Top 10 Data and Analytics Trends That Will Change Your Business

Gartner

• The top ten strategic data and analytics technology trends and what they enable. How these trends enable you to build an intelligent and emergent data and analytics portfolio of capabilities that scale to the needs of digital business. Why these trends are growing and having an impact now. How these trends will change your organization, data and analytics program and skills needed. Strategic technology trends have significant disruptive potential over the next 5 years. You must examine your business impacts of these trends and appropriately adjust investments, business models and operations or else your company is at risk of losing competitive advantage to those who do. Data and analytics leaders cannot afford to ignore these 2019 top data and analytics trends.
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Spotlight

Machine Learning as a concept has been in existence for many decades now. However, most manufacturing operations — such as repairing an aircraft engine, planning the product mix in cement production, or ensuring energy control in a large facility — are still largely dependent on experience-based human decisions. The advent of Big Data technology, coupled with efficient data storage mechanisms and parallel processing frameworks, has found new use for the petabytes of data generated by manufacturing operations. Applying Machine Learning techniques to the shop floor has enabled increased accuracy in decisionmaking and improvement in performance.

OTHER ON-DEMAND WEBINARS

Visualizing Geospatial Data at Scale

Arcadiadata

Geospatial data is everywhere today. Your mapping capabilities need to handle the growing volumes of “big data” to deliver location-based insights at any level. Large-scale mapping use cases require a scalable and real-time visualization platform that enables self-service analysis. End users need a fast, interactive system that can immediately display any view of their data on demand.
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Data Protection Considerations for Multi-Cloud Environments

Veeam

The benefits of turning to the cloud are numerous; scalability, control, flexibility, and power to name a few. While transitioning to the cloud has many strong points, there are still the challenges of data and application protection. In this webinar, you’ll learn: - How to leverage multi-cloud as an addition to your traditional data center - What applications and services require the most protection in hybrid mode and how to protect them - The importance of portability and how to avoid lock-in - How to get better compliance and data protection by moving data between on-prem and the cloud.
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Cloud Data Management

tdwi.org

Cloud data management CDM is simply data management that involves clouds. For example, when focused on data persistence, CDM provides cloud-native data storage and optimized processing for the burgeoning volumes of enterprise data, big data, and data from new sources that users are choosing to manage and use on clouds. When focused on integration, CDM provides data integration infrastructure (with related functions for quality and semantics) to unify multicloud and hybrid on-premises/cloud environments.
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Activate Your Data Governance Policy

DATAVERSITY

What does it mean to activate a Data Governance policy? Can an inactive policy be effective? Data Governance policies can address different things depending on the organization. Some policies are very general and introduce the awareness of formal Data Governance to the organization. Other policies address specific needs like Data Quality, data documentation, and data protection. Join Bob Seiner and a special guest for this RWDG webinar where they will tackle of the subject of how to develop and deploy an active Data Governance policy. Bob and his guest will provide specific examples of policy components and examples of how organizations use policies to govern their data.
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Spotlight

Machine Learning as a concept has been in existence for many decades now. However, most manufacturing operations — such as repairing an aircraft engine, planning the product mix in cement production, or ensuring energy control in a large facility — are still largely dependent on experience-based human decisions. The advent of Big Data technology, coupled with efficient data storage mechanisms and parallel processing frameworks, has found new use for the petabytes of data generated by manufacturing operations. Applying Machine Learning techniques to the shop floor has enabled increased accuracy in decisionmaking and improvement in performance.

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