Data Articles | Data Science, Business Intelligence, & More

Assessing your Early Warning Capabilities & Options: Converging AML & Fraud Data for Integrated Warning & Tracking

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by Dan Meers and Gregory Lampshire In the financial services industry, time is the enemy for fraud detection and risk warning. There is an urgent need for a clear scope, needs statement, and prioritized plans to address detection, warning and risk management mandates. Rapid assessment and analysis of current controls and conditions is essential to…

Hadoop Embraces the Internet of Things

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by Jelani Harper The Internet of Things (IoT) exists all around us, inside and outside of our homes, offices, cities and vehicles and includes, in addition to traditional smartphones: Smart Cities/Smart Streets: There are cities with streets that utilize smart LED lighting that reduce costs and improve safety by detecting traffic and increasing luminosity when…

The Necessity of Personalizing Business Intelligence

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by Jelani Harper The evolution of Business Intelligence (BI) and analytics began in the back offices of IT departments where the proverbial gatekeepers lorded over reporting technologies, while disseminating largely historic data. More recently, the business was able to take control of these tools due to developments in Data Discovery, Cloud Computing, Open Source analytics,…

Mobile Data Governance

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by Jelani Harper The benefits of utilizing mobile devices for the enterprise include: Reduced Infrastructure Costs: Organizations can drastically reduce their costs for infrastructure by taking advantage of the Bring Your Own Device (BYOD) phenomenon, in which employees utilize their own mobile devices—smartphones, tablets and laptops—to work. Greater Productivity: Mobile devices increase worker productivity and…

Monetizing the Industrial Internet of Things

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by Jelani Harper The Industrial Internet of Things may well be the ultimate expression of where the data sphere is heading in the next couple of years. It combines Big Data sets with real time and predictive analytics (Operational Intelligence) capabilities through the Cloud to influence decision making and to create and tailor a variety…

Bringing Clarity to the Topic of Cognitive Computing

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by Jennifer Zaino DATAVERSITY’s™ Cognitive Computing Forum is taking place this August in San Jose, and in advance of the event, the team here also has released the 2014 Cognitive Computing Survey as well as conducted the webinar Understanding The World of Cognitive Computing. Just like the upcoming Forum, with presenters tackling topics ranging from…

The Data Architecture Trinity

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by Michael Brackett Past articles have explained the data description, data structure, and data integrity components of a formal data resource architecture.  The diagram below shows those three components in graphical form.  The descriptive component consists of two sub-components for formal data names and comprehensive data definitions (The Missing Descriptive Component, A Suitable Descriptive Component). …

Building More NoSQL Support with Rich Development Experience

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by Jennifer Zaino Know UnQL (Unstructured Query Language)? It was designed to be a community-supported, common database query language that could be used to access document-oriented databases from a range of vendors, enabling application portability across database engines and easing the load on application developers. Its SQL-like syntax was meant to be especially helpful to…

Data Science and Art: RRE Certification

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by Jelani Harper Gartner describes Open Source R—arguably the script of choice for Data Scientists and one of the most pivotal tools for creating advanced and predictive analytics—as “increasingly popular” (Herschel et al, 2014), while Forrester lauds its ubiquity. As one of the earliest champions of Open Source R, Revolution Analytics swiftly addressed its scalability…

Governing Self-Service Analytics

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by Jelani Harper It is possible to simultaneously empower the end user and his or her business objectives with self-service analytics and self-service Business Intelligence, while still ensuring full adherence to existing Data Governance policies, roles, and structure. It is also possible to combine the benefits of the traditional centralized approach to BI (governance, privacy,…