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Organizations' desire to maximize the untapped potential of all their data assets is stymied by piecemeal attempts to manually govern data silos without a consistent and cohesive view of corporate data. To address consumers' endemic distrust of data, organizations are considering data governance platforms that help build trust, ensure data quality, and govern access to its data assets. These platforms promise to unify disparate governance efforts into a single integrated platform. This webinar will go over the concepts and capabilities of data governance platforms, followed by a presentation from OvalEdge, including a demo of its data governance platform.

Speakers:

- Sanjeev Mohan, Senior Consultant at Eckerson Group

- Sharad Varshney, Founder at OvalEdge

Sponsor: OvalEdge

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A half-day virtual event for chief data officers and team leaders that provides practical advice for selecting and implementing data catalogs.

Sponsors: Sandhill Consultants Ltd, Alation, BigID, data.world, Promethium, OneTrust

Media sponsors: BARC, TechTarget, NewVantage Partners, The Bloor Group, Datanami, DAMA-I

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So, you're ready to start a DataOps program, but the DataOps vendor landscape is confusing - especially with vendors (re-)branding themselves as 'DataOps' solutions.

In this webinar, Wayne Eckerson, President of Eckerson Group, joins DataKitchen's Chris Bergh to discuss Eckerson's DataOps Processes and Technologies framework to understand DataOps solutions. They discuss:

• The critical capabilities needed for DataOps success;

• How to understand & compare different vendors' DataOps offerings;

• The benefits of a DataOps Platform; &

• The best approach for implementing a DataOps platform throughout your data organization.

Speakers:

Wayne Eckerson - President, Eckerson Group

Christopher Bergh - CEO & Head Chef, DataKitchen

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Elastic cloud platforms empower enterprises to digitize their business, optimize analytics, and gain a competitive advantage. However, they first must get their data onto cloud platforms. This is no trivial task. Duplicate data, data inaccuracies, slow pipelines, and insufficient streaming capabilities can lead to a data swamp versus data clarity. But, with the right strategic approach, businesses can achieve accurate, real-time data arriving on the Cloud that can easily scale as organizational demands grow.

Cloud databases and cloud analytics platforms consume rivers of data from sources such as SaaS applications, telemetry sensors, and legacy databases on premises. Each data point captures a digital event—the click of a customer purchase, turn of machinery gears, or arrival of a delivery package—that offers an opportunity. To operate and thrive, cloud platforms need streaming data pipelines to ingest and transform that data in a timely fashion. This webinar defines streaming data pipelines and why modern enterprises need them. It also explores how enterprise data teams can build efficient, effective streaming data pipelines that drive the modern digital business.

Join this webinar with Kevin Petrie, VP of Research at Eckerson Group, and Ido Friedman, CTO at Equalum, to learn:
• The role of streaming data pipelines in cloud and hybrid environments
• The business demands driving their adoption
• Use cases for zero-downtime migrations and real-time ingestion
• Technical requirements and tool evaluation criteria
• Guiding principles to architect streaming data pipelines

Speakers:

- Kevin Petrie, VP of Research at Eckerson Group

- Ido Friedman, CTO at Equalum

Sponsor: Equalum

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Can enterprises tame the dragon? Maybe. They need to govern the full AI/ML lifecycle—data and feature engineering, model development, and model operations—with special attention to the operations stage. They need to carefully monitor, control, and reuse models during the entire lifecycle.

Eckerson Group analysts study this problem. Our consultants live it. Join both for a free-wheeling discussion of the tradeoffs and pitfalls of AI/ML governance.

You will hear:

• What AI/ML governance means, and why enterprises need it

• How governance applies to key stages of the AI/ML lifecycle

• Guiding principles, best practices, and tradeoffs of AI/ML governance

• Case studies and what they teach us

• How AI/ML governance is likely to evolve