There can be no two views on the need for data quality. Yet, data quality itself is a qualitative term. Organizations do not have a clear view on what they consider quality data. The cloud draws attention to this fact and helps enterprises develop strategies that measure and report on quality of data that is used for decision making. It puts in place processes that drive data quality initiatives and defines the metrics that scrutinize and measure the qualitative aspects of data.
Here are a few ways in which the cloud helps streamline data quality:
Cloud software based data quality validation solutions constantly use business rules and business processes to define the quality of data.
Does the data expose the business rule? Is it relevant to the business process? The relationship between the data, the rule and the process must be well established. The core KPIs (Key Process indicators) must be segregated and the metrics that drive them must isolated for underpinning the kind of data quality that is desired. However, the quality definition should not be tied to a specific application or process, but reverberate through all downstream applications and processes. Data that is copied, reused and repurposed across applications must be checked and repeatedly validated against the rules to prevent data errors and anomalies that can be propagated as the data is requisitioned by one application and then another.
Data e-discovery and profiling are automatic in the cloud.
Data profiling at granular levels helps organizations understand the current state of the data and course corrections that may be required for ensuring quality data and reducing business risk. Data discovery tools in the cloud are optimized for business collaboration, so that businesses can easily understand and handle data anomalies. These tools also help evaluate the differences between data sets that have been created over time.
Multiple data-quality-process mappings will ultimately yield a holistic view of the data impact on the business.
Poor data quality efforts in the cloud or otherwise can produce drastic business effects. While the task of identifying quality data for a task on hand can be daunting, the identification will yield extremely satisfying and dramatic results. When time frames for the metrics are computed and implemented, cost savings can be traced back to data-quality-process mappings. Interpretation of trends and metrics will be facilitated. Risks and issues will be quickly visible as manifestations of poor data quality levels.