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With corporate data growth the volume of unstructured data is growing in parallel. Their volume is increasing annually at a rate of 55 to 65%. By ignoring such data companies don’t receive certain knowledge and don’t have a possibility to use it for analytics. This automatically doesn’t allow them to use all the possibilities. However, it is very important to know how to properly use unstructured data to achieve business goals.

Unstructured data benefits:

1. Product development. With the help of unstructured data it is possible to study users’ moods and needs, analyze requests that come to the support service or social networks. This approach will improve company service or product;
2. Sales and marketing. In this case, unstructured data is used to identify shopping trends and brand perception. The advantage of such data is the ability to assess consumer sentiment. Studying social media posts, forum discussions, support calls, and more can help increase sales and marketing strategy effectiveness. Unstructured data usage by CRM algorithms allows to conduct predictive analytics and know in advance consumers’ desires. So, employees of the sales department will be able to offer the necessary product or service to the consumer in time;
3. Customer service. Automated chatbots allow to direct customer requests to the right people to resolve the issue quickly. Then the analysis of these issues is carried out, as mentioned above. This allows not only to know consumers’ moods and wishes, but also to identify effective and inefficient features of a product or service. This, in turn, will allow to improve the product or service.

Using unstructured data for BI involves 3 main steps:

1. Determine the purpose of using unstructured data. It is necessary to clearly understand what problems need to be closed with the help of external data and how exactly it will be used;
2. Optimize data sources. To create a set of valid data it is necessary to create a common data model. Since unstructured data is drawn from different sources and in different formats, it is possible to ensure data consistency and reliability by quality data flows creating;
3. Create a plan and upgrade data processing programs. It is worth partnering with providers of high performance and high-quality data integration applications and resources. The key issue is an internal interface and methods definition for connecting data sources.