Suppliers United States Datawatch 
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Datawatch 

Self-Service Data Preparation for the Enterprise
United States
1985
Private
$10-100m
51 - 200
Open website
Datawatch Corporation 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.

Year Founded : 1985
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Datawatch ’s IoT Snapshot maps the range and focus areas of Datawatch ’s IoT business across Technologies, Use Cases, Industries, and Services. Only categories with active products will be shown. Missing categories indicate that there is no activity in those areas.
Technologies
Sensors
Camera / Video Systems
Analytics & Modeling
Computer Vision Software
Industries
Security & Public Safety
Datawatch ’s Tech Stack maps Datawatch ’s participation in the IoT tech stack.
  • Application Layer
  • Functional Applications

  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service

  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors

  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting
    Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies2
MasterCard Improves Customer Experience Through Self-Service Data Prep
MasterCard Improves Customer Experience Through Self-Service Data Prep
Derek Madison, Leader of Business Financial Support at MasterCard, oversees the validation of transactions and cash between two systems, whether they’re MasterCard owned or not. He was charged with identifying new ways to increase efficiency and improve MasterCard processes. At the outset, the 13-person team had to manually reconcile system interfaces using reports that resided on the company’s mainframe. Their first order of business each day was to print 20-30 individual, multi-page reports. Using a ruler to keep their place within each report, they would then hand-key the relevant data, line by line, into Excel for validation. “We’re talking about a task that took 40-80 hours each week,” recalls Madison, “As a growing company with rapidly expanding product offerings, we had to find a better way to prepare this data for analysis.”
Modell’s Ups its Game with Self-service Data Prep
Modell’s Ups its Game with Self-service Data Prep
Retail is a fast-paced business. To stay competitive, organizations like Modell’s must react quickly to market place conditions, industry trends, and economic upticks and downturns, sometimes overnight. 20 years ago, critical sales, inventory and transactional data lived in a mainframe system that was only accessible through Lotus 1-2-3. Joe Paltenstein, now AVP, Assistant Controller at Modell’s Sporting Goods said, “It was challenging to get the data we needed out of the mainframe system and into a workable format that we could use. And with so many different types of data it was hard to extract any useful insights without a painfully slow reformatting process. We had to print out reports on green bar paper and then entered the data into spreadsheets.”
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