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It is a challenge to leverage the power of ever-burgeoning data. Particularly, unstructured data are difficult to leverage, as it is snared in unstructured documents, social media posts, machine logs, etc. The reasons are not far to seek. They are the lack of flexibility and limitations of scale when it comes to utilizing data. Accordingly, how can an enterprise derive significant business value from harnessing unstructured data?

The first step is to conduct a thorough analysis to comprehend the extent of the unstructured data challenge. It will help enterprises recognize that even unstructured data have to be organized. This is where text analytics, taxonomy development, categorization and tagging of content prove their worth. Briefly, the analysis is critical for deciding on new solutions or tapping current solutions to identify, secure and monetize unstructured data.

Understanding the data is as important as understanding the extent of the challenge. For it is easy to accumulate data without knowing where it is stored and how it can be retrieved. Understanding data requires a layer of abstraction above all data including unstructured data. This layer of abstraction, metadata as it is called, will present a more comprehensive view of the information processing process. Therefore, to follow the data, you need to identify your metadata needs. Having a plan for metadata will simplify the process of extracting value from data, offer more efficient searches and help convert data into an asset.

Understanding data does not stop with identifying how much of it needs to be stored. It is about articulating data discoveries in business terms. Therefore, it is vital to interact with various stakeholders and understand their business challenges. Unless the business challenges are identified, it is difficult to decide which metadata process to use, storage to deploy, rules to establish and which actionable insights are important.

New threats, laws and consumer activism make data privacy a strategic initiative. It is imperative for enterprises to adopt privacy issues as part of their core business operations. Hence, privacy challenges require a better understanding of the risks of losing critical data. This calls for better management of both structured and unstructured data. The capability to identify and manage this sensitive data can help enterprises to navigate these initiatives successfully.

Beyond the four points mentioned above, enterprises should realize it is possible to draw actionable insights from unstructured data only when it is integrated with structured data. This will help enterprises to recognize tendencies and have greater flexibility to adjust to the evolving needs of their clients. It will help enhance the value of enterprise data and improve the quality of decision-making. The integration of metadata, personalized search patterns and simulation variables offers a powerful repository for active decision support and early insight into future developments. However, the first step is to organize data that makes sense for an enterprise and for data to make sense, it is critical to define a universal taxonomy.