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

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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Powering Data Democratization: Six Steps to Implement Modern Analytics Data Governance

Self-service data access opens up massive opportunities for your organization. But it can also open you up to major vulnerabilities and compliance risks. Keep it secure and compliant with robust, enterprise-wide data democratization. Join our Senior VP of Marketing Piet Loubser on August 25th to learn how to remove critical roadblocks to self-service data access. He’ll hand you the keys to achieving agile data governance.
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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.
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Unleash the Power of Your Finance Data

In today’s corporate finance departments, the pressure to do more with less is greater than ever. The most successful finance teams partner with other lines of business to drive growth through data-driven insights, using advanced analytics and predictive models. But translating insights into real-time actions requires real-time analytics that can’t be achieved with traditional business intelligence solutions and dashboards. To unleash the full power of your finance data, you need a new approach.
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Automated Data and Analytics Workload Modernization

Moving on-premises legacy data and analytics workloads to the cloud is unavoidable if you want to overcome infrastructural constraints, facilitate proactive analytics, and lower costs. You need a service that enables seamless scalability for petabyte-scale data processing, interactive analytics, and machine learning. However, end-to-end, automated workload transformation and optimization on serverless services is not straightforward.
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