Top 5 Most Common Data Quality Issues

High-quality data is one of the greatest drivers of revenue for a modern business in this era of digital disruption. Data-driven companies are highly dependent on the latest technologies and artificial intelligence to be able to process and get the best out of their data assets. In this process, one of the biggest challenges is with the issues related to data quality such as inaccurate or incomplete data, hidden data, data security, and others. Quality Data helps in developing an in-depth understanding of your business and you can make successful decisions if you are relying on accurate data.

top data quality issues

Let's discuss the top data quality issues and how can address them:

Issue 1: Duplicated Data – In every business duplicated data is a challenge that has to be dealt with. Multiple copies of the same records provide incorrect insights when they are not addressed effectively. Duplications may arise due to many reasons such as human error, change of demographic data, default values, someone entering data multiple times and lack of data standardization, etc. Data deduplication helps to identify duplicate records. Marketing campaigns or historical trend data will not be a correct representation of actual market scenarios if data some prospects may get missed or may be duplicated multiple times.

Issue 2: Inaccurate Data – Data is not complete if you are not gathering all the hidden data as this hinders you from making winning decisions on accurate and complete data sets. The cause of inaccurate data in systems is often that data is filled with mistakes such as typo or wrong sentence / word formation provided by the customer or while inputting the details in the wrong fields and it also might be due to data decay, data drift, and human errors. Accurate data plays a significant role in every industry and helps you to plan with the appropriate response. Accuracy of the customer data matters a lot for providing a personalized customer experience and developing right business plans.

Issue 3: Ambiguous Data – In large data lakes or databases, some errors are responsible for incomplete, inaccurate, and misleading data. Spelling errors can go undetected, formatting can have issues, and column headings can be misleading. Such type of ambiguous data is responsible for multiple flaws in analytics and reporting. Amurta’s DIP Tool helps to resolve ambiguity by swiftly tracking down the issues as soon as they arise.

Issue 4: Hidden Data – Most of the companies use only some of their data, while the remaining valuable data might be lost in data silos or dumped in data graveyards. For instance, customer data that is available with the sales and marketing may not get shared with the customer service team which tends to lose any opportunity to create complete and accurate customer profiles. Hidden data results in missing out on opportunities to innovate new products, improve services and optimize processes. Learn how a Data catalog helps you deal with the challenges of hidden data.

Issue 5: Inconsistent Data – When we are working with multiple data sources, there tend to be mismatches in the same information across sources. This inconsistency may be due to the spellings, units, or formats. Most of the time we come across inconsistent data during company mergers or migration. Reconciliation must be done effectively to protect the value of the data. Successful & data-driven enterprises always focus on data consistency because they can power their analytics only with the trusted data.

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5 Most Common Data Quality Issues

Duplicated Data , Inaccurate Data, Ambiguous Data, Hidden Data, Inconsistent Data