What Is Data Cleansing?

Understanding Data Cleansing

Many modern-day institutions rely on large volumes of data in various formats, from different sources, and managed by diverse actors and departments. Cleansing data ensures you can trust data quality, make accurate decisions and create successful strategies based on precise insights and analytics.

Data Cleaning Techniques or Steps

Key steps or techniques you can follow for comprehensive data cleaning include:

• Inspection and auditing
• Profiling
• Duplicate removal
• Anomaly or error detection
• Data integration and standardization
• Validation
• Cleansing reporting

Automated tools enable IT professionals and data professionals to execute efficient cleansing techniques.

Implementing Data Cleansing: Challenges, Best Practices and Tools

Implementing data cleansing requires accuracy, consistency and rigorous dedication to quality. Your organization’s cleaning efforts should be a regular part of your data governance strategy, implemented at stages including data extraction, transformation, loading and application.

Data Cleaning Challenges and Best Practices to Solve Them

Some challenges to effective implementation you may encounter and best practices to address them include:

• Managing high-volume systems: Manual cleansing becomes difficult in modern, data-heavy ecosystems. Automation can enable IT teams to handle large data volumes and reduce cleansing errors.
• Getting buy-in from organization actors: Sponsorship from organizational leaders like executives and managers plays a vital role in maintaining data quality. Aim to create a culture that understands the value of data and has policies for data input standardization and systematic care.
• Encountering data silos: Centralized data storage systems like lakes and warehouses can help tackle silos accompanied by set data standards across departments.
• Dealing with data inaccuracy: Implementing a range of cleansing techniques can ensure that where one step makes an error, another can account for that issue. Design comprehensive and regular data cleansing routines for maximum efficacy.
• Variant data sources and types: Utilizing modern technologies and systems that can adapt to your organization’s various data formats and sources ensures you can manage all your business data.

Big Data Cleansing Solutions

Big data systems present many challenges, including high data volumes, variant sources and data types, and too many actors. For these reasons, big data cleansing may require unique solutions. Automated systems can process diverse types, volumes and formats of organizational data and combine them into unified structures.

ETL- and ELT-compatible tools like data warehouses and lakes can simplify large-scale data cleansing. These systems’ transformation capabilities enable them to cleanse data during processing before or after your system loads it onto your storage database, providing an excellent opportunity to identify errors and issues at the beginning of your data’s life cycle.

Tools for Data Preparation

Organizations use many different tools to cleanse and prepare data for analytics, business intelligence and strategy design. For example, Anomalo can provide validation software for data accuracy testing and AI anomaly and error detection. Other solutions can complete tasks such as data cleaning and transformation, CRM cleaning, and ETL processing.