Name: Data Governance is Killing Your Business — Time to Fix It
Start: 2020-12-15T20:00:00Z
End: 2020-12-15T20:00:29.000Z
Location: BrightTALK
Rating: 0

data.world is the cloud data catalog powered by a knowledge graph. It maps your data to familiar and consistent business concepts so your people get clear, accurate, fast answers to any business question.

What’s the secret behind Airbnb’s famously data-driven culture? It’s the company’s ability to provide all employees - not just the ‘data people’ - the ability to discover, understand, and trust data. Join Juan, Tim, and special guest Jeff Feng, product lead at Airbnb, to learn how the vacation experience juggernaut designed and developed its enterprise data catalog, DataPortal.

Whether you’re still unclear on the business value of a data catalog, or debating whether to build or buy, do not miss this episode. 

Catalog & Cocktails is a weekly data podcast, presented by data.world. It's your 30-minute elixir on the advancing and innovative world of data catalogs, metadata management, knowledge graphs, and more. Join podcast hosts and data nerds Juan Sequeda (Principal Scientist at data.world and Tim Gasper (Director of Product at data.world) to explore emerging topics and visionary leaders every week.

Catalog & Cocktails: How Airbnb Built Its Internal Data Catalog, DataPortal

Mesh is everywhere. It’s in our clothes, our fishing nets, our Wifi networks, and now our data architecture. But what is a data mesh? Do you need one? And if so, how do you start? 

Zhamak Dehghani is the director of Emerging Technologies at Thoughtworks and the leading expert on data mesh. We’ll chat about the emergence of the data mesh as a concept, why the approach works for eliminating architectural silos, and how it's producing more data-driven cultures.

Other topics include: 
- Key tools, technologies, and skills for adopting a data mesh
- What to do if you’re data mesh curious
- Inspiring architectural designs outside the data space

Catalog and Cocktails: Why it’s time to mesh with your data architecture

Let’s talk about data management frameworks. What is a data fabric and how is it different from a data mesh? No, seriously, we’re trying to figure it out ourselves. Is this an attempt by industry analysts and enterprising vendors to rebrand existing technology or are these fundamentally new data architectures?

In this episode, we’ll explore who and what is driving the conversation and try to demystify these emerging data disciplines.

This episode features:
- Clear definitions for data fabric and data mesh
- Where these frameworks overlap, intersect, and chart their own course
- Tim’s excuses following a Cleveland Browns playoff loss

Catalog & Cocktails: Is Your Data Fabric a Mesh?

“What is a data mesh?” It’s one of the most frequently asked questions on this podcast, and is almost a microcosm for data and analytics as a whole. It may be the next big thing. It may be yet another passing fad in architectural design. We compared and contrasted it with data fabric in Episode 32 and spoke with Zhamak Dehghani, the founder of the data mesh paradigm in Episode 44. We even hosted a special panel discussion dedicated to data mesh. 

After all these discussions, we are still trying to figure out what it is. So, we’re settling this the only way we know how: a good old-fashioned debate. Only one side can win.

Catalog & Cocktails: The Great Data Mesh Debate

Highly accurate, trustworthy, and timely metadata is gold to data catalogs and the business communities they serve. Integrations between DataOps.live and Data Catalogs like data.world are critical for the value they deliver to business communities looking to explore data. These integrations enable DataOps to publish the rich and highly accurate metadata we gather about data pipelines to the business catalog. The value to a customer to have 1-click, zero effort, highly accurate, data cataloging is immense.

Join this LIVE hands-on technical masterclass session to see how to integrate DataOps.live and data.world to automate an entire Snowflake infrastructure from scratch (including databases, roles, warehouses, stages), and how customers can ingest, transform data, test data, assure data quality, share data, and then build highly accurate up-to-date data catalogs, every time a data pipeline runs, with one single click.

1-Click Data Catalogs For Snowflake with DataOps.live

DJ Patil is perhaps the most influential data scientist in the world. Having been appointed by President Obama as the very first U.S. Chief Data Scientist, he was tasked with making the largest organization in history—the U.S. Federal Government—a data driven enterprise. Prior to that, he was the Chief Scientist, Chief Security Officer and Head of Analytics and Data Product Teams at the LinkedIn Corporation, where he co-coined the term “Data Scientist.”

Listen to DJ’s conversation on his work as the first U.S. Chief Data Scientist, lessons learned in the private sector, and his thoughts on community-driven data efforts.

Originally debuted at data.world summit 2020.

A Conversation with DJ Patil, First U.S. Chief Data Officer

Dr. Amy Gershkoff Bolles is a five-time general manager and data-driven growth executive, with experience building and leading product and engineering teams, as well as building and leading sales and marketing teams, managing user acquisition, revenue management, business operations, and pricing optimization, all with a data-driven approach.

Get her five lessons learned in creating impactful data-driven organization in her presentation.

Original presentation at data.world summit

5 Lessons from a Three Time Chief Data Officer

What comes to mind when you think of a data catalog? Metadata management? Data governance? A business glossary? It’s true that a data catalog must cover each of these disciplines, but modern, data-driven enterprises need more.

Join special guest, Joe Hilleary of Eckerson Group, for a look at the evolution of data catalogs and their role within enterprise data architectures. We’ll talk about traditional catalog capabilities and what you should demand your catalog provider deliver that you’re not getting today.

What You Should Expect from Your Data Catalog

Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:

Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?

Join Tim Gasper, Juan Sequeda, and Gary George, Data Ecosystem Platform Product Manager at Indeed, for an honest, no-bs discussion about data mesh and its role in data governance.

