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Streamline Data Discovery and Analysis with Trifacta

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 Wrangler 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:

  1. Discovering – evaluate and explore data to quickly determine the value and potential of a datasets
  2. Structuring – change formats or schemas with predictive transformations that allow you to automatically split data into rows and columns
  3. 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
  4. Enriching – execute lookups to data dictionaries or execute joins with disparate datasets using machine learning to rapidly identify appropriate join keys across diverse datasets
  5. Validating – check and correct any missing or mismatched data before starting analysis
  6. Publishing – deliver output to data analytics tools or downstream analytic users

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.

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