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Companies today are grappling with a wealth of unstructured data from multiple sources—email and other digital messaging communications, social media channels, documents and PDFs, IoT sensors and server logs, and image and audio files, just to name just a few. And while this may seem like a scattering of unrelated information, unstructured data can actually offer some significant advantages—if organizations can ensure its quality.

What is the Value of Unstructured Data?

Since unstructured data comprises around 80 percent of enterprise data, there’s potentially a lot of value for data-driven decision making to be found there. Especially since unstructured data is growing at the rate of 55-65 percent per year, companies must learn how to unlock the value hidden in their unstructured data. 

However, unlocking that value is not always easy. Unstructured data does not conform to a specific, pre-defined data model, so it doesn’t fit easily into the relational database management systems (RDBMS) enterprises use for data management. Transforming unstructured data into row-and-column RDBMs structures is difficult, often requiring manual intervention, custom coding, and possibly even vendor solutions. Storing all this information without a plan for analysis to help yield its value is costly too. 

Although unstructured data is often difficult to tag, sort, and analyze—it can have many benefits for businesses. For example, if a company could analyze hundreds of thousands of social media shares and reviews about its brand online, that company could build end-to-end customer sentiment insights from that complex data. It could get a real sense of what customers want and the issues they are running into. Structured data in your own ERP systems can give you demographic information about your customers, but unstructured data could tell you how your customers feel about your brand.

The Importance of Data Management

Data management is key to helping get value not just from unstructured data but also from semi-structured and structured data. Here are some key tips for improving your company’s data quality and management:

Establish a Data Management Team 

A good first step is to establish data management roles and responsibilities among data scientists, data engineers, business analysts, and any other relevant positions on your data team. These individuals should understand who is responsible for collecting, analyzing, and maintaining data and what is expected of them to be successful. 

Cleanse Data Regularly and in Real Time

Data cleansing and scrubbing methods remove all irrelevant, obsolete, or redundant data. This makes it much easier for your analytics teams to use unstructured data and determine what information is relevant to business decision-making.

Real-time data validation and verification can help organizations avoid accessing unnecessary, incomplete, or incorrect information—all of which can detract from efforts to derive value from the data. 

Use the Right Tools and Techniques

Your data quality can only be as good as the environments in which it is housed. Invest in comprehensive monitoring and alerting controls to ensure that data platforms and storage systems are performing optimally. 

Finally, teams can periodically audit their data management processes and performance against new market technologies. In particular, keep an eye out for data collection and storage improvements, new data standards, and new governance requirements. 

Leverage Your Data with TIBCO

Manage your unstructured data and other data diversity in a well-governed and consistent way with the TIBCO data management platform. Enable your team with the capabilities required for better data access, trust, and control. TIBCO master data management software helps you govern, manage, and share your data in a single solution, on-premises, or as-a-service.

Learn more about how TIBCO can help you make the most of your data for greater business value: