The Art of Data Science: Making Sense of Unstructured Data

Here's how you can better utilize unstructured data to solve data science-related business problems.

Data science professionals have a deal with business problems that might seem mundane, but amount to significant impacts. For example, a supermarket would want to know the right design to maximize its sales. For data scientists, predicting consumer behavior can be tough to quantify. Do they have to understand what aisles shoppers spend the most time on? How do other stores differ from the store in question? And what products sell well at what times?

The answers to these questions and more will only be answered after analyzing the unstructured data that comes from emails, memos, videos, customer calls, tweets, Facebook messages, and blogs. Unstructured data are present in large quantities, hence companies find understanding this information challenging, despite the fact that crunching that data will give them better insights.

Tech vendors boast about the hidden value of unstructured data as organizations can unearth real value from big data. But these vendors use the wrong approach. Instead of pulling the data apart,  the first task should be to establish the right question like – Who is my best customer? What products are failing? Once the questions are set, a data scientist will start linking unstructured data to structured information available. The goal will be to find information that is connected to the problem at hand.

For that, it is important to identify the "who", "what", and "whom". Who refers to all the usernames, logins, and other IDs that relate to the employees. Once that data is captured, a data scientist should understand who the customer is. Understanding the customer helps in capturing the right information. This structural information should then be paired with unstructured data from several conversations. These conversations can range from internal employee interactions and customer interactions. Hence, companies must realize that unstructured data is not a separate entity. Only when the right structured data is available, unstructured data can be made sensible.

By now, the data scientist will know what product leads the highest sales. Then, the question demands an optimal layout to increase the overall sales. Crowd analysis will get the answer. Used by retail companies, theme parks, and even police, this method predicts how groups of people react in certain situations. For this case, videos of shoppers can be analyzed to assess the pattern in which people move around, where they stop, where they put the items in the basket, and they behave in empty areas.

Once that information is at hand, this unstructured information will be combined with structured data like the position of certain products and how they appear on the aisle. This will create a full picture of the shopper's behavior. This final data can be used to make predictions about future sales. The staff will be advised to shuffle the placement of a few products according to the wants and demands of the majority of the customers.

Combining structured and unstructured data builds the foundation for successful predictive analysis. But for the right decision and the full picture, organizations need to acknowledge to dependency that unstructured data has on structured data.