median and correlation to determine data sufficiency.
7 data quality metrics mandatory to follow
Unfortunately, though, it’s not enough to ensure the quality of your data on a case-to-case basis. Proper data governance requires building a foundation and implementing data quality management best practices to collect, prepare, and clean data. You know what they say, “An ounce of prevention is worth a pound of cure”.
And it doesn’t end there too. Data governance isn’t a one-time process but is a continuous set of procedures that includes monitoring, reporting, and enhancing data quality.
A typical data quality management process flow
By making sure your data goes through these stringent procedures, you can rest assured that only quality results will come out of data analysis—resulting in accurate forecasts and reliable reports.
Quality over quantity in the data age
As companies of various industries and sizes collect increasing volumes of data, it has become essential for quality control and error mitigation. You want to get actionable and accurate insights from your data. The last thing you need is an abundance of low-quality data that will give you inaccurate insights and lead you in the wrong direction.
The benefits of data quality management are too many to ignore: sound decisions, confidence in data, reliability of algorithms, and more efficient workflows, among others.
A proper data quality management plan and workflow are enough to boost your business operations. But for good measure, you should also consider hiring data quality experts and using a reliable data science platform that complies with how data is managed. Analance is an end-to-end data science platform that comes with built-in data preparation features. Manage master data with minimal integration issues before data modeling, forecasting, and visualization.