Transform data, ensure quality, and automate data pipelines at any scale.
The Basics of Data Discovery
Data discovery starts with a sloppy crowd of data coming from multiple directions in a mess of forms. And yet, the process creates digestible information and ends with applicable insights. How does it happen? Data discovery compiles data from multiple sources, and then configures the data so it can be understood and examined. The steps in data discovery can be broken down a few different ways, but they all include (1) data preparation, (2) data visualization and (3) data analysis.
Using Data Discovery to Visually Explore and Understand Diverse Data
When working with complicated data, data discovery is a critical step in the data preparation process. Completing data discovery step allows you to gain some initial understanding as to what is actually in the dataset and how it can be leveraged for analytics and valuable business insights.
The process of data discovery can be difficult when working with various datasets that are not well structured to begin with or that are too large to use with common tools such as excel. For an analyst working with a new or third-party dataset, the faster they’re able to perform the process of data discovery, the faster they’re able to show value from their work.
The Benefits of Using Trifacta for Data Discovery
Trifacta helps reduce the time and resources needed to perform challenging data preparation tasked and helps to accelerate data discovery process. Trifacta helps by:
Providing users with the best visualization for each specific type of data automatically
Enabling analysts to interactively filter and find relationships across attributes in a dataset
Identifying potential data quality issues such as missing or mismatching values
The Process of Data Discovery with Trifacta
Trifacta has developed a unique end-to-end data wrangling tool designed to help data analysts or business professionals do the data discovery process of taking raw data sources and transforming them into the appropriate format for analysis–right from the desktop. With Trifacta the user is able to see how the data will can be used for different types of analysis. Trifacta has a six-step iterative data wrangling process that leads to a more accurate analysis. The steps include:
Discovering – evaluate and explore data to quickly determine the value and potential of a datasets
Structuring – change formats or schemas with predictive transformations that allow you to automatically split data into rows and columns
Cleansing – identify data quality issues, such as missing data or mismatched values and apply the appropriate transformation to correct or delete these values from the dataset
Enriching – execute lookups to data dictionaries or execute joins with disparate datasets using machine learning to rapidly identify appropriate join keys across diverse datasets
Validating – check and correct any missing or mismatched data before starting analysis
Publishing – deliver output to data analytics tools or downstream analytic users
Learn more about how Trifacta accelerates data discovery
To learn more about how Trifacta accelerates data discovery and how it ties into the broader data wrangling process, we invite you to download our free ebook Six Core Data Wrangling Activities: An introductory guide to data wrangling with Trifacta.