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Datawatch Launches Managed Analytics Platform for Self-Service Data Preparation and Visual Data Discovery - Datawatch Corporation today at the Gartner Business Intelligence Summit in Las Vegas, unveiled the Datawatch Managed Analytics Platform to deliver an enterprise solution for self-service data preparation and visual data discovery
Datawatch Launches Managed Analytics Platform for Self-Service Data Preparation and Visual Data Discovery


NewswireToday - /newswire/ - Chelmsford, MA, United States, 2015/03/30 - Datawatch Corporation today at the Gartner Business Intelligence Summit in Las Vegas, unveiled the Datawatch Managed Analytics Platform to deliver an enterprise solution for self-service data preparation and visual data discovery. NASDAQ-CM: DWCH

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Datawatch is the only solution that can bridge the gap between the ease-of-use and agility that business users demand together with the scalability, automation and governance needed by IT.

Unique capabilities delivered with the Datawatch Managed Analytics Platform include:

• Self-service data preparation leveraging the robustness and power of the Datawatch Monarch technology, the ‘original’ data preparation solution in the industry, Datawatch has created a powerful yet simple to use offering to manipulate, filter, enrich, blend and combine disparate data sets.
• Advanced data enrichment - sophisticated redaction capabilities to remove or obscure sensitive information used in regulated industries; predictive analytic enrichment including real-time scoring; and time series interpolation and conflation.
• Automation without scripting - using an intuitive visual process designer to schedule data extraction and preparation processes and deliver prepared data to authorized users or other enterprise systems.
• Access multi-structured data - unmatched capabilities to extract and prepare data from PDF files, log files, telemetry data, print spools, text, CSV, EDI, HTML, and even BI reports created by third party tools and legacy systems.
• Synchronous visual authoring - ability to simultaneously prep and visualize data side-by-side to get the desired results quickly.
• Visual data discovery - delivers more meaningful and timely decisions through real-time visualization of streaming data that can be combined with other relevant business data.
• Frictionless governance - securely store, manage and control access to all of the assets in the analytics workflow including data preparation models, data discovery workbooks, and source content not typically managed, including documents and XLS/CSV extracts. Administrators can curate managed data sets with complete audit trails and data lineage.

The Datawatch Managed Analytics Platform is an integrated yet modular solution. The new data preparation capabilities are available both as a stand-alone offering (for use by any third party analytic front-end or to load into a data warehouse/data mart) as well as tightly coupled with the latest release of the visual authoring tool for quickly building visualization applications.

“No other vendor can match the depth and breadth of capabilities delivered with the new Datawatch Managed Analytics Platform. Datawatch Monarch’s longstanding technology leadership in delivering self-service data preparation for the most challenging multi-structured data sets provides an unparalleled foundation for Datawatch to address the emerging requirements in this exciting market,” said Michael A. Morrison, president and chief executive officer of Datawatch. “We are the only vendor that can scale from individual analysts using self-service data preparation and analytics on their desktops all the way up to the enterprise. Indeed, one of our business partners uses Datawatch in a managed services cloud deployment to extract, prepare and deliver over 300 million data sets per month using 9,000 data preparation models.”

According to Gartner,“Data preparation is one of most difficult and most time-consuming challenges facing users of BI and data discovery tools, as well as data scientists. The unprecedented growth in multi-structured data has contributed to greater effort required in preparing the data to support decision-making processes.” 1

“Getting good clean data is tough. Most data doesn’t come in an easy-to-use format. But with Datawatch, you can work with even the most difficult data sources like reports, invoices, PDFs and complex real-time streams the formats that would otherwise take a lot of heavy lifting from IT to access,” said Joe Lovati, Director of Business Analytics and Reporting at Loan Depot. “The Datawatch Managed Analytic Platform provides a complete solution for delivering the right information, both data and visualizations, to all of our users.”

The Datawatch Managed Analytics Platform is available today with subscription pricing starting at $895 per user per year.

1 Gartner, Market Guide for Self-Service Data Preparation for Analytics, Lakshmi Randall et al., March 5, 2015

About Datawatch Corporation
Datawatch Corporation ( provides the only platform for visual analytics to leverage any data at any speed delivering valuable insights for improving business. The unique ability to acquire, prepare, and transform data from structured and multi-structured sources such as PDF and log files, as well as real-time streaming data, into visually rich analytic applications allows users to dynamically discover key factors that impact any operational aspect of their business. This ability to perform visual discovery against any data at any speed sets Datawatch apart in the big data and visualization markets. Organizations of every size, worldwide use Datawatch products, including 93 of the Fortune 100. Datawatch is headquartered in Chelmsford, Massachusetts with offices in New York, London, Frankfurt, Stockholm, Singapore, Melbourne, and Manila and with partners and customers in more than 100 countries worldwide.

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995 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, including but not limited to those relating to results of operations, contained herein 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: risks associated with the continuing weak global economy; risks associated with fluctuations in quarterly operating results due, among other factors, to the long sales cycle with enterprise customers and the size and timing of large customer orders; risks associated with acquisitions, including the acquisition and integration of Panopticon; the risk that our goodwill resulting from acquisitions may become impaired and require a write-down; limitations on the effectiveness of internal controls; rapid technological change; Datawatch’s dependence on the introduction of new products and product enhancements and possible delays in those introductions; competition in the software industry generally, and in the markets for next generation analytics in particular; Datawatch’s dependence on its principal products, proprietary software technology and software licensed from third parties; Datawatch’s concentration of customers in the financial sector; risks associated with international sales and operations; risks associated with indirect distribution channels and co-marketing arrangements, many of which were only recently established; the adequacy of Datawatch’s sales returns reserve; risks associated with a subscription sales model; Datawatch’s dependence on its ability to hire and retain skilled personnel; disruption or failure of Datawatch’s technology systems that may result from a natural disaster, cyber-attack, security breach or other catastrophic event; and uncertainty and additional costs that may result from evolving regulation of corporate governance and public disclosure. 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, 2014 and Form 10-Q for the quarter ending December 31, 2014. Any forward-looking statements should be considered in light of those factors.

Datawatch Investor Relations: E: investor[.] - P: 978-441-2200 x8323

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Datawatch Launches Managed Analytics Platform for Self-Service Data Preparation and Visual Data Discovery

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