16 Feb

7 Steps to Analyze Unstructured Data

Unstructured Data

Information digitization with a high volume of a multi-channel transaction has resulted in data flood. The always growing speed of digital data has forced the world’s combined data to twofold. As per Gartner report, approximately 80% data apprehended by a company is unstructured data. It includes data from consumer calls, emails, and opinion on social platforms. In addition to this, a huge amount of data is being generated through diagnostic information logged by various user devices. In the first place, organized data itself is so huge that it demands a humongous effort to analyze the same. Making sense out of unstructured data would be far more difficult than structured data.

While going through a huge amount of data might seem a tough job but at the end, it would be rewarding. By going through unstructured data sets, relation and pattern can be found out by detecting the connection between unrelated data sources. Trends can be discovered through this analysis method that would be a useful insight for a business.

Steps to analyze unstructured data:

  1. Use relevant data sources

To start, it is essential to understand data sources that are significant for the analysis. Streaming videos, chat, emails, voice files and weblogs, all of them comes from unstructured data sources. If the information is only loosely connected to the issue, it must be kept aside. Only relevant data sources should be used for analysis that would result in a relevant outcome.

  1. Define analytics requirement

An analysis may become useless in case end requirement is not defined. It is key to know what kind of result is expected. Expectation could be volume, pattern, reason, impact or altogether something different. Also, usage roadmap for analysis result should be given so that it can be utilized during predictive analysis prior to segmentation and integration.

  1. Pick technology stack for data incorporation and storage

Fresh data can be brought from various data sources. The analysis result should be kept in a technology stack or in cloud storage so that it is simpler to get data for analysis purpose. Picking data storage system is dependent on various aspects such as scalability, quantity, and velocity needs. It is essential to pick right technology stack for data incorporation and storage. Project information architecture can be set only after evaluation of final requirement against technology stack.

  1. Use data lake to keep data before sending to data warehouse

Conventionally, a company gathered data, cleaned it and stored like if data source was HTML file, only text will be extracted stored. Other information from HTML file will be lost in such a way that it seems the same has been lost while storing in the data warehouse. The plea of this preceding approach was that the data was in an unspoiled, changeable format. It could be used on the basis of requirement. Though, with the arrival of Big Data, data lake is being utilized to store the data in its original format. So that when it is thought beneficial and required for a reason data can be provided in its original format. It protects the data with all information that might help in analysis.

  1. Clean the Data

It is advised to clean up a copy of data and keep the original file in native format. For example, a text file can have plenty of noise that vague important information. It is a good method to remove noise such as whitespace, symbols while changing casual text into a formal document. Spoken language should be specified and kept separately. Duplicate information should be removed.

  1. Ontology Assessment

Connections among sources and entities can be built to create specifically structured database through analysis. It might be a time-consuming task but obtained insights would be significant to any business.

  1. Data Modeling and Text Mining

Data should be classified and segmented post database creation. It will consume less time while utilizing supervised and unsupervised machine learning such as:

    • K- means

    • Logistic Regression

    • Naïve Bayes

    • Support Vector Machine Algorithms

Consumer behavior resemblances and comparisons can be found out through these tools. It would help to design a campaign. The nature of consumers can be identified with sentiment analysis of opinions and feedbacks.

The actual worth is in the usage of data analysis for 360-degree insight. It should have combined analysis of structured and unstructured data. Structured data can forecast consumer behavior. Unstructured data analysis can reveal the motive behind such behavior. Fresh data sources like social platforms are vital to companies as they offer unique information that can be analyzed. Data scientists need to equip themselves with new and appropriate skills to analyze unstructured data.