In business analytics, data cleansing is the process of identifying and correcting inaccurate or incomplete data within a dataset. It is an important early step in preparing data for analytics because, quite simply, bad data leads to inaccurate insights, poor decisions, and unfortunate outcomes.
It typically occurs after an organization identifies and gains access to new data sources but before data from those sources is used in their analytics processes. This applies to business intelligence (BI), which depends on large quantities and varieties of reliable data to drive decision-making within organizations.
Data cleansing starts with identifying data sources, types, and any inconsistencies or errors in the data itself. This is followed by data validation, which involves data cleansing to verify data accuracy, integrity, and data quality. Finally, data transformation is used to reformat data so it can be easily loaded into analytics systems for analysis.
Traditional methods involved manually scanning data sources and resolving data errors such as incorrect or missing values, data duplication, data inaccuracies, data formatting errors, and data integrity issues. But “data preprocessing such as cleansing and formatting it for analysis is time-consuming,” says Deloitte. This is especially true when those processes rely on humans alone: “Some estimates suggest that this can account for 80% of the effort in data analysis projects.”
As data analytics has grown in complexity and importance, cleansing software and techniques have become essential tools for data-driven businesses. These include machine learning (ML) algorithms and data scrubbing tools that can automate aspects of identifying, cleansing, and formatting data from disparate data sources.
Specifically, these augmented analytics tools will optimize human-controlled cleansing processes by automatically detecting data errors and pinpointing or correcting them. As Gartner describes, “A data quality tool with augmented capabilities can simplify the core data quality tasks like matching, linking, merging, deduplication, and cleansing with a higher level of accuracy than humans.”
Tools may also include data enrichment features, such as data classification and tagging. Using these tools, data scientists can clean and prepare data faster and with greater success. Emerging tools can also automate data integration and analysis, reducing organizations’ reliance on data scientists and other technical experts.
Cleansing adds value for both individual decision-makers and their organizations. That’s because they ensure BI tools deliver insights that have a foundation in truth. Specifically, data cleansing for BI:
Emerging business analytics tools—such as decision intelligence (DI) platforms, which make data-driven insights available to anyone in the organization in a governed way, without the direct support of technical experts—still require cleansing before data analysis. However, DI, in particular, streamlines data preparation and data wrangling tasks, delivering data-driven insights faster to a wider variety of team members based on each individual’s needs.
The DI platform from Pyramid Analytics provides data-driven insights in a governed, self-service manner to team members of all skill levels. In preparing these insights, the platform offers a wide range of automated data cleansing and data preparation capabilities. Beneficiaries can include data scientists, business analysts, and even frontline employees.
Contact us today to learn more about how we can help your organization with your broader analytics initiatives.