Discovering exactly what is in your data and how it might be useful for different analytic explorations is key to quickly identifying the value of a dataset.
Trifacta’s Data Profiling features provide immediate visibility into unique elements of the data set like data distributions and outliers to inform the transformation and analysis process.
Examining your data quickly to ensure the fit and accuracy of data for analysis assists with early avoidance of erroneous analytic results. Understanding as early as possible, gaps in data collection and unusual skew of the data is critical to assessing the quality of your data.
Trifacta’s automated detection of data types, value distribution and missing or inconsistent values provides the user with immediate cues to a dataset’s fit and trustworthiness.
Data lacking human-readable structure is difficult to work with using traditional reporting, analytic and predictive analytic applications. Even tabular, well-structured datasets often lack the proper formatting or appropriate level of aggregation required for joining with other data sets or running more advanced analytics.
Trifacta uses data inference techniques to introspect the data and automatically apply initial shaping and metadata recommendations for the user. This greatly accelerates the transformation process. Users can quickly un-nest and iterate on the shape of their data in preparation for the dataset’s downstream use.
Single datasets on their own rarely contain all of the required information for business analysis. To make real decisions with data, you must first enrich a dataset by making the data human-readable with lookups to data dictionaries, join multiple data sources to provide additional context, or create derived fields with calculations that highlight business opportunities or gaps.
Trifacta’s data enrichment features make standardizing data, joining datasets and aggregating data outputs to the right level, faster and more accurate.
Enabling your analysts to benefit from the work of other data professionals across the organization is critical to fostering a data-driven culture and achieving real business impact with Big Data.
Trifacta often starts with exploratory data analysis, but once a useful data structure is identified, it can be shared with others throughout the organization in the form of a repeatable transformation script that encourages the organic evolution of shared data transformations throughout the organization, taking what was once a top-down process and making it a grassroots effort to share common data definitions throughout the enterprise.