Today the success of every enterprise is directly linked to the data that it consumes for making its business decisions. No data is perfect and usage of poor quality data can lead to inaccurate and untimely decisions resulting in increased operational costs, loss of customers and negative brand image. Recent Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million losses per year. Enterprise data architectures are moving from an Extract-Transform-Load (ETL) to Extract-Load-Transform (ELT) strategy. This will result into accumulation of more and more data and at a much higher speed than ever before.
Traditional Approach: A typical approach to build a data quality solution involves collecting requirements from business users and then handing it over to technology team which in turn implements using database procedures or any scripting language. But to cater this kind of complexity at scale, the traditional approach wont suffice and can lead into longer implementation cycles and sub-optimal routines taking longer execution times. It may also have limitations in terms of handling non-standard data structures.
Building Robust Data Quality Solution involves handling these challenges in a cost effective and an efficient manner. Here are few things we suggest to keep in mind while designing.
Inferyx Analytics Platform provides these capabilities out of box to implement a fully configurable data quality solution without writing any code. It can cut down your project delivery timelines by 50-60% and provide a very low maintenance solution. For more details, please contact Inferyx Inc. and we would be happy to provide you a demo of the platform to showcase its capabilities on how to build a robust data quality solution for your enterprise.