#data-quality
#data-quality

Marketing tech

Is your CRM lying to you? The hidden costs of dirty B2B data | MarTech

fromBusiness Matters

3 weeks ago

E-Commerce

Emailmovers' Expert Guide: AI-Personalised B2B Email Marketing That Converts

Marketing tech

Is your CRM lying to you? The hidden costs of dirty B2B data | MarTech

fromBusiness Matters

3 weeks ago

E-Commerce

Emailmovers' Expert Guide: AI-Personalised B2B Email Marketing That Converts

Data science

Key data leadership roles are becoming vacant as DOGE pushes for access

fromBusiness Insider

4 weeks ago

Marketing tech

Alexandr Wang said he refused to let go of 2 tasks - no matter how big Scale AI got

fromNextgov.com

Data science

Key data leadership roles are becoming vacant as DOGE pushes for access

fromBusiness Insider

4 weeks ago

Marketing tech

Alexandr Wang said he refused to let go of 2 tasks - no matter how big Scale AI got

more#leadership

fromsiliconcanals.com

1 month ago

Brussels' Soda acquires AI monitoring company NannyML

With NannyML, we are accelerating our ability to deliver intelligent data quality infrastructure that adapts to the way modern data systems work.

Data science

fromwww.bbc.com

1 month ago

UK inflation number too high after data blunder

The downfall of the UK's inflation report highlights significant data flaws, particularly with Vehicle Excise Duty statistics. Incorrect road tax data has caused an overestimation.

UK news

#data-engineering

Business intelligence

Serving Data in the Data Engineering Lifecycle: A Comprehensive Guide

Data engineering culminates in serving data for analytics, ML, and operations.

Data quality and trust are critical in serving data effectively.

Understanding Data Generation in Source Systems: How It Works and Real-Time Applications

Data generation is crucial in data engineering lifecycle for reliable processing and transformation.

fromfaun.pub

Business intelligence

Serving Data in the Data Engineering Lifecycle: A Comprehensive Guide

5 years ago

Data science

Traditional Monitoring Is Dead. Long Live Data Observability | HackerNoon

Scala

Data Quality Verification with Deequ: A Practical Approach Using Scala

Business intelligence

Serving Data in the Data Engineering Lifecycle: A Comprehensive Guide

Data engineering culminates in serving data for analytics, ML, and operations.

Data quality and trust are critical in serving data effectively.

Understanding Data Generation in Source Systems: How It Works and Real-Time Applications

Data generation is crucial in data engineering lifecycle for reliable processing and transformation.

fromfaun.pub

Business intelligence

Serving Data in the Data Engineering Lifecycle: A Comprehensive Guide

5 years ago

Data science

Traditional Monitoring Is Dead. Long Live Data Observability | HackerNoon

Scala

Data Quality Verification with Deequ: A Practical Approach Using Scala

more#data-engineering

Data Cleansing: Harnessing Clean Data to Fuel Business Innovation

Clean and accurate data is essential for effective decision-making and organizational innovation.

Online Community Development

Limitations of Current Biomedical Text Mining Community Challenges | HackerNoon

fromBusiness Matters

Data Cleansing: Harnessing Clean Data to Fuel Business Innovation

Clean and accurate data is essential for effective decision-making and organizational innovation.

fromSearch Engine Roundtable

Online Community Development

Limitations of Current Biomedical Text Mining Community Challenges | HackerNoon

more#innovation

#marketing-analytics

Marketing tech

Google Analytics Makes Updates To Campaign Data Quality & Attribution Reporting (Data not available)

fromSearch Engine Roundtable

6 months ago

Data science

4 ways to correct bad data and improve your AI | MarTech

Marketing tech

Google Analytics Makes Updates To Campaign Data Quality & Attribution Reporting (Data not available)

6 months ago

Data science

4 ways to correct bad data and improve your AI | MarTech

more#marketing-analytics

#machine-learning

fromcontributor.insightmediagroup.io

Effortless Spreadsheet Normalisation With LLM

Clean, well-structured data is crucial for accurate analysis and decision-making.

fromInfoWorld

10 machine learning mistakes and how to avoid them

Machine learning has vast potential but carries significant risks that can lead to project failures.

