Data scientists deal with business issues that may appear insignificant on the surface but have huge ramifications. A supermarket, for example, would want to determine the best design to boost sales. Predicting customer behavior might be difficult for data scientists to quantify. Do they need to know which aisles customers spend the most time in? What distinguishes other stores from the one in question? And at what periods do certain things sell well?
These and other questions will be answered only after unstructured data from emails, memos, films, customer calls, tweets, Facebook messages, and blogs is analyzed. Because unstructured data is abundant, corporations find it difficult to comprehend it, even though analyzing the data will provide them with greater insights.
As enterprises can unearth actual value from big data, tech providers talk about the hidden worth of unstructured data. However, these suppliers are taking the incorrect approach. Rather than dissecting the data, the first step should be to ask the correct question, such as, “Who is my best customer?” What items aren’t succeeding? A data scientist will begin integrating unstructured data to structured information once the queries have been established. The goal will be to locate information related to the issue at hand.
It’s crucial to figure out “who,” “what,” and “whom” in this case. Who refers to all of the employees’ usernames, logins, and other identifiers. A data scientist should be able to identify the customer once the data has been collected. Understanding the customer aids in the collection of accurate data. This structural data should then be combined with unstructured data from a variety of conversations. Internal staff interactions and consumer encounters are examples of these types of dialogues. As a result, businesses must recognize that unstructured data is not a distinct entity. Unstructured data can only be made sensible when the appropriate structured data is present.
By now, the data scientist will have figured out which product generates the most revenue. Then, the question is which the optimal layout to boost the overall sales is, and the answer is crowd analysis. This strategy, which is used by retailers, theme parks, and even the police, predicts how groups of people would respond in specific scenarios. In this scenario, videos of consumers may be studied to determine the pattern in which people move about, pause, place products in the basket, and behave in empty regions.
Once such information is obtained, it will be merged with structured data such as the location of specific products and how they appear in the aisle. This will provide you a complete picture of the shopper’s actions. This final data can be utilized to develop sales forecasts in the future. Staff will be encouraged to rearrange the placement of a few things based on the demands of the majority of customers.
The foundation for good predictive analysis is built by combining structured and unstructured data. However, to make the best decision and get the complete picture, businesses must recognize the unstructured data’s reliance on structured data.