Slemma Embeds Datawatch Monarch Swarm Into Data Visualization Offering for Improved Data Quality, Collaboration and Governanc...
November 08 2017 - 9:57AM
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 CorporationDatawatch
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:
www.datawatch.com.
Safe Harbor Statement under the Private Securities
Litigation Reform Act of 1995Any 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.
Source: Datawatch
Media Contact:Frank MorenoVice
President Worldwide Marketing, Datawatch
Corporationfrank_moreno@datawatch.com 978-275-8225Twitter:
@datawatch
© 2017 Datawatch Corporation. Datawatch and the Datawatch logo
are trademarks or registered trademarks of Datawatch Corporation in
the United States and/or other countries. All other names are
trademarks or registered trademarks of their respective
companies.
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