Combining Data Management with Organizational Change

Global Data Strategy

Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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Spotlight

Text analytics, applied to social, online, and enterprise data, aims to extract useful information and create usable insights for business, personal, government, and research ends. The technology is pervasive, even if not ubiquitous, which is to say that it is deployed in applications ranging in scale from device-installed to distributed, “total information awareness” data mining. Text analytics is utilized wherever big/fast text is found. And while not every analytical application directly involves text, in our emerging big-data world, every task – including analyses of “machine data” and transactional records – may be enriched by the inclusion of text-sourced information.

OTHER ON-DEMAND WEBINARS

Harnessing Big Data in Healthcare

Healthbox

The evolution of sensors, smart phones, medical devices, and wearables all collecting and uploading information in real-time has led to the rapid accumulation of health-related data, or Big Data. The sheer volume, velocity, and variety of the data being collected poses challenges for harnessing and ensuring its validity to benefit both the macro, population-level health and the micro, evidence-based precision medicine. Join Dr. Eric Louie, Chief Medical Officer, and Jessica Baker, Associate, as they discuss changes in healthcare data over time and how to overcome human biases, curate meaningful data sets, and better inform actionable decisions in the healthcare setting.
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Riding the Data Privacy Wave: How Will You Stay Afloat?

IBM

Data privacy regulation is bigger than just GDPR. Other countries and jurisdictions are enacting their own versions of the data privacy regulation, each with subtle nuances - such as the California Consumer Privacy Act (CCPA), Lei Geral de Proteção de Dados (LGPD), and more - and that’s on top of existing privacy regulations. Moreover, consumers increasingly expect more protection for their sensitive information. A recent IBM-Harris poll of 10,000 individuals revealed that 75% of consumers won’t buy from companies they don’t trust no matter how great their product or service.
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How to Modernize Data Lake Technologies with Cloud-Based Solutions

tdwi.org

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.
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Modernize Your Data Architecture to Deliver Oracle as a Service

robin.io

The migration to cloud-based data architectures continues at a rapid pace, including databases and data management. Oracle databases are part of this trend, and during this webinar you will learn how to automate the provisioning and management of Oracle databases so that you can deliver an “as-a-service” experience with 1-click simplicity. Experts will walk you through the process of
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Spotlight

Text analytics, applied to social, online, and enterprise data, aims to extract useful information and create usable insights for business, personal, government, and research ends. The technology is pervasive, even if not ubiquitous, which is to say that it is deployed in applications ranging in scale from device-installed to distributed, “total information awareness” data mining. Text analytics is utilized wherever big/fast text is found. And while not every analytical application directly involves text, in our emerging big-data world, every task – including analyses of “machine data” and transactional records – may be enriched by the inclusion of text-sourced information.

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