Data Preparation Platform uses Machine Learning

​7Park Data, a provider of data transformation software, has announced the launch of its data preparation platform, powered by machine learning models trained for more than seven years on proprietary data sets.

 “Businesses know that they must harness their own data to compete and succeed, although most are only able to use around one percent of the data they have in their own walls,” said Brian Lichtenberger, CEO and co-founder of 7Park Data.

“When we built our first datasets in 2012, we had many of the same challenges that companies face today - data is unorganized, incomplete and full of duplicates, and existing solutions lack the flexibility, speed and accuracy required to be truly effective. We’re excited to bring our platform to other companies so they can unlock the potential of their own data assets.”

Th platform creates high-quality, decision-ready data with three key solutions:

Data Deduplication and Matching: Clean, enrich and link data across databases. The platform has an F1 score greater than 90% on its matching and linking algorithms, and its natural language processing models are claimed to be 3x more accurate than Amazon Comprehend and other leading self-service data prep tools. With better quality data, enterprises can:

  • Improve operational efficiencies by linking and resolving entities across databases and establish consistent taxonomies;
  • Optimize compliance and risk management by resolving data inaccuracies and automating alerts;
  • Build customer 360 profiles to foster better experiences, services and products.

Intelligent Document Processing: Automate document processing of unstructured data to reduce manual labour on repetitive tasks, resulting in lower associated overhead cost and improved operational efficiencies.

Insight Engine: Boosts research and analytics productivity by reducing time that analysts, data scientists, and anyone who relies on accessible data spend on search and discovery, and increase the quantity and quality of insights.

To learn more and schedule a demo, please visit [] or explore models available in AWS Marketplace []