3 Ways to Achieve Trusted Data Fast
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?
Did you know?
- Without a modern approach to data governance, 80% of organizations will fail at scaling digital business initiatives.
- 60% of organizations say the need for data quality across data sources and environments is their biggest data management challenge.
- 67% of companies that met the GDPR compliance deadline worry about maintaining compliance.
- Data scientists spend about 45% of their time preparing and integrating data for analysis.
Fast access to clean, trusted data is a critical driver of business value.
Here are three ways to make that happen.
1. Speed up data ingestion
Developers spend significant time building pipelines to capture and transform data into formats required by target systems, and additional time when structures and configurations change. All this coding and maintenance contributes to data quality issues and delays insights.
Did you know?
60% of companies say they have too many data sources and inconsistent data.
Hint: Use a metadata-driven framework to automate data pipeline development and save developer time and cost.
2. Get faster insights about data quality
Once data reaches a data warehouse or data lake, it is often inaccurate, duplicated, or incomplete. Organizations need earlier signals that something is wrong, because poor-quality data delays projects and leads to misguided decisions.
Did you know?
- Data quality issues can delay analytics and AI projects by about 40%.
- Organizations estimate the average annual cost of poor data quality at roughly 1.28 million dollars.
Hint: A data quality dashboard powered by machine learning can highlight exactly which data is wrong and where to fix it.
3. Automate data privacy compliance
Organizations are spending heavily on privacy tools yet still feel unprepared for emerging regulations. Managing changing privacy requests across every system and dataset with personally identifiable information is complex and costly, so automation is essential.
Did you know?
The average cost for organizations that experience non-compliance problems is estimated at around 14.82 million dollars.
Hint: A machine-learning-based solution can help catalog and protect sensitive information so you can meet regulatory requirements more efficiently.
Your path to fast, trusted data: Augment
Wavicle’s unified, augmented data management platform automates many data management tasks, reducing project timelines and delivering trusted data at lower cost.
With a single, cloud-based platform, you can integrate data from multiple sources, check data quality, and meet data privacy requirements without coding.
Sources
Gartner, CIODIVE, Anaconda, O’Reilly, Towards Data Science, and Globalscape are cited as sources for the statistics and findings referenced in this guide.
