Continuous Integration for Data Science · Pivotal Engineering Journal

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This is a follow up post on Test-Driven Development for Data Science and API First for Data Science focusing on Continuous Integration. Last time we wrote about the importance of test-driven development for data science, especially in the context of what we call smart applications. There are many examples for smart applications, for example, Google's Inbox has a feature that is called Smart Reply which uses machine learning to suggest three possible answers to your incoming messages. Another instance of a smart app is Apple's Photo app on the iPhone. While taking a photo, it recognizes faces and places through machine learning and automatically relates them together so that searching for photos becomes much smarter.


How to justify the purchase of a data integration tool

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The growing importance of business intelligence and data analytics applications in driving business decision making has made data integration's vital role in the enterprise crystal clear. From gathering data, transforming it into useful information and delivering it to the business users or processes that need it, data integration routines provide the crucial link between a variety of source and target systems. As the first article in this series examined, several types of packaged software have emerged to meet the challenges of data integration. The current generation of data integration tools consists of full-fledged suites that support extract, transform and load (ETL) processes, application integration, cloud-based and real-time integration, data virtualization, data cleansing and data profiling. How can you determine if your organization should invest in a data integration tool?


Digital transformation: three ways HR can use AI more effectively

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Data is changing the world, but today's enterprises remain inundated with an ever-rising tide of data generated by products, customers, partners, and business ecosystems. Successful organisations in this digital age are those that have found a way to harness it in order to drive operational efficiencies, better serve customers, tap new market opportunities, and gain competitive advantage. Despite this, not all business functions have progressed swiftly in their usage of data. HR is a case in point. Depending on how digitally mature a company is, data-driven decision-making can be adopted by HR for great results.


Analytics & AI

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Software platforms that provide the facility to add modules to the core software according to a company's requirements, each module performing a specialized task that resolves the needs of a section, department or division of the company. These software platforms act as a hub for all added apps, eliminating the need for implementing a separate integration infrastructure to some extent. Salesforce is a prime example of such a software platform. It's AppExchange portal contains over 2,700 applications, most of which are built with native force.com This type of software provides dashboards and wizards to ease the tasks of designing and managing integrations for IT administrators, and eliminates the need for acquiring extensive software architecture knowledge by IT staff or for hiring consultants for small integration projects.


Bringing AI to enterprise integration

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Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers. Turn-by-turn navigation apps have changed all that: You may still take a wrong turn along the way, but the apps usually get you back on track with little fuss. Self-service integration specialist SnapLogic is turning to artificial intelligence (AI) to help its customers achieve that sort of turn-by-turn navigation when it comes to enterprise integration. Citing GPS navigation and digital home assistants like Amazon's Alexa, SnapLogic Founder and CEO Gaurav Dhillon says the company's new technology, Iris, will eliminate the integration backlog that stifles so many technology initiatives through the use of AI to automate highly repetitive, low-level development tasks. "Companies can't innovate and transform their businesses if they're bogged down in rote, repetitive tasks that don't do much for the organization," Doug Henschen, vice president and principal analyst at Constellation Research, said in a statement last week.