Five things to consider about Data Mesh and Data Governance

How Airbnb Built Its Internal Data Catalog, DataPortal

What’s standing in the way of a successful customer 360 program? Or a supply chain 360 program? Or any other 360 programs?

Is it disconnected systems, data silos, or governance policies? Maybe it’s lack of alignment on use cases, or how to gauge program success. Perhaps it’s all of the above.

The most important thing an enterprise can do to support a winning data strategy is to connect your data to how your business operates, because relationships matter! Knowledge graphs describe your most important business concepts and their relationships and connect them to your different data sources, providing organizations with a flexible model on which to grow their business.

In this webinar, we’ll explore the evolution of the knowledge graphs in the enterprise. As a data leader, you’ll get practical and proven advice along with best practices for deploying a knowledge graph strategy in your organization.

Topics covered include:
- How to accelerate business outcomes with knowledge graphs
- Why your first knowledge graph should be a data catalog
- Customer 360 – knowledge graph brings the key to unlock this use case

How to Achieve Your Business Goals With Knowledge Graphs

An enterprise data catalog is your foundation for data empowerment. It can index all of your knowledge and unify people, data, and analysis to become your true collaboration hub and information radiator.

In this webinar, we’ll show you how to keep your data catalog fresh and efficient. We’ll cover ways to help data engineers develop a scalable set of data resources and enable data stewards to promote consensus about data definition, quality, and usage.

Join us and learn:

- Best practices for maintaining a data catalog
- A framework to capture knowledge as data engineers, data stewards, and data consumers work 
   collaboratively, engaging people and teams
- Helpful resources and tips to achieve your data and analytics goals.

5 Ways to Supercharge Your Data Catalog

Graphs are the foundation of modern data and analytics, according to Gartner. They’re the bedrock of enterprise AI according to a leading knowledge management consultancy. And they’re powering search and recommendation engines at Facebook, Amazon, and Google.

Yet only one enterprise data catalog is built on top of a knowledge graph.

Join us for an overview of knowledge graphs and a discussion of why this transformational architecture matters in the context of data catalogs. Don’t miss this special event, presented by the hosts of the Catalog & Cocktails podcast.

Topics covered include:
- How knowledge graphs accelerate common data catalog use cases
- How knowledge graph architecture differs from traditional relational models
- How to use your data catalog’s knowledge graph for deeper exploration and analysis
- What’s next for knowledge graphs

Why Your Data Catalog Must Be Powered by a Knowledge Graph

Self-supervised machine learning, the next frontier in AI

Artificial Intelligence & Machine Learning: All to Know for a Successful 2021

Panel Discussion: Enabling Data Driven Supply Chains through Information Sharing

Tableau + COVID-19: Impactful Data Visualizations and Ethical Analytics

Defining the Data Economy: Data Production and Gathering

2021 Data & Analytics priorities for the CMO

How to Shape the Future of Analytics

Achieving a Single Source of Truth with Business Intelligence in Your Enterprise

Putting Big Data Self-Service Analytics in the hands of Business People

Panel Discussion: Expert Predictions - Trends in Big Data and Analytics in 2021

Bias in AI and Machine Learning Models

Customer Experience Focused Business Intelligence - Better with a CDP

Lessons from Massive Scale Growth – A case study in EdTech

The mosaic of platforms & services: System thinking in a customer insights era

Breaking Proof of Concept Cycle: Machine Learning from Idea to Production

Why “Where” is a Must Have for Data Science

Accelerating Analytics & ML in Financial Services in 2021 and Beyond

Achieving Ambitions: A Data Culture with Perpetual Learning

The 10 Vs of Big Data

Hulu: Driving Data Trust and Accessibility with Alation’s Data Catalog

Path to Brilliance: Building an Analytical Center of Excellence

How the ‘Cookie’ Crumbled: Preparing You for a Time Without Third-Party Cookies

Big Data: Tactics For Managing Legal Risks in the Operation & Sale of a Business

Six dimensions in data storytelling to transform insights into decisions

Rethinking Data Governance and Data Integration for a cloud-based digital world

Defining & Executing Your Data & AI Strategy: Best Practices & Lessons Learnt

5 A's to Big Data Success (Agility, Automation, Accessible, Accuracy, Adoption)

The rise of automated analytics and the demise of dashboards

Master Data, Master Data Management and MDM Implementation Patterns

Big Data and Analytics 2021: Trends and Outlook 2020

Enterprises waste millions of dollars on failed data initiatives because they apply outdated thinking to new data problems. Trying to fix these issues with technology just makes it worse. Deploying new self-service BI and data science tools and adding a layer of governance over the top typically results in overly complex, rigid processes that benefit the few and make everyone else less productive. Agile data governance is a new practice that applies the best of agile and open software development to data and analytics. It iteratively captures knowledge as data producers and consumers work together so that everyone can benefit.

Attendees will learn:

How to build a data-driven culture by embracing data literacy and inclusion
How to use agile and open methodologies to identify business problems and assemble the teams and data to help solve them
5 real-world data problems that agile data governance is uniquely suited to address
How to start applying agile data governance techniques within a data catalog

Data Governance is Killing Your Business — Time to Fix It

Data Governance

Data Analysis

Data Analytics

Data Architecture

Data Best Practices

Business Intelligence

SaaS

Metadata Management

Cloud

Data Catalog

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Business Intelligence and Analytics

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

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Data Governance is Killing Your Business — Time to Fix It

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