UNIQUE Data Cleansing Service
Improving the accuracy and completeness of data

Data cleansing is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data. It involves detecting and removing duplicate records, correcting spelling errors, standardizing data formats, and removing irrelevant or incomplete data. 

Why is cleansing important? Your key benefits 

The benefits of data cleansing include: 

  1. Improved accuracy: Data cleansing can improve the accuracy of your data by eliminating errors, inconsistencies, and duplications. Clean data can lead to better insights, analysis, and decision-making.
  2. Increased efficiency: With clean data, you can reduce the time and resources needed to analyze and process your data. This can lead to increased efficiency and productivity.
  3. Better decision-making: Clean data can provide a clearer picture of your business operations, customers, and market trends. This can help you make better-informed decisions and develop more effective strategies.
  4. Improved customer satisfaction: Clean data can help you better understand your customers and their needs, allowing you to provide more personalized and targeted marketing campaigns and customer service.
  5. Reduced cascading costs: Significant reduced costs such as the processing of returned postal, bounced emails, time to search for missing information, etc.
  6. Cost savings: By identifying and eliminating inaccurate or irrelevant data, you can reduce storage costs and avoid making costly business decisions based on inaccurate information. 

In summary, data cleansing is a crucial process that can help organizations to improve data accuracy, increase efficiency, make better decisions, improve customer satisfaction, and reduce admin as well as cascading costs. 

The difference between Cleaning and Cleansing 

In general, 'data cleaning' and 'data cleansing' refer to the same process of identifying and correcting errors, inconsistencies, and inaccuracies in data. However, some people may use the terms slightly differently, depending on their specific context or industry. In some cases, "data cleaning" may be used more broadly to refer to any process that involves preparing data for analysis, which can include tasks like formatting, transforming, and structuring data in a way that makes it more usable. On the other hand, "data cleansing" may be used more specifically to refer to the process of identifying and correcting errors in data, such as removing duplicates, filling in missing values, and correcting formatting or spelling mistakes. 

The difference between Data cleansing and data scrubbing 

Data cleansing and data scrubbing are terms that are often used interchangeably, but they actually refer to slightly different processes.  

  1. Data cleansing: also known as data cleaning, is the process of identifying and correcting or removing errors and inconsistencies in data. This includes tasks such as removing duplicates, correcting misspellings, filling in missing values, and formatting data to ensure consistency. The ultimate goal of data cleansing is to improve the accuracy and completeness of the data, which in turn helps to improve the quality of any analysis or decision-making that relies on that data.
  2. Data scrubbing: on the other hand, is a more comprehensive process that involves identifying and removing any sensitive or confidential information from a dataset. This can include personal information such as names, addresses, and Social Security numbers, as well as financial information or other types of confidential data. The goal of data scrubbing is to protect the privacy and security of individuals or organizations whose data is being used, while still allowing the data to be used for analysis or other purposes. 

In summary, data cleansing is focused on improving the accuracy and completeness of data, while data scrubbing is focused on removing sensitive or confidential information from a dataset. Only Once focusses on data cleansing. 

Characteristics of clean data 

Various data characteristics and attributes are used to measure the cleanliness and overall quality of data sets, including the following: 

  1. accuracy
  2. completeness
  3. consistency
  4. integrity
  5. timeliness
  6. uniformity
  7. validity 

Data management teams create data quality metrics to track those characteristics, as well as things like error rates and the overall number of errors in data sets. Many also try to calculate the business impact of data quality problems and the potential business value of fixing them, partly through surveys and interviews with business executives. 

what we do

Cleaning business data in your applications data involves several activities and increments we must execute to ensure that your databases are accurate, consistent, and complete. 
Such as 

  1. Remove data (deduplication)
  2. Correct data (fix errors, typos)
  3. Enhance data (add missing information)
  4. Update outdated data 
Getting started

In case you want to clean your data and make use of the unique and smart Only Once Data Cleansing service, these are some up front steps you need to make: 

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The pricing of data cleansing services can vary depending on several factors, including the size of the database, the complexity of the data, and the level of customization required.  We can offer a flat fee for a set number of records or tiered pricing structure based on the volume of data to be cleansed. 

It's important to note that while the cost of data cleansing services may seem high, the benefits of having accurate and up-to-date data can far outweigh the cost in terms of improved business outcomes, increased efficiency, and reduced errors and costs associated with bad data. 

 Why Only Once Cleansing hits a 99.9% accuracy/cleaned record 

The sources of up-to-date data used for cleansing databases, in general, are third-party data providers, internal data sources, data enrichment tools, scraping technology, data mining - and machine learning algorithms, and manual labor. However, none of these tools offer guaranteed error-free, up-to-date, and correct data. 

The reason Only Once can achieve 99.9% accuracy is that we request updated data directly from the source, which is the data owner themselves. We take some typos into account with our accuracy. That is our unique value proposition.