The Most Common Data Quality Issues and How To Address Them

Data is an invaluable resource in today’s global village. From complex tasks like sending rockets to the sky to ordering a cup of coffee, there are not many things you can do without experimenting with data. The business world leverages data from multiple sources for decision-making and operational efficiency, but a huge part of data’s resourcefulness relies on the level of quality associated with your data sets. Therefore, setting data quality KPIs to improve its usefulness can never be a miss. Here are the most common data quality issues and how to address them.

Duplicate Data

Companies source data from multiple locations, including their local databases, cloud data lakes, and legacy and on-premise system applications. Managing these disparate data sources and independent silos can often lead to duplicates. Duplicated data can have several implications for your operations and decisions. For instance, duplicating contact details can mislead you about the exact customer population for your CRM planning efforts.

Data quality issues occur often, and many professionals tend to overlook their effects. But entertaining some duplicated data increases the probability of skewed analytics, leading to avoidable and costly mistakes. Therefore, it’s essential to invest in data quality resources to rid your systems of duplicate data. Ensuring your data provider has a strict verification process to fish out duplicated data records earlier in the data management cycle can be a great option.

Incomplete Fields

Capturing data manually can lead to incomplete fields. This is often the situation at the data entry stage when agents rush, skipping some bits of information in the process. Inaccurate data can slow down your data management efforts and lead to redundancies. You can manage such issues using systems that don’t deliver submissions unless business sets provide details for all the required fields. These systems can automatically eliminate incomplete entries, setting them aside for further reconsideration. This level of accuracy is vital for businesses of all sizes and industries. You can get a real-world picture of situations to tailor decisions accordingly. Data accuracy can also impact your marketing efforts with personalized campaigns that churn positive return on investment (ROI).

Data Overload

The saying “too much of everything is bad” can apply to data assets, especially when using traditional data management solutions.

Having too much data can decrease the speed with which users can access data relevant to analytical projects. Also, other data quality issues can become more critical as data volumes increase, and it’s possible to get lost in the data troves.

This issue is the reality for many businesses. Many data professionals spend about 80 percent of their time locating the right data. An ideal remedy is investing in fully automatic profiling, outlier detection, schema change detection, and pattern analysis.

Inconsistent Data

Data can go wrong in even the simplest ways. For instance, people can enter dates in several formats, from “dd mm yy” to “yy mm dd.”

Some systems may have been programmed with the latter, others with the former. And as data flows from one system to another, interpreting these multiple formats can become challenging, leading to many data errors. Ensuring data providers include specific formats in your data quality standards can reduce data inconsistencies.

Ultimately, you can use automated artificial intelligence-based verification processes to detect persistent inconsistencies. Human error-related issues can have grave consequences depending on your industry. For instance, NASA lost its $125 million spacecraft due to incorrect data. Many businesses have also had a fair share of wrong decisions premised on ill-informed intelligence. Therefore, it’s important to know the common data quality issues and how your business can efficiently manage them.