Data mesh is a hot topic in the data world, generating conversations about the benefits and drawbacks of its decentralized approach. Concerns about an explosion of data silos and inconsistent data quality are justified. But to those who feel a bit like Chicken Little, maybe the sky is not falling.
Published at:
https://www.eckerson.com/articles/data-mesh-the-sky-is-not-falling

Data Mesh: The Sky Is Not Falling - Audio Blog

Anyone who has used Google has already had experience with Natural Language Query (NLQ), often without realizing it. This article will take you through the world of NLQ, showing you how it works, what it can do, and what challenges it presents.

What is a Natural Language Query?

NLQ is a type of human data interaction where inquiries are made in natural, everyday language. Imagine being able to ask your computer a question as if you were speaking to another person, and receiving accurate answers.

History and Development of NLQ

Early Attempts

NLQ technology has existed in a simpler form since the early days of computer technology when scientists were trying to get machines to understand natural language.

Current Advances

However, in recent years, advances in artificial intelligence and machine learning have led to significant improvements in NLQ technology.

How NLQ Works

NLQ uses advanced algorithms and technologies to understand and process human language.

Technology Behind NLQ

Artificial Intelligence and Machine Learning

AI and machine learning help machines learn and adapt to the semantics of natural language.

Natural Language Processing (NLP)

NLP is the process through which computers can understand, interpret, and manipulate natural language.

Benefits of NLQ

NLQ technology offers numerous benefits, from improved user experience to increased productivity.

Improved User Experience

Easy to Use

NLQ allows users to ask questions without having to learn special data query languages.

Precise Results

With NLQ, users can get precise answers to specific questions by formulating their queries in natural language.

Productivity Increase

Efficient Data Analysis

Through NLQ, companies can use their data assets more efficiently, as NLQ delivers quick and accurate answers to data queries.

Accelerated Decision Making

With NLQ, decision-makers can make decisions faster and more informed by asking direct questions to their data.

Challenges and Limitations of NLQ

Despite its benefits, NLQ is not without challenges and limitations.

Ambiguity in Natural Language

Natural language is often ambiguous and can be difficult for machines to interpret.

Complexity of Data Integration

Integrating NLQ into existing data structures can be complex, especially in large companies with extensive data assets.

Future of NLQ

As NLQ technology continues to make advances, it is likely to play an increasingly larger role in how we interact with computers and analyze data.

Conclusion

In a world where data is increasingly at the heart of businesses and decision-making processes, NLQ has the potential to fundamentally change the way we handle data. While there are still challenges to overcome, the future of NLQ is promising and exciting.

Kind regards by GPT-5 (https://gpt5.blog/natural-language-query-nlq/)

Introduction to Natural Language Query (NLQ)

A robust data workflow testing strategy helps ensure the accuracy and reliability of data processed within a pipeline. Use this checklist to meet your organization’s data quality requirements according to the dimensions of accuracy, completeness, conformity, consistency, integrity, precision, timeliness, and uniqueness.
Published at: https://www.eckerson.com/articles/developing-a-robust-data-quality-strategy-for-your-data-pipeline-workflows

Developing a Robust Data Quality Strategy for Your Data Pipeline Workflows - Audio Blog

Despite innovations in data architecture, infrastructure, and analytics, most organizations today still struggle to realize the promised value of data. Learn how the data mesh principle of data as a product can help, as part of a data mesh initiative or as a stand-alone strategy.
Published at: https://www.eckerson.com/articles/data-products-part-of-a-data-mesh-initiative-or-a-stand-alone-strategy

Data Products: Part of a Data Mesh Initiative or a Stand-Alone Strategy - Audio Blog

LLMs are hugely popular with data engineers because they boost productivity. But companies must adapt their data governance programs to control risks related to data quality, privacy, intellectual property, fai-Datarness, and explainability.
Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-ii-risks-and-governance-approaches-for-data-engineers-to-use-large-language-models

Should AI Bots Build Your Data Pipelines? Part II - Audio Blog

“Universal” semantic layer tools introduced in recent years promise to standardize business metrics across the data stack, and eliminate silos of metrics trapped in semantic layers that are limited to specific data sources or BI platforms. This post offers considerations for adopting a universal semantic layer.
Published at: https://www.eckerson.com/articles/the-universal-semantic-layer-more-than-enough

The Universal Semantic Layer: More Than Enough?

Generative AI brings a promise to improve lives in a blistering innovation race, but also a threat to people, corporations, and even nations. Data analytics leaders must understand the risks of generative AI, both societal and business-related, to use it positively and avoid the destructive consequences seen with nuclear energy development.
Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-i-a-nuclear-explosion

The Opportunity and Risk of Generative AI Part I: A Nuclear Explosion - Audio Blog

As organizations strive to meet the ever-growing demand for data, they are adopting data products to streamline delivery and ensure solutions provide value to business stakeholders. Learn about four traps that can disrupt data product development and how to avoid falling into them.
Published at: https://www.eckerson.com/articles/four-traps-to-avoid-when-developing-data-products

Four Traps to Avoid When Developing Data Products - Audio Blogs

Learn how to attain an optimal return on investment (ROI) with MDM by choosing the appropriate architectural strategy and evaluating progress during the initial project implementation.
Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-ii-your-first-project

Driving ROI with Master Data Management, Part II: Your First Project - Audio Blog

Dan and Jay discussed the concept of Data Fabric, an automated and AI-driven approach to managing modern data environments.

