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Cloud Data Migration

The trend to migrate existing data to cloud platforms is increasing. Pre- and post-migration data quality assessment is necessary to build trust and to ensure that data migration processes do not introduce defects.

Cloud data warehousing. Organisations that are building new data warehouses are most often turning to cloud data warehousing.

The benefits are substantial:

  • Scalability,
  • Agility,
  • Managed infrastructure, and more

Doing the data quality management where the data is – It simply makes sense to undertake the data quality processing to the locations where data resides. This is more efficient and more secure than moving the data across networks for processing.

Analytic models are highly sensitive to data quality deficiencies, and data science use cases vary widely in both expectations for and definitions of data quality. Data quality assessment and data cleansing are integral parts of the analytic process, which should not be done in isolation from the modelling work.

Recognising that data cleansing and data quality are critical data warehousing processes, and especially valuable technology for cloud data warehousing, Catapult BI will develop the best strategy for your needs.

What’s Next in Data Quality Management?

We can no longer afford for data quality to be an afterthought. Profiling, assessment, and cleansing late in the data lifecycle doesn’t work in today’s high-speed world where real-time data, multiple use cases, and self-service data analysis are the norm.

Data quality must be woven into the analytics lifecycle and the information supply chain, and that is not practical with a wide range of disparate tools and a steep learning curve. A small set of familiar and stable tools that work across on-premises, hybrid, and cloud environments is the new necessity for Data Quality.

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