Data cleansing tools, also known as data scrubbing tools, update inaccurate data, standardize formats across datasets, and de-duplicate entries to improve analytics outcomes as well as sales outreach efforts.
Additionally, data cleansing tools are highly sought after for data used and stored across ERP solutions and other operational software modules, to facilitate seamless workflows and system migration. However, these tools are agnostic by nature, and are suitable for any use case that needs accurate data.
Key features of data cleansing tools include:
- Data correction: Includes updating old data or data that isn't functional anymore, by identifying new and correct entries across other datasets (internal or external).
- Data format standardization: Creating a uniform format for necessary fields, to maintain consistency throughout the database.
- Data de-duplication: Identifying and merging duplicate fields, so only one primary record is available.
- Data quality checks and verification: Many data cleansing tools incorporate human agents to manually assess the overall quality of cleaned data. For data belonging to individual persons, manual verifications are also carried out in some cases to ensure contact details are accurate, and individuals are actively available over recorded phone numbers and email addresses.
Common Software Integrations With Data Cleansing Tools
- CRM software: One of the most common integrations, as data cleansing tools update outdated customer records and de-duplicate redundant entries.
- Customer Experience (CX) tools: Data cleansing tools offer potential for any CX tool (from Customer Data Platforms to contact center software) as customer analytics and customer service always need up-to-date information.
- Sales prospecting tools: While many leading sales prospecting software providers offer in-built data cleansing capabilities, dedicated data cleansing tools can offer an extra round of updates to improve accuracy rates.
- Sales intelligence tools: These tools rely on accurate data that has been refreshed by data cleansing tools for analyzing leads, revenue and agent performance.
- Business Intelligence (BI) software: Big data, and any form of raw/unfiltered data is often the starting point for analytics carried out by BI platforms. These require comprehensive cleansing and updating, before any processing can take place.
- ERP software: Data cleansing tools are particularly resourceful for ETL, ahead of complex ERP migrations. Additionally, clean data is a requisite for both day-to-day operations and analytics that are facilitated by ERPs.
Data Cleansing vs Data Enrichment: What's the Difference?
While data cleansing focuses on correcting inaccurate entries and standardizing data formats, data enrichment appends new items to existing data. While both terms are used interchangeably, they are each distinct in their respective functions.
In other words, data cleansing solely aims to keep data accurate. On the other hand, data enrichment aims to increase insights from existing data by adding more fields and values, so teams have greater context when using that data for sales outreach, customer engagement, or analytics.