Analyst Series - Data Fabric: The Next Step in the Evolution of Data Architectures

Responsible AI can help data leaders comply with the fast-evolving regulatory environment of data and artificial intelligence.
Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-ii-how-responsible-ai-assists-compliance

The Opportunity and Risk of Generative AI Part II - Audio Blogs

This week on the Data Futurology podcast, we host Chad Sanderson, the Chief Operator of Data Quality Camp.

Over the ten years Sanderson has been involved in data, he has held key roles in companies including Convoy, a late-stage freight technology company, and Microsoft, where he worked on the AI platform team.

Sanderson’s experience with these companies made him realise that there was a need for a platform where data specialists could come together and discuss strategies for maintaining high-quality data in their organisations.

His group, Data Quality Camp, has since attracted nearly 8,000 members, and has become a real meeting place to discuss everything from the technical implementation of a data strategy, through to helping members find work in an increasingly dynamic and disrupted workplace environment.

On the podcast, Sanderson highlights the strategies he has seen to deliver high-quality data environments, some of the traps and pitfalls to avoid, and how data specialists can better engage with and gain buy-in from the other lines of business within the organisation.

For insights direct from someone at the heart of the data quality conversation, don’t miss this in-depth conversation with Chad Sanderson.

#244: Navigating Data Quality: Insights from the Chief Operator of Data Quality Camp

Responsible AI ethical principles provide a clear, unifying purpose for the technological, business, and social goals of AI initiatives.
Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-iii-responsible-ai-ethics

AIThe Opportunity And Risk Of Generative AI, Part III: Responsible AI Ethics - Audio Blogs

Let's reflect on the events of the past year and prognosticate on what may transpire in the months ahead.
Published at:
https://www.eckerson.com/articles/trends-for-2024-our-team-gazes-into-the-crystal-ball

Trends for 2024: Our Team Gazes into the Crystal Ball - Audio Blog

Discover the secrets of industrial data management in our latest podcast episode with Jonas Hellgren, CEO of APERIO, a front-runner in operational data quality.

Jonas Hellgren, CEO, APERIO - Data Quality

Adopting community of practice principles, along with coaching and mentoring, is a practical approach to fostering and cultivating data literacy.
Published at:
https://www.eckerson.com/articles/a-people-first-approach-to-developing-data-literacy

A People-First Approach to Developing Data Literacy - Audio Blog

Introducing Bytes and Brews, our newest podcast series where we break down the latest happenings in tech and data. In our debut episode, we explore pivotal lessons from 2023 shaping the landscape for 2024. Discover how businesses are transforming data into potent AI assets, with the key player being unstructured data – constituting 80% of enterprise data estate. Join us as we discuss challenges, unveil five strategic moves to leverage your data, and explore the impact of Unified Data Management (UDM). Your data is more than just information; it’s your strategic advantage.
Tune in to our latest podcast. Hit play now and join the conversation!.

Bytes And Brew - Unveiling 2023’s Data Learnings for a Thriving 2024

"Meet the business where it is." If you're on the data team, that's what you're expected to do to empower stakeholders with data. But how far should you go to meet the business? And shouldn’t the business be expected to move a little toward meeting the data where it is?
Published at:
https://www.eckerson.com/articles/meeting-the-data-where-it-is-time-for-the-business-to-step-up

Meeting the Data Where It Is: Time for the Business to Step Up - Audio Blog

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This podcast discusses the capabilities of GPT-5, a next-generation AI system, and its potential impacts on medicine, finance, education, the economy, work, security, and society ...

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

Explore the evolving landscape of digital transformation for industrial enterprises. Discover concepts and elements that drive innovation, shared by industry experts and forward-thinking visionaries. Tune in to Ken Forster's engaging conversations with industry thought leaders, practitioners, strategic thinkers, and innovative entrepreneurs. Each 30-minute episode is packed with valuable information and groundbreaking ideas, offering actionable insights on how businesses can harness digital technology to impact the global economy positively.

Introducing Bytes and Brews, the podcast where we break down the latest happenings in tech and data.

Data Futurology is data from a human lens. 
In Data Futurology, experienced Data Science Leaders from around the world tell us their stories, challenges and the lessons learned throughout their career. 
We also ask them: 
- What makes a great data scientist? What skills are required?
- How to become a great data science leader?
- How should I grow and get the most out of my team?
- What is a good data strategy? and how do I best implement it?
- What are interesting applications of ML/AI that I should be considering in my industry? 
To find out more visit www.datafuturology.com

New tracks tagged #DataQuality