Person clicking a "Data Quality" button.
Image: momius/Adobe Stock

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.

SEE: Hiring kit: Business information analyst (TechRepublic Premium)

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.

Jump to:

What is data quality?

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

  • Accuracy: Is the data valid? Does it possess sufficient details to be useful?
  • Completeness: Is all relevant data present in the data set? Is it sufficiently comprehensive? Are there any gaps or inconsistencies?
  • Reliability: Can the data be trusted for business decision-making? Are there any contradictions in the data set that cause you to question its reliability?
  • Relevance: Can the data be applied to all relevant business needs and concerns?
  • Timeliness: Is the data up-to-date? Can it be used to make real-time decisions?

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 Irrelevant, outdated, unnecessary and/or incorrect elements are weeded out or corrected throughout the data quality management process. Data usage methods are then examined to see if they can be improved for better results after correcting outdated or inefficient processes.

SEE: Best practices to improve data quality (TechRepublic)

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.

What is unstructured data?

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.

SEE: 5 ways to improve the governance of unstructured data (TechRepublic)

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.

What is the main difference between structured and unstructured data?

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.

How to analyze unstructured data

Before you can start analyzing your unstructured data effectively, it’s important to set goals regarding what data you want to analyze and for which intended outcomes. Depending on your business and its data goals, you may be looking at unstructured data to understand anything from customer shopping trends to seasonal real estate purchases and geographic-based spending. Knowing the type of data you want to analyze and what it needs to communicate to your users is an important first move in data quality management.

SEE: Top 10 benefits of data quality management (TechRepublic)

Next, you should identify where the necessary data resides, how it should be collected and analyzed, and which methodologies will work best with this data type. It’s important to ensure you have a secure and reliable method for collecting this information and feeding it into data analysis tools. Factor in mobile or portable devices and how you will need to keep them linked during the data collection process as well.

Throughout your unstructured data analysis, plan to utilize metadata — or data about data — for better performance. You should also determine whether artificial intelligence and machine learning techniques can or should come into play for automated workflows and real-time data management requirements.

5 tips for improving data quality for unstructured data

Set up a data quality management team

Before you can effectively manage data quality of any kind, it’s important to establish distinct data quality management roles and responsibilities among your data scientists, data engineers and business analysts. Identify the data quality management team members who will each be responsible for collecting, analyzing and maintaining unstructured data.

SEE: Data quality management: Roles & responsibilities (TechRepublic)

For each set of tasks and roles that you designate, ensure the scope of their duties is properly established and agreed upon. Conduct training as needed to ensure employees have the appropriate skills — as well as security and compliance knowledge — to manage data quality well.

Use system and performance monitoring tools

Data quality can only be as good as the environments where data resides. To ensure that your data platforms and storage systems are performing optimally, utilize comprehensive monitoring and alerting controls for all relevant environments.

Consistent, real-time monitoring of these data-storing systems ensures the availability, reliability and security of the data assets in question. APM monitoring and data observability tools are some of the best options on the market to support this kind of data monitoring.

Make data quality fixes in real time whenever possible

It’s a good idea to incorporate real-time data validation and verification across your data operations. This will help you to avoid harnessing unnecessary, incomplete or incorrect information, which will detract from business efforts to obtain value from the data.

Cleanse data regularly

Utilize comprehensive data cleansing and scrubbing methods to remove irrelevant, obsolete or redundant data. Removing excess data makes it much easier to sort through and assess the relevant information in your systems. It may be worth investing in a data cleansing tool that helps you to automate and simplify this process.

Research and apply new data quality management techniques

It’s important to conduct routine analysis of your existing data quality improvement techniques and to look at new technologies and techniques as they emerge. Especially be on the lookout for data collection and storage improvements, developing data standards, and new governance and compliance requirements.

Read next: Top data quality tools (TechRepublic)

Subscribe to the Data Insider Newsletter

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more. Delivered Mondays and Thursdays

Subscribe to the Data Insider Newsletter

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more. Delivered Mondays and Thursdays