The Art of Data Creation: Behind the Scenes of AI Training | HackerNoon

Data creation is essential for AI development, focusing on generating realistic datasets for effective model training.

fromInfoQ

Growth hacking

The Harsh Reality of Building a Real-time ML Feature Platform

Media industry

Scientists Built a Smarter, Sharper Materials Graph by Teaching AI to Double-Check Its Work | HackerNoon

fromcontributor.insightmediagroup.io

Effortless Spreadsheet Normalisation With LLM

Clean, well-structured data is crucial for accurate analysis and decision-making.

fromInfoWorld

10 machine learning mistakes and how to avoid them

Machine learning has vast potential but carries significant risks that can lead to project failures.

The Art of Data Creation: Behind the Scenes of AI Training | HackerNoon

Data creation is essential for AI development, focusing on generating realistic datasets for effective model training.

fromInfoQ

Growth hacking

The Harsh Reality of Building a Real-time ML Feature Platform

Media industry

Scientists Built a Smarter, Sharper Materials Graph by Teaching AI to Double-Check Its Work | HackerNoon

more#machine-learning

frommedium.com

3 months ago

Spark Scala Exercise 24: Error Handling and Logging in SparkBuild Safe, Auditable ETL Pipelines

"In this exercise, you'll build a defensive Spark ETL pipeline that handles various data issues, ensuring robust processing and reliability. The focus will be on preventing silent failures."

Scala

fromEntrepreneur

3 months ago

More Than a Quarter of Your Email List May Be Bad - Here Are 5 Ways to Clean It | Entrepreneur

Email marketing revenue is projected to reach $13.6 billion this year. Prioritizing email and maintaining a healthy list is key to leveraging its potential.

Marketing tech

How to be data-ready for AI adoption

Data quality is critical for effective generative AI usage; poor data can lead to inaccuracies and 'garbage in, garbage out' outcomes.

fromSocial Media Today

11 months ago

Question Posts May Become a Key Focus for AI Training Data

Better quality datasets are crucial for effective generative AI.

Platforms are enhancing data ingestion to improve AI responses.

fromLondon Business News | Londonlovesbusiness.com

1 year ago

Artificial intelligence

Data Centric AI? Yes, but... | HackerNoon

Data science

Rethinking unified observability: AI at the forefront - London Business News | Londonlovesbusiness.com

fromNextgov.com

10 months ago

How to be data-ready for AI adoption

Data quality is critical for effective generative AI usage; poor data can lead to inaccuracies and 'garbage in, garbage out' outcomes.

fromSocial Media Today

11 months ago

Question Posts May Become a Key Focus for AI Training Data

Better quality datasets are crucial for effective generative AI.

Platforms are enhancing data ingestion to improve AI responses.

fromLondon Business News | Londonlovesbusiness.com

1 year ago

Artificial intelligence

Data Centric AI? Yes, but... | HackerNoon

Data science

Rethinking unified observability: AI at the forefront - London Business News | Londonlovesbusiness.com

How data marketplaces are evolving to meet buyers' expectations

Data buyers are prioritizing data quality, privacy compliance, and addressability over broad audience availability.

Modern data marketplaces are evolving into specialized platforms responding to buyer expectations.

Marketing tech

Are synthetic audiences the future of marketing testing? | MarTech

Synthetic audience testing offers marketers a sophisticated alternative to traditional focus groups, utilizing digital avatars for customer insights.

fromITPro

Data quality worries are holding back AI adoption among manufacturers, despite optimism over its growth potential

Manufacturers see AI as valuable but are hindered by concerns over data quality in implementation.

fromClickUp

CRM Hygiene: Improve Data Quality and Sales Performance

Poor data quality costs businesses an average of $13 million annually in lost opportunities and wasted resources, largely due to sales reps battling incorrect contact details.

Business intelligence

6 months ago

Overcoming AI challenges in martech to maximize ROI | MarTech

AI integration into martech stacks is complex but essential for improving marketing efficiency.

Organizations must address resistance to AI and focus on data readiness for successful integration.

fromHarvard Business Review

Is AI the Right Tool to Solve That Problem?

Organizations face various obstacles when implementing AI, including data issues and lack of clear objectives.

Google DeepMind offers solutions to help guide organizations in successful AI adoption.