The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results. CTC draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. To ensure robust analysis, CTC data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.
The primary goal of CTC’s data analysts is to increase efficiency and improve performance by discovering patterns in data. CTC has extensive experience with Health data analytics which involves the extrapolation of actionable insights from sets of patient data, typically collected from electronic health records (EHRs).
Predictive Support. CTC applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. These techniques use historical data to identify trends and determine if they are likely to recur.
Customer Sentiment Analysis. Customer sentiment analysis is the automated process of discovering emotions in online communications to determine how customers feel about a product, brand, or service. CTC’s Customer Sentiment Analysis helps businesses gain insights and respond effectively to their customers.
Business analytics uses data analytics techniques, including data mining, statistical analysis, and predictive modeling, to drive better business decisions. CTC builds analysis models and simulations to create scenarios, understand realities, and predict future states.
The application of data analytics includes fraud detection. Analyzing data can optimize efficiency in many different industries by improving performance, enabling businesses to succeed in an increasingly competitive world, and identifying fraudulent activity and data.
Customer segmentation is the process of separating customers into groups based on what certain traits (e.g. personality, interests, habits) and factors (e.g. demographics, industry, income) they share.