ETL
Your organization needs accurate data to operate efficiently, but cleansing data is a tenuous and technical task, leading to complexities in the data cleansing process that are hard to follow and often lacking visualization.
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Challenges associated with ETL processes
Data Complexity
ETL processes involve various data sources with different formats, structures and schemas that can be complex to transform and harmonize.
Manual Data Preparation
Manual data preparation during the transformation phase can lead to inefficiencies and errors.
Quality Issues
ETL processes need to address data quality problems, like missing values, duplicates and inconsistencies to ensure reliable insights.
Inefficient Workflows
Inefficient ETL workflows often result in slower data processing and analysis during and after the process.
Manual Entry/Extraction
Manual data entry and extraction takes significant amounts of time, especially when dealing with large volumes of data.
Integration Challenges
Integrating data from disparate sources and systems consistently maintaining data integrity is challenging
Unlike traditional methods that involve a lot of coding, Data360 Analyze offers a visual interface that speeds up data integration and analysis. This platform is not only great for combining data quickly but also excels in handling complex business processes through a user-friendly visual setup.
Key Data360 Analyze features
Multi-source Analysis
Schema-free Analysis
Direct Publishing
Acquire and cleanse data from any source or sources essential to the analysis simultaneously.
Discover, add or change data sources needed for your analysis on the fly and without pre-configuration.
Publish directly to QVX and TDE file formats and visualize quickly with your respective BI tool.
Efficient Transformations
Split, combine and merge data from multiple sources with ease through a single interface.
Transparent Processes
Retain visibility over your data and its attributes throughout each and all stages of an ETL process.