Datawatch Monarch Swarm Features New Tools to Drive Decisions

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The enterprise data preparation platform gives teams the tools to create, discover and share trustworthy data models for data-driven decision-making

Datawatch Corporation has announced the latest version of Datawatch Monarch Swarm, a team driven enterprise data preparation platform. Monarch Swarm is designed to make collaboration faster and governance easier. The platform gives teams the tools needed to create, find, validate and share governed, trustworthy data sets and models. The end result is improved collaboration and decision-making.

Self-service analytics’ rise in popularity was supposed to be a boon to businesses but in many cases it has backfired. IT no longer controls data usage, analysts are working alone, and the result is a widespread lack of confidence in data and insights. Datawatch says that Monarch Swarm is designed to eliminate that through its ease of use and collaborative features.

“The explosive use of data across the business has elevated the importance of having it readily available for any operational or analytical needs. But, the seismic shift is not about one individual’s needs; rather, it’s about the requirements of the entire business,” said Mark Smith, CEO and chief research officer at Ventana Research. “The need to collaborate and work together across the business – and securely with IT – has driven organizations to look for more than just self-service data preparation. They now seek technology that works across the entire enterprise,” Smith said.

According to the company, key features include the following:

  • Cloud-ready Data Preparation– Provides robust data preparation for the masses – anytime, anywhere – including automated and scheduled data extraction, cleansing, blending, transformation, enrichment and exports.
  • Data Marketplace – Enables users to search and browse secure and governed cataloged data, metadata and data preparation models indexed by user, type, application and unique data values.
  • Data Socialization– Promotes the socialization and reuse of models, curated data and analytics outcomes, and includes social features, such as user ratings, comments and popularity, to help users make better decisions about which data to leverage for analysis. Users can also like, follow and subscribe to colleagues to learn how they are using and rating data for preparation and analysis.
  • Machine Learning– Facilitates data discovery with “smart recommendations.” Machine learning technology identifies patterns of use and success, performs data quality scoring, suggests relevant sources, and automatically recommends likely data preparation actions based on user persona.
  • Data Collaboration– Drives awareness of what data and assets are being created and by whom; enables creators to know how people are using their models; and allows administrators to see who is contributing and making an impact.
  • Trusted Data– Identifies sanctioned, curated data sets, ensuring analysis is fueled with secure, governed, quality data, sourced by experts.
  • Data Governance– Applies governance features, including data masking, data retention, data lineage and role-based permissions, to uphold corporate and regulatory compliance, and enhance trust in data, analytics processes and results.
  • Gamification and Visibility – Includes ranking contributions, social scoring and gamification to drive participation and contribution.

The latest version of Datawatch Monarch Swarm is generally available.

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

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