What is data quality and why is it important?
Data quality can be defined as the suitability and accuracy of data used by an
organization. Consider the scenario in which a database was created when little thought
was given to obtaining complete and accurate information. Without complete and
accurate information you automatically compromise the timing & quality of your
organizations work. Data quality issues will remain a major concern for organizations
unless they pro-actively look at their data quality management solutions. Data quality is
becoming increasingly more important for companies of all sizes. Poor or incorrect data
can create significant problems and lead to a lack of customer confidence.
5 Tips to improve data quality:
Centralization: Centralizing data is a must-have in many industries, as it streamlines data management and improves the quality of information.
Consistent documentation: Outdated documentation delays data consumers to be able to provide actionable business insights therefore consistent documentation hat provides context is key for all organizations.
Automation: Automate the process of adding context to new and existing data. Algorithms that learn to identify data classes through machine learning can reduce the time your team spends tagging and classifying data!
Data transparency: Enable self-service data access so users can quickly and easily find the data they need to do their jobs.
Access Management: Protect data accuracy with appropriate governance processes and controls. Give the right people access at the right time.
Yes you probably guessed it, Datalogz offers all of that and more! So come check us out to find out more!