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Data Cleansing for Better Analysis & Business Insight

Trifacta’s unique approach to data cleansing

When analyzing organizational data to make strategic decisions you must start with a thorough data cleansing process. Good analysis rests on clean data–it’s as simple as that. Data cleansing is the first step in the overall data preparation process.

All too often organizations lack the attention and resources needed to have an effect on the end result of analysis. Inadequate data cleansing and data preparation frequently allow inaccuracies to slip through the cracks. This is not the fault of the data analyst, but a symptom of a much larger problem of manual and siloed data cleansing and data preparation. Beyond the lackluster and faulty analysis, the larger issue with traditional data cleansing and preparation is the amount of time it takes–Forrester Research reports that up to 80% of an analyst’s time is spent on data cleansing and preparation.

Data cleansing can be difficult, but the solution doesn’t need to be. We have created a 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 or business users to do more with their data by guiding them through the process using intelligent suggestions powered by machine learning. What was once the daunting and overwhelming task of data cleansing, is now made simple with Trifacta Wrangler.

Data cleansing process to remove bad data for better analysis by businesses

 

Trifacta’s solution for data cleansing

Our six-step wrangling process lends itself to a more iterative data cleansing and data wrangling, ultimately leading to a 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 data 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.

To learn more about Trifacta’s unique approach to data cleaning and data wrangling, download our eBook, Six Core Wrangling Activities.

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