The evolution of analytics: From BI to AI

Analytics have come a long way – from data warehousing and manual report building to centralised data in the cloud and machine learning driven analytics. Business Intelligence (BI) tools were revolutionary when they arrived: they made analytics more accessible to the wider business users. Their speedy and powerful analytics provided insights that businesses only dreamt off in the past. But as technology advances, so has the analytics world evolved.
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Spotlight

OTHER ON-DEMAND WEBINARS

Building the Road Map for Real-Time Data and Analytics

As organizations strive to be more competitive, they often need real-time insights; no one wants to make decisions based on stale data. TDWI research indicates that real-time data collection is already in the mainstream. Some use cases include inventory management, fulfillment, supply chain, and logistics in which retailers must be able to assess product availability and consumer demand in real time. Forward-looking organizations also want to enrich real-time data with other data types to provide even better analytics.
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Empowering Collaboration Between Data Practitioners and Product Managers

As data practitioners and product managers strive to gain actionable insights with the data at hand, it’s important for both roles to work collaboratively.
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SMALL DATA, BIG INSIGHTS

Agillic

There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.
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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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