What is Quality Data and How can you Achieve it?

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Data is the lifeblood of business today, with companies using data-based insights for everything from predicting new product trends and personalizing marketing messaging to cutting costs and increasing operational efficiency. 

That’s why data scientists and analysts of all stripes put so much time and effort into ensuring data quality. But what really is data quality, and what can you do to achieve it for your business processes?

Data quality essentially means how much you can rely on your data to serve your purposes. If your data is high quality, that means that it is trustworthy and accurate, without mistakes, duplications, or missing data points, well-organized, and accessible for your analytics tools.

Big data is good, but high quality data is better. Poor quality data can be responsible for unreliable reports, missed opportunities, overlooked risks that blew up into crises, error-strewn decision-making, and result in costing far more in time and money than if you had no data to begin with. 

Data scientists measure data quality according to a number of factors, including:

If you’re confident that your datasets tick all these boxes, then congratulations! You are in possession of high quality data that you can mine to produce valuable, trustworthy insights. 

But if that’s not quite the case, then don’t despair. There are steps you can take to improve your data quality and raise the trustworthiness of your insights. 

Manual data cleaning processes that rely on human eyes to check datasets for errors or duplications simply can’t hold up under the weight of big data. Machine learning (ML) data preprocessing pipelines can standardize data formats, detect duplicates, mistakes, and gaps, and flag anomalies far faster and more accurately than humans can. 

Additionally, ML data cleaning can compare data points against similar datasets from other sources to validate new data and merge it into existing data repositories, helping raise the reliability of your data even further. 

Data profiling is the first step in data processing, coming even before you act to fix any mistakes or enrich any datasets. With data profiling, you’ll run a quick scan of the data before you, to assess its state and consider how much work needs to be done to make it acceptable. 

ML processing pipelines speed up data profiling immensely, and some companies are going a step further and incorporating data profiling into their data catalogs instead of treating it as a standalone step. 

Siloed data is poor quality data. When your data is stored in fragmented and disparate locations, it’s harder to spot missing or mistaken elements. On top of that, if analytics tools can’t find and access datasets, they might just as well not exist. 

That’s why organizations are adopting cloud data warehousing, making choices between options like Redshift vs Bigquery to integrate data from numerous different sources and in multiple formats into a single “source of truth” which helps close gaps in your datasets, reveal those that can’t be resolved, and raise data consistency.

It’s essential to keep checking data quality on a regular basis. Frequent data monitoring enables you to pick up on flawed data before it makes its way through the pipelines and undermines the reliability of your reports. 

Automated data quality rules based on data trends are beginning to take over from manual data quality checks, plus some organizations are “shifting left” and writing data quality tests into the data pipeline itself, to pick up on compromised data quality before it goes any further. 

Making sure that your data is up to date plays a big role in boosting data quality, but it’s not always that easy when you’re dealing with large, complex databases. 

However, with cloud-based data warehousing, you can quickly swap out and renew datasets whenever you like, making your data more up to date and bringing you closer to real time. 

The more context you have for your business datasets, the more reliable you’ll make them. Business queries have a tendency to blur the boundaries of units, departments, and functions, so the more additional data you can include, the more useful it will be. 

By harnessing artificial intelligence (AI) for data capture, you’ll be able to include more contextual information alongside your core data, helping make datasets more relevant and meaningful and enriching data quality. 

Understanding data quality is a prerequisite to raising the standard of your data, bringing you closer to reliable and trustworthy business decision-making. By automating data processing, improving your data quality monitoring, data profiling, and data context, integrating data sources, and simplifying the process of updating data, you’ll be able to boost data quality and ultimately increase your bottom line.