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Data Cleansing

Data Cleansing also known as data cleaning or data scrubbing, is the process of identifying and correcting or removing inaccuracies, inconsistencies, or missing data in a dataset. It is an important step in the data preparation process, as it helps to ensure that the data is accurate, consistent, and complete, which is necessary for effective data analysis and decision making.

There are several different techniques that can be used to perform data cleansing, including:

  1. Identifying and correcting errors: This involves identifying and correcting errors in the data, such as spelling mistakes, incorrect data types, and incorrect values.

  2. Detecting and handling missing data: This involves identifying and handling missing data, such as replacing missing values with suitable estimates or deleting records that are missing critical information.

  3. Removing duplicates: This involves identifying and removing duplicate records from the dataset.

  4. Standardizing data: This involves ensuring that data is consistent and follows a set of standards or rules, such as using a consistent format for dates or ensuring that all text is in the same case.

  5. Enriching data: This involves adding additional data or information to the dataset to make it more valuable and useful for analysis.

Data cleansing is an important step in the data preparation process because it helps to ensure that the data is accurate, consistent, and complete, which is necessary for effective data analysis and decision making. If data is inaccurate or corrupt, then workflows and algorithms become unreliable. Companies who are innovating with machine learning and artificial intelligence rely on clean data. According to current analyst projections, the volume of data is increasing from 79 zettabytes in 2021 to 181 zettabytes in 2025.

 

What is the problem?

Common challenges to ensure the data is clean and ready for use as soon as it enters the organization include:

  • Corrupt data
  • Inaccurate data
  • Invalid data
  • Data is in an inconvenient format
  • Data is duplicated
 

How do we solve this?

InfinyOn Cloud facilitates data cleansing with a premiere feature called SmartModules that allows users to have full control over their streaming data by providing a programmable API for inline data manipulation. Filters, Maps, FilterMaps, ArrayMaps and Aggregate SmartModules are user-defined functions and offer flexibility for building and cleansing your data pipelines for any use-case.

  • SmartModule filters are used to examine each record in a stream and decide whether to accept or reject it.
  • SmartModule Maps are used to transform or edit each record in a stream.
  • SmartModule FilterMaps are used to both transform and potentially filter records from a stream at the same time.
  • SmartModule ArrayMaps are used to break apart Records into smaller pieces.
  • SmartModule Aggregates are functions that define how to combine each record in a stream with some accumulated value.

Data Cleansing is essential for any organization that wants to make the most of its data. InfinyOn Cloud offers a comprehensive set of tools that allow you to automatically and quickly clean, filter, and transform your data in real-time. By registering for a demo, you’ll get a firsthand look at how our platform can help you increase data quality, reduce costs, and improve data-driven decision making. Don’t miss this opportunity to see how InfinyOn Cloud can revolutionize the way you handle data cleansing. Register for a demo today.