Team-driven, Enterprise Data Preparation Platform Enables Slemma Customers to Create, Discover and Share Trustworthy Data for More Strategic Decision-making
BEDFORD, Mass. – November 8, 2017 – Datawatch Corporation (NASDAQ-CM: DWCH) today announced that Slemma, a data analytics tool for small and medium-sized businesses, is integrating Datawatch Monarch Swarm into its data visualization offering to enhance data access, data quality, information sharing and team collaboration, while enforcing IT governance.
Slemma’s data visualization tool integrates with more than 70 cloud service, cloud storage and data warehouse solutions, enabling customers to connect to their preferred offering, visualize their data and share their insights with both team members and clients. With the addition of Monarch Swarm, the industry’s first team-driven, enterprise data preparation and socialization platform, Slemma users can now create, find, access, prepare, blend and share governed, trustworthy data sets and models for true enterprise collaboration and faster, more strategic decision-making.
“Given today’s focus on analyst autonomy, data is often managed in isolation, ‘tribal knowledge’ frequently goes unshared, and analytics outcomes aren’t being reused for the greater good,” said Michael Frasier, CEO, Slemma. “Monarch Swarm redefines how businesses approach analytics by putting a greater focus on team sharing and enterprise-wide collaboration. By integrating Monarch Swarm, our customers not only benefit from fast, easy data preparation and visualization, but they can easily socialize and collaborate on curated data sets and analytics outcomes for improved operational processes and better business decisions.”
Announced on November 1, 2017, the latest version of Monarch Swarm applies the fundamental concepts of self-service data preparation, collaboration and socialization while supporting governance and cataloging. The platform’s key features include:
- 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.
“When it comes to self-service data preparation and analytics processes, too many organizations are duplicating work and data,” said Jon Pilkington, chief product officer, Datawatch. “Monarch Swarm creates an online data marketplace where users can create, discover, reuse and share trustworthy data, and leverage social features to select the data sets that best meet their analytics and reporting needs. Not only does this speed collaboration and uphold governance practices, but it expedites self-service analytics, boosts productivity, promotes cooperation, and empowers both individuals and teams to tap data’s full potential to drive fundamental business change.”
For more information on Monarch Swarm, please visit: http://www.datawatch.com/our-platform/monarch-swarm/, or request a demo at: http://www.datawatch.com/monarch-swarm-demo/. To learn more about Slemma, go to: https://slemma.com/.
About Slemma, Inc.
Slemma is a data visualization tool that connects with 70+ cloud service, cloud storage and data warehouse solutions. Slemma makes it easy for anyone to connect to their preferred solution, visualize their data and share their findings with a team. Learn more at: https://slemma.com/.
About Datawatch Corporation
Datawatch Corporation (NASDAQ-CM: DWCH) enables ordinary users to achieve extraordinary results with their data. Only Datawatch can unlock data from the widest variety of sources and prepare it for use in visualization and analytics tools, or for other business processes. When real-time visibility into rapidly changing data is critical, Datawatch also enables users to analyze streaming data, even in the most demanding environments, such as capital markets. Organizations of all sizes in more than 100 countries worldwide use Datawatch products, including 93 of the Fortune 100. The company is headquartered in Bedford, Massachusetts, with offices in New York, London, Frankfurt, Stockholm, Singapore and Manila. To learn more about Datawatch or download a free version of its enterprise software, please visit: datawatch.imarc.io.
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Any statements contained in this press release that do not describe historical facts may constitute forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995. Any such statements contained herein, including but not limited to those relating to product performance and viability, are based on current expectations, but are subject to a number of risks and uncertainties that may cause actual results to differ materially from expectations. The factors that could cause actual future results to differ materially from current expectations include the following: rapid technological change; Datawatch’s dependence on the introduction of new products and product enhancements and possible delays in those introductions; acceptance of new products by the market, competition in the software industry generally, and in the markets for next generation analytics in particular; and Datawatch’s dependence on its principal products, proprietary software technology and software licensed from third parties. Further information on factors that could cause actual results to differ from those anticipated is detailed in various publicly-available documents, which include, but are not limited to, filings made by Datawatch from time to time with the Securities and Exchange Commission, including but not limited to, those appearing in the Company’s Annual Report on Form 10-K for the year ended September 30, 2015. Any forward-looking statements should be considered in light of those factors.
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Vice President Worldwide Marketing, Datawatch Corporation
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