Unstructured Data Summarized

With modern advancements in high performance computing, data processing technologies now have the ability to handle enormous volumes of data, often in real-time. Whereas in the past it was only possible to efficiently process structured data, it is now also possible to extract useful information from unstructured data, which is a format that comprises up to 80% of a typical company’s total data.

Structured data typically refers to alpha and numeric data that are arranged in row and column format, as in a spreadsheet file or database table; another example would be a binary file from which a set of variables can be read in a repeating sequence. In these cases, the data file or table contains a format that can be read systematically by following a routine pattern of actions.

Unstructured data however, does not adhere to any format that defines how it could be systematically read or interpreted. Therefore, a computer cannot directly extract useful information for analysis merely by reading the file. Before analytics can be performed on unstructured textual data, there must be a custom designed preprocessing stage, during which the files are scanned for certain types of lexical sequences or features.  Once assembled, these features are then processed by a Natural Language Processing algorithm, that will produce numerical data that describes them in some relevant way. Only after this stage has been performed will it be then possible to apply analytical machine learning methods to the resulting numerical data, in order to identify trends and behavioral patterns.

Typical examples of unstructured data include log files, voicemails, text messages, social media posts, word processing files, as well as web search histories. Overall, unstructured data can be utilized in a multitude of ways to acquire useful insights that would lead to improved business operations. Several examples are as follows:

1. Social media commentary can now provide access to actionable information, when the data streams are properly monitored and processed. For instance, marketers can use this technology to monitor specific posts discussing products and personal purchase priorities, and then extend tailored service offerings to meet the unique needs of the authors of those posts. A similar strategy could be used to identify and console dissatisfied customers.
2. Analysis of customer feedback in call center transcripts and on-demand web chats with service reps can be employed in order to identify sentiments that reveal evolving trends. For a successful business, information such as this cannot be ignored, and will pertain to issues such as product popularity or faults, service reputation, customer complaints, and reasons for technical assistance requests.
3. In search engine marketing (SEM), identification of appropriate key words for website targeting/retargeting is crucial. Monitoring unstructured data to detect keywords used in searches can help to optimize SEM campaigns around the best performing keywords used by the target customer group.
4. Customer complaint data can be analyzed for degree of severity and dissatisfaction by employing text analysis to assess overall emotional tone and sentiment.