Today every business wants to take advantage of vast amount of data that has been generated or acquired by IT infrastructure. Many Companies are able to create business value from Data initiative programs but at the same time many companies struggle to get any significant benefit from data even after investing so much of money.
One of the root causes of failure of Big Data initiative is poor quality of source data. Quality of data is a key input in data-driven business. Many companies assume that whatever data they have is already clean. So once they start developing data pipeline, Big Data ETL consultant often complains about incompleteness of data or missing values or incorrect values or bad reference data. This leads to many regression cycles and bad or delayed business decisions.
Typically companies start their Big Data initiative with few sources and as they see value from data, they tend to add more sources which makes Big Data infrastructure more complicated. Adding a data quality solution in the very first phase of Big Data initiative can save lot of regression and increase ROI.
Data Quality solutions include :
- Data Profiling
Understanding statistics and pattern of data.
- Data Cleaning
Removing junk data
- Data Deduplication
Filtering out redundant data
- Data Enrichment
Adding missing values or Correcting incorrect values
- Dictionary Building
Building reference tables from source data
Our Solution :
DataPro is extensive data quality solution backed by Hadoop and Spark.
Talk to us for more information.