Data cleansing

As a business expands, its data asset not only increases but also becomes stale over some time. Data cleansing is the process of identifying and removing inaccurate or irrelevant details from a database to maintain data accuracy. Our data cleansing service optimizes database management to improve efficiency and aid business operations.

Refine your business goals with data cleansing solutions

The efficiency of a business depends on the quality of data that is being analyzed to make informed decisions. Our ultimate data cleansing techniques De-duplicate, clean, standardize, verify, and validate data to improve the quality of data that reaches you. utilize an enriched database to run targeted campaigns and make quick conversions.

Data cleansing experts at DataCaptiveTM work towards delivering an error-free database to improve and centralize your business operations management efforts.

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Data hygiene services that add value to your business

           

B2B contact data cleansing

B2B company data cleansing

Migration, standardize and monitor

Merging and Rebuilding

Duplication and normalization

Appending and verify missing data

Data cleansing and scrubbing cycle

A business must have a strategic approach backed by quality data to connect with prospects. Acquiring a comprehensive data scrubbing services can give your business the competitive advantage it needs.

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The 5 Steps Data Cleansing Process Includes:

Collect and analyze data

The data is collected and analyzed to identify the data format, inconsistent records, and to check for data anomalies present in the database.

Collect and analyze data

The data is collected and analyzed to identify the data format, inconsistent records, and to check for data anomalies present in the database.

Segmenting critical data fields

The data is segregated from the correct data to avoid any data altercations. This method also helps to erase irrelevant data fields and identify gaps in the database.

Segmenting critical data fields

The data is segregated from the correct data to avoid any data altercations. This method also helps to erase irrelevant data fields and identify gaps in the database.

Eliminate duplicate values

After the successful collection and segmenting of data, duplicate and inconsistent values are identified and eliminated to resolve redundancy in the database.

Eliminate duplicate values

After the successful collection and segmenting of data, duplicate and inconsistent values are identified and eliminated to resolve redundancy in the database.

Standardize the cleansing process

Once the data is de-duplicated and validated, the information is standardized to a common format for easy integration into any CRM. The standardized data can be utilized by multiple departments to run data-driven functions.

Standardize the cleansing process

Once the data is de-duplicated and validated, the information is standardized to a common format for easy integration into any CRM. The standardized data can be utilized by multiple departments to run data-driven functions.

Integrity checks

The cleansed data further undergoes multiple stringent checks to maintain data quality. Our experts also audit the responsiveness of the contacts to ensure that the data is actionable. Data profiling summarizes the quality of data.

Integrity checks

It is a process that aims to simplify reoccurring marketing activities such as social media messages, emails, follow-ups, etc.

It not only covers the marketing aspect of the business but also makes marketing measurable.

Why choose data cleansing?

DataCaptiveTM’s Data Cleansing service helps you achieve your business goals and positively impacts your result by amending incorrect data and eliminating duplicate records.

Benefits of B2B data cleansing:

Detect, compare and delete duplicate records

Improves Retention and Customer Acquisition Actions

De-duplicating the data can increase the storage space in your CRM

Streamline productivity with improved Business operations

Minimizes compliance and data protection risks

Informed business decisions to generate high ROI

Invest in our data cleansing services

DataCaptiveTM  is the partner you need to exceed your goals.

FAQ

  • What is data cleansing? why is it important?

    Data cleansing, also known as data cleaning, data scrubbing, or data wash, is the process of detecting and correcting Incomplete, Incorrect, and duplicated data from a database.

    DataCaptive™'s GDPR compliant data cleansing process guarantees actionable and relevant data-driven insights like full name, contact details, business email IDs, occupational codes, organization name, social media pofiles and many more. It improves your brand efficiency, minimizes budget on marketing campaigns, maintains sound customer relationships, and avoids fines levied due to breached data regulation.

  • How is data cleansing different from data enrichment and data profiling?

    Data profiling is the first process in any data project, where data scientists review the data, understand its structure, content, and identify the potential for future actions.

    The difference between data cleaning and data enrichment is that while data cleaning involves resolving discrepancies and updating data by discarding old or inaccurate data, data enrichment is enhancing data quality by filling missing information for a complete profile.

  • What methods are used for data cleansing?

    Our data engineers' methods for data cleansing are removal of unwanted, duplicate, and irrelevant data; fix data structure; filter out outlier data (data that does not match any category); add missing data and input missing values.

  • What are the different types of data issues?

    The most common data issues that our data team faces are manual data entry errors, OCR (optical character recognition) errors, incomplete information, ambiguous information, duplicate data, and data transformation errors.

    We deliver 100% accurate data by tackling these errors, both manually and through high-tech AI tools.

  • Is data cleansing in data mining and data science the same?

    Data science is the process of deriving insights from both structured and unstructured data for qualitative analysis. Data mining is a field of study under data science that extracts relevant information in a dataset to discover and decode future trends.

    The process of data cleaning, however, is universal in both the fields.

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