Data-Driven Product Development

DataCamp

In this webinar, Ramnath Vaidyanathan, VP Product Research at DataCamp, shares how DataCamp uses data in a big way to drive the development of products and content at scale. He gives some practical examples & insights of ways data has shaped DataCamp's products. Finally, Ramnath goes through the data-driven development process so you can apply the same principles to the products you are building.
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Spotlight

While complex statistics may still be limited to data scientists, data-driven decision-making shouldn’t be. In the coming year, simpler big data discovery tools will let business analysts shop for data sets in enterprise Hadoop clusters, reshape them into new mashup combinations, and even analyze them with exploratory machine learning techniques. Extending this kind of exploration to a broader audience will both improve self-service access to big data and provide richer hypotheses and experiments that drive the next level of innovation.

OTHER ON-DEMAND WEBINARS

Sharing and Deploying Data Science with KNIME Server - February 2019

KNIME

You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems.
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Does More Data Mean More Effective Control Charts The Surprising Truth

info.minitab.com

When you need to monitor and control variation in your process, a control chart will help you to separate special causes from common cause variation or random variation. Yet how do todays types of data, including full inspection, impact this tool's effectiveness? The control chart was created in the 1920s, a time when measurement, data capture and calculation were done manually, making sampling essential. Today, how data is collected has all changed.
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Data Strategy Best Practices

infogix

Your Data Strategy should be concise, actionable, and understandable by business and IT! Data is not just another resource. It is your most powerful, yet poorly managed and therefore underutilized organizational asset. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Overcoming lack of talent, barriers in organizational thinking, and seven specific data sins are organizational prerequisites to be satisfied before (a measurable) nine out of 10 organizations can achieve the three primary goals of an organizational Data Strategy, which are to:
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Augmenting BI and Analytics in the Age of AI and Big Data

Arcadia Data

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.
Watch Now

Spotlight

While complex statistics may still be limited to data scientists, data-driven decision-making shouldn’t be. In the coming year, simpler big data discovery tools will let business analysts shop for data sets in enterprise Hadoop clusters, reshape them into new mashup combinations, and even analyze them with exploratory machine learning techniques. Extending this kind of exploration to a broader audience will both improve self-service access to big data and provide richer hypotheses and experiments that drive the next level of innovation.

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