Enabling the Third Wave of Analytics: Embedded BI Takes Center Stage

GoodData

Today’s data-savvy companies actively seek ways to enhance their approach to analytics and evolve out of the past.  While the first wave of BI involved static, rigid, IT-owned systems, and the second expanded the capabilities to a more business-facing analyst set, the third wave aims to infuse analytical activity into multiple layers of non-technical business roles.  As a key strategy to enable this third wave, companies today are exploring an embedded approach that places analytical activity directly in the context of everyday user applications, and the independent software vendors (ISVs) and enterprises providing those applications have taken notice of this trend.  Done efficiently, embedded analytics promotes faster deployment and pervasive usage of analytics for end-users while delivering a competitive advantage and the opportunity for new revenue streams for software vendors and enterprises.
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Spotlight

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

OTHER ON-DEMAND WEBINARS

Data Architecture Best Practices for Advanced Analytics

Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need. There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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Eliminating Data Silos: Modern Data Architectures for Analytics

Modern data applications and analytics rely on a wide variety of data both inside and outside the company; organizations depend on enriched data sets for better insights. This need, in part, has driven many companies to move to cloud data warehouses and cloud data lakes. However, it’s no longer simply about migrating to the cloud. It’s about modernizing using a combination of industry-leading services in the cloud and cloud-native data management services to deliver better business decisions, faster.
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The Role of Data Governance in a Data Strategy

A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
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2020 and Beyond: Architecting Your Data Warehouse for the New Decade

Companies continue to experience a dynamic shift in the growth of enterprise data. Fueled by a wave of emerging startups, innovative technologies and greater competition, the opportunity to drive faster decision-making has exploded – but so too have the questions on the best way to architect analytics-ready BI from the modern cloud-ready data warehouse.
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Spotlight

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

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