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Modern companies own a large data amount. Approximately 80% of them are unstructured data. Now the volume of unstructured data is increasing dramatically at a rate of 55 – 65% per year. Such data can be very useful in achieving business goals. It is worth paying attention to tools for studying unstructured data, that will allow to get additional information and use it in the decision-making process.

Main purposes of using unstructured data

1. Product development

With the help of unstructured data, companies can study and analyze consumer sentiment, support calls, social networks, etc. This approach will allow companies to find ways to improve their services and/or products, which in turn will lead to improved performance indicators;

2. Sales and marketing

The main purpose of using unstructured data in marketing is to determine the buying trend, brand perception, and evaluate customer sentiment. Analysis of messages in social networks, discussions on forums and other sites will help to evaluate the effectiveness of sales and marketing. Also, unstructured data is useful for algorithms that are used in CRM platforms. Predictive analytics provides insights that companies can use to anticipate customer needs. In this case, the company gets the opportunity to offer a certain product exactly at the time when the client needs it;

3. Customer service

Companies often process customer requests using automated chatbots. They support customer service managers and direct their questions to the appropriate staff who help resolve the issue. This information can be analyzed to determine customer sentiment. Also, it allows to identify non-working and inefficient functions. This information is used when developing a new product or improving an old one.

3 steps to using unstructured data for BI:

1. Determine the specific purpose of using unstructured data – based on the needs of the company, it becomes clear what kind of data needs to be collected;
2. Optimization of data sources – first of all, it is necessary to create a common data model in order to ensure the reliability and quality of data, regardless of the source;
3. Plan and maintenance of data processing programs – it is important to work with providers who specialize in high-performance, high-quality data integration applications and resources.