How do you avoid the big data pitfall, deliver actionable big data BI and pave the way for artificial intelligence (AI)? Our three-pillar QA and testing process has you covered.
Pillar 1: Data Profiling
To leverage big data resources, you need to know more about data itself. Where did it come from? What characteristics does it display? How does it relate to other data? Our data profiling tools collect critical characteristics about your source data using value distribution graphs or summary statistics for every data column. Output comes in the format of your choice: HDFS, Cassandra or cloud-compatible and ready for loading into your existing data infrastructure.
Pillar 2: Data Preparation
Not all data is created equal. As noted above, missing, empty or redundant data fields cause aggregation issues and can increase the total cost of big data initiatives as IT teams and managers are forced to identify and then remedy missing or incomplete datasets.
XBOSoft’s data preparation combines both your business requirements and feedback gained during data profiling to deliver specific column aggregation, new column creation and/or transformation as needed — giving you the clean, complete data required to derive statistical results, perform drill-down analysis and begin layering on big data AI solutions.
Pillar 3: Data Validation
Want better data? Implement effective big data QA. Our data validation services identify data value limit ranges and deliver automated notifications to ensure you’re never in the dark about the quality and consistency of your data.
By combining source, process and output validation, we’re able to deliver superior QA that empowers big data BI while avoiding delays, limiting confusion and reducing the time between data collection and actionable insight.