In the digital age that we live in, millions of us are generating huge amounts of data from multiple devices. The massive growth and the diversity of this data pool are raising new challenges for businesses in decoding data into actionable insights. Without accurate analysis of data, companies are wasting valuable insights, or worse, acting on non-complete information. In order to make sense of all the information available, businesses need to implement a data analysis process with a team and the application of tools to record, analyze, dissect and present the findings in an understandable language that can guide senior teams in business decisions.

Data analysts look at data as a raw material which needs to be quality checked, analyzed, and processed in order to create a final product that can be used as insights. Businesses are then able to use these insights to make strategic decisions and solve complex problems. Whether it is a marketing decision or designing new HR policies, enterprises cannot rely on data unless it is accurate and in a format, they understand in order to use it to drive their strategy. Usually, data analytics only involves quantitative data, such as the age group of customers, product ratings, etc. The reviews and the opinion expressed in natural language doesn’t translate into insight as these data are qualitative. In order to have a complete customer picture, enterprises are using artificial intelligence (AI) and machine learning (ML) solutions to better understand consumer opinion and convert it into quantitative data. AI and ML bots are programmed to understand customer intent with the help of Natural Language Processing and are able to score each consumer opinion based on positivity, dis-satisfaction, intrigue and a number of other parameters.