Data architecture refers to the structure and design of the systems and processes used to manage and store large volumes of data, ensuring its quality, security, and accessibility for later use and analysis.
Data engineering refers to the set of processes and techniques used to design, build, maintain, and optimize systems that allow for the efficient flow of data between different systems and applications, ensuring its integrity and quality.
It is the process of cleaning, transforming, and organizing data for use in analysis, mining, and other applications. The goal is to ensure quality and consistency, eliminating errors and redundancies, and preparing the data for use in machine learning models and algorithms.
It is the process of designing, developing, and deploying interactive and customized dashboards using data analysis and visualization tools. These dashboards allow users to quickly and effectively analyze and explore data, identifying patterns and trends.
It is the process of examining, cleaning, transforming, and modeling data to discover patterns, relationships, and trends. It can be performed through statistical techniques, machine learning, and data mining. Its goal is to provide information that can be used to improve decision-making.
At TDW Analytics we have a variety of tools to carry out efficient and accurate data analysis. Our toolset includes powerful statistical analysis and data mining software, which allow us to extract valuable insights from complex data sets. In addition, we use interactive and intuitive data visualization tools, which allow us to present the results in a clear and understandable way. We also have advanced data processing and cleaning capabilities, ensuring that the data used is reliable and of quality.