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

A leading geophysical services company sought TEKsystems’ partnership in managing the high volume and velocity of their data, as well as developing predictive analytics to save time, money and resources.We would implement predictive analytics modeling using Oracle R and Oracle Data Miner technologies. Our modeling would be able to predict the possibility of equipment failure within 16 hours.

OTHER ON-DEMAND WEBINARS

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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Moving from centralized data platform to a federated data mesh

In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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The Software-Defined Data Center: A Foundation for Digital Transformation

tierpoint

As reported in Forbes last year, 73% of companies are planning to move to a fully software-defined data center within two years. A software-defined data center is based on a virtualized environment of compute, storage, networking and security in conjunction with policy-based management and automation. The evolution from a traditional data center architecture to one that is software-defined can take months if not years but can yield immense benefits for the business. Join us for a discussion on the progress toward the fully software-defined data center, including benefits of infrastructure as code and overcoming challenges associated with traditional workflows.
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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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Spotlight

A leading geophysical services company sought TEKsystems’ partnership in managing the high volume and velocity of their data, as well as developing predictive analytics to save time, money and resources.We would implement predictive analytics modeling using Oracle R and Oracle Data Miner technologies. Our modeling would be able to predict the possibility of equipment failure within 16 hours.

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