Big data quality

In every data management and analytics project, data quality can be a significant barrier. Typos, various name practices, and data integration challenges can all cause complications. However, data quality is much more important in big data applications since there is a considerably larger amount, diversity, and velocity of data.

Considering big data quality challenges can arise from a variety of applications, data kinds, platforms, and use cases, it is suggested that a 4th V for veracity be included to big data management activities.

Importance

As a result of real-world system results, big data quality flaws can lead to not only erroneous algorithms but also significant accidents and injuries. At the absolute least, corporate users will be less trusting of data sets and the apps developed on top of them. Furthermore, if data accuracy and quality play a role in front-line business decisions, organizations may be exposed to government regulatory inspection.

Only if adequate policies and support structures to regulate and monitor data quality are in place can data become a strategic asset.

Poor-quality data can drive up data management expenses due to the constant cleanup, additional personnel requirements, and compliance difficulties. It can also cause problems with decision-making and planning.

What makes big data different in terms of data quality

For as long as people have been acquiring data, data quality has been a concern. Big data, on the other hand, alters everything.

Consider a team of 100 people that generates and processes a couple gigabytes of client data every day. Managing this volume of data necessitates a new strategy to assuring data quality for big data, which must take into account the following factors:

• Data forms that are complex and dynamic

  
  

  Among event kinds, user groups, program versions, and device types, big data might have various dimensions. Performing checks on specific slices of data, which might easily number in the hundreds or thousands, is required to map out the data quality problem in a meaningful way. When new events and characteristics are added and old ones are deprecated, the structure of data can also change.

  
  

• Issues with scaling

  
  

  Import-and-inspect designs that functioned for traditional data files or spreadsheets are no longer practicable. Big data quality practices must be developed for both classic data warehouses and current data lakes, as well as streams of real-time data.

  
  

• Large amount of data

  
  

  It's difficult to effectively inspect new data in big data systems. To ensure data quality for big data, quality metrics must be developed that can be tracked automatically against alterations in big data systems and use cases.