Before deeper analysis can be done, you must summarize the characteristics of a dataset: number of cases, variables included, missing observations, and any prospective hypotheses the data might support.
When reviewing the data, the analyst hopes to immediately zero in on variables that will lead to valuable insights about their business. If several data points correlate, those may be great candidates for in-depth analysis. By skipping this first exploratory step, the analyst won’t immediately understand the key issues or won’t be able to guide the deeper analysis in the right direction.
Data Exploration: Using Visual Tools
Historically, analysts have used statistical software for data exploration, but now most use data visualization software and tools. Visualizing data through the use of dashboards, graphs and charts, analysts are better able to quickly discover the most relevant aspects of their datasets.
The best tools don’t just help visualize data, they interact with their analyst as they perform data exploration. Instead of just spitting out limited reports, the best data exploration tools now interact with the analyst and their team, allowing everyone to collaboratively annotate and search datasets, make recommendations for visualizations, and even automate the exploration process through machine-learning.
Interactive Exploration with Trifacta
Trifacta’s unique data wrangling software was designed with data exploration in mind. Trifacta offers easy-to-use, intelligent and interactive visual analysis that improves the users ability to understand data immediately. Trifacta automatically presents the user with the most compelling and appropriate visual representation based on their data for example, geographic elements are presented as maps. Every profile is customized and completely interactive – allowing the user to simply select certain elements of the profile to prompt transformation suggestions.