5 tips to improve data quality for unstructured data

Allowing quality data in can lead to a better understanding of an organization. Here are 5 steps to improve your organization’s data quality for unstructured data.

Finding effective ways to use data has been an organizational focus for many years. The significance of these efforts has only advanced in the digital era as businesses engage in fierce competition to maintain and grow their customer bases.

Many organizations are discovering a problem as they start to rely more heavily on their business data: Data on its own is only semi-useful, especially if a data set is unstructured and difficult to interpret.

Finding ways to improve data quality while properly storing, presenting and analyzing this information is key to delivering full value from data to the business. However, ensuring this data quality across both structured and unstructured data sets is no simple task, particularly in organizations that have not invested in the right people and tools.

This guide for improving unstructured data quality is a good starting point if your organization wants to better understand and leverage all of its existing data, regardless of source or format.

Data quality management involves optimizing data for all kinds of business uses and purposes. To truly judge data quality, consider the following evaluation criteria:

Proper data quality management is based upon the principles of assessment, remediation, enrichment and maintenance, whereby data is continually analyzed. Irrelevant, outdated, unnecessary and/or incorrect elements are weeded out or corrected throughout the data quality management process. Data usage methods are then examined to see if they can be improved for better results after correcting outdated or inefficient processes.

Data quality management is crucial for both unstructured and structured data, though some of the steps taken may look different depending on the type of data you’re working with.

Unstructured data is a heterogeneous set of different data types that are stored in native formats across multiple environments or systems. Email and instant messaging communications, Microsoft Office documents, social media and blog entries, IoT data, server logs and other “standalone” information repositories are common examples of unstructured data.

Unstructured data may sound like a complicated scattering of unrelated information, not to mention a nightmare to analyze and manage, and it does take data science expertise and specialized tools to make use of this information, but despite the complexity of working with and making sense of unstructured data, this data type offers some significant advantages to companies that learn how to use it.

Structured data is made up of standard and homogenous data set structures in a predefined format, which is more easily analyzed and maintained and is usually kept in a standard data warehouse. With clearer formats and storage setups, structured data usually requires less skill to administer and manage properly when compared to unstructured data.