The quality of your analytics is only as good as your data. As more companies pair internal data with external data, data integration and preparation are more important than ever.
How can you reduce the time and cost it takes to ingest and clean data while ensuring it meets privacy and regulatory requirements?
Without a modern approach to data governance, 80% of organizations will fail at scaling digital businesses1
60% of organizations say the need for data quality across data sources and environments is their biggest data management challenge3
67% of companies that met the GDPR compliance deadline worry about maintaining compliance2
Data scientists spend 45% of their time preparing and integrating data for analysis4
Developers spend significant time building pipelines to capture and transform data into formats required by target systems. And when data structures and system configurations change (which they often do) it takes even more time to build new connections or reconfigure ingestion rules to capture new data.
All this requires significant coding and ongoing maintenance, which contribute to data quality issues and delay business insights.
60% of companies say they have “too many data sources and inconsistent data.5
Hint: Use a meta-driven framework to automate data pipeline development to save developer time and cost.
Once data reaches a data warehouse or data lake, it’s often inaccurate, duplicate, or incomplete. Organizations need earlier signals that something is wrong with the data. Left unchecked, poor-quality data leads to inaccuracies in reporting, delayed projects, and misguided business decisions.6
Hint: Adopting a data quality dashboard powered by machine learning can ensure you know exactly which data in the data warehouse is wrong and where to go fix it.
Organizations worldwide are spending $8 billion on privacy tools, and yet most admit to being unprepared for emerging regulations. The effort required to manage changes to customer privacy requests by every global regulation in every database, table, and file that contains personally identifiable information is astounding. Organizations need automated solutions to help them get compliant and stay compliant – without spending a fortune doing it.
The average cost for organizations that experience non-compliance problems is $14.82 million8
Hint: A machine learning-based solution can help you easily catalog and protect sensitive information, ensuring you meet regulatory requirements.
Wavicle’s unified, augmented data management platform automates many of your data management tasks, shaving months off your project timelines and delivering trusted data at a lower cost.
faster development of data pipelines
less time spent on data quality checks
less time spent managing data privacy rules
Now you can quickly and easily integrate data from multiple sources, check your data quality, and meet data privacy requirements in a single, comprehensive cloud-based platform – no coding required.
