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Data Cleansing

Trifacta’s unique approach to an age-old data problem

When working with organizational data, one quickly realizes that strategic decision-making rests upon thorough data cleansing. Without clean data, you cannot have good analysis—it’s as simple as that—which makes data cleansing, one step of the larger data preparation process, a critical task.

What happens all too often in organizations is a different story—data preparation and data cleansing lack the attention and resources they deserve to have a more meaningful effect on the end result or, worse yet, inadequate data cleansing allows inaccuracies to slip through. This is to no fault of the individual analyst, but a symptom of the larger problem of manual, siloed data cleansing and data preparation. But beyond lackluster or faulty analysis, the biggest problem with traditional data preparation and data cleansing is the amount of time that it takes—Forrester Research reports that up to 80% of an analyst’s time is spent on data preparing and data cleansing for analysis.

Trifacta knows data cleansing can be difficult, but the solution doesn’t need to be. Trifacta has created an entirely new approach to data preparation that helps organizations get the most value out of their data with proper data cleansing. With its visual, user-friendly interface, Trifacta’s data wrangling software allows non-technical users wrangle data all of shapes and sizes for sophisticated analysis. Unlike any other data preparation product, Trifacta empowers non-technical users to do more with their data by constantly guiding them through the process using intelligent suggestions powered by machine learning. With Trifacta’s software, data cleansing, what once was a daunting and overwhelming task, is made simple.

With a new means to wrangle data comes a new process to do so. Trifacta’s six-step wrangling process lends itself to more iterative data cleansing and data wrangling, and, ultimately, leads to more accurate analysis. The steps involved include:

  1. Discovering helps the user understand what’s in the data and how it can be used effectively for analysis
  2. Structuring makes working with data of all shapes and sizes easy by formatting the data to be used in traditional applications   
  3. Cleaning involves removing data that may distort your analysis or standardizing your data into a single format
  4. Enriching allows the user to augment the data with internal or third party data to enhance the data for better analysis
  5. Validating brings data quality and inconsistency issues to the surface so the appropriate transformations can be applied  
  6. Publishing allows the user to deliver the output of your data and load into downstream systems for analysis

With Trifacta, and a six-step approach to data cleansing and preparation process, it’s easy to remove or correct records that are inaccurate, missing, or corrupt, whether from a database, a table, or a set of records. This allows organizations to dramatically reduce their time spent on data cleansing, and leads to better, more accurate analysis.  

For more information on Trifacta’s unique approach to data cleaning and data wrangling, download our eBook, Six Core Wrangling Activities.

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