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.

Behavioral Analytics
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.

Fraud Detection
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
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.

CTC’s core services are directly 
tied to data: 

Data information and requirements analysis
Data security, privacy and OMB compliance
Data collection
Data flows
Data cleansing
Data harmonization
Data dictionary
Data and Database modeling
Database entity relationship structure
Database and Data Warehouse development
Data movement: APIs, microservices, ETL scripts
Data reporting
Data analysis
Data analytics and business intelligence
Data improvement
Data archiving

Case Study

DATIM (Data for Transparency, Accountability, & Impact) is a health-oriented software platform to support the monitoring, evaluation, and reporting of the PEPFAR program. DATIM gathers HIV/AIDS data from more than: 80,000 health care facilities in over 63 countries and encompasses 50+ million recordsIn support of the CDC and Department of State, CTC supports the Data for Transparency, Accountability, & Impact (DATIM) project. DATIM gathers HIV/AIDS data from more than 80,000 health care facilities in over 63 countries and the database encompasses 50+ million records. The source data is collected in several different ways including mobile phone apps, tablets, online web entry, and Excel for uploading when an internet link is enabled. With this large volume and variety of data sources, there is an extensive effort to cleanse and harmonize the data before it is analyzed. When the data is appropriately sanitized, it is ingested into a CTC designed and built data warehouse. Once the data is in the data hub, it can be analyzed using different tools including MicroStrategy, GENIE (a CTC custom built tool), Excel, R, SQL, SAS, or other methods selected by the data analyst.