The hottest Data Quality Substack posts right now

And their main takeaways
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Sort Feature Overview: Better Data Through Collaboration

Database Engineering by Sort • 15 implied HN points • 01 Mar 24
🕹 Technology Data Quality Collaboration Database Management Workflow Transparency
  1. Data quality is crucial for businesses as it influences customer experience, decision-making, and AI outcomes.
  2. Collaboration is key for improving data quality, as automated tools can only address a portion of data issues.
  3. Sort provides a platform for transparent collaboration on databases, allowing for public and private database sharing, issue tracking, proposing and reviewing database changes.

The Making of Whisper: An In-Depth Exploration of its Training Data and Process

Amgad’s Substack • 39 implied HN points • 22 Dec 23
🕹 Technology Data Collection Data Quality
  1. OpenAI's Whisper ASR model stands out for its accuracy, made possible by releasing both its architecture and checkpoints under an open-source license, setting a new standard of innovation in the field.
  2. The training of AI models can be divided into supervised and unsupervised approaches, each with its unique strengths and limitations, with significant implications for achieving high-quality results.
  3. Data curation is a critical aspect of model training, with OpenAI showcasing the importance of maintaining data integrity through a meticulous process of automated filtering, manual inspection, and guarding against data leakage.

Revisiting A/B tests in SaaS 💯

The SaaS Baton • 117 implied HN points • 26 Apr 23
🕹 Technology SaaS A/B Testing Data Quality Network Effects
  1. Running A/B tests on SaaS products has unique challenges beyond just having enough users for statistically significant results.
  2. Incorporating minimal clear constraints in projects can drive creativity and productivity, as seen in Buffer's Build Week.
  3. Establishing indirect growth channels, like Gusto did with accounting firms, can create network effects and be a win-win for both parties.

Joe's Nerdy Rants #4

Joe Reis • 78 implied HN points • 10 Jun 23
🕹 Technology AI Data Management Data Quality
  1. Encourage kids and others to interact more in real life, consider alternatives to college, find careers that can't be easily automated, and learn to coexist with AI.
  2. Embrace lifelong learning and be open to change in order to adapt to evolving technologies and industries.
  3. Read up on interesting articles about tech, AI, data, and business topics for insights and inspiration.

Choosing a Data Quality Tool

Sarah's Newsletter • 359 implied HN points • 22 Feb 22
🕹 Technology Data Quality Data Tools
  1. Data quality tools are essential for maintaining trust in data and preventing stakeholders from resorting to workaround solutions.
  2. Choosing the right data quality tool involves understanding the specific needs of your organization and considering factors like budget, technical resources, and overall data quality goals.
  3. There are different types of data quality tools available, including auto-profiling data tools, pipeline testing tools, infrastructure monitoring tools, and integrated solutions, each with unique characteristics and considerations for selection.

More than 30 unique tracking events will cause you problems

timo's substack • 78 implied HN points • 12 Feb 23
🕹 Technology Data Tracking Data Quality Data Analysis Documentation
  1. Having more than 30 unique tracking events can lead to problems in data adoption and productivity.
  2. Too many unique events can lead to difficulties in analyst productivity and data exploration.
  3. Implementing a lean event approach with a focus on good event design and ownership can help prevent issues caused by high event volumes.

Data Contracts Book - Early Release

Data Products • 2 implied HN points • 27 Feb 24
🕹 Technology Data products Data Contracts Data Engineering Data Quality Data Governance
  1. Chad Sanderson announced an upcoming book on Data Contracts with O'Reilly, covering topics like what data contracts are, how they work, implementation, examples, and the future implications. The book will delve into Data Quality and Governance.
  2. The first two chapters of the book are available for free on the O'Reilly website. They cover the importance of data contracts and the real goals of data quality initiatives, totaling about 45 pages of content.
  3. Chad Sanderson is currently selecting technical reviewers for the book. Interested individuals can reach out to him to share their thoughts on an advance copy.

Why Data Quality Is More Important Than Ever in an AI-Driven World

Data Products • 5 implied HN points • 08 Jan 24
🕹 Technology Data Quality Artificial Intelligence Machine Learning Data Engineering Generative AI
  1. Data quality is crucial for machine learning projects and can have negative impacts on both society and individuals.
  2. Advances in Generative AI highlight the importance of high-quality data and the potential shortage of such data.
  3. Data quality affects the machine learning product development cycle, including ongoing maintenance costs of ML pipelines.

A Digital Cohort on Personalized Nutrition

Digital Epidemiology • 19 implied HN points • 02 Jun 23
🔬 Science Nutrition Data Quality Recruitment
  1. The study focused on personalized nutrition with a digital cohort of 1,000+ participants tracking various data for glucose level management.
  2. Developing a digital cohort requires intricate digital infrastructure and investment in user-friendly applications for high retention rates.
  3. Data quality assessment is crucial for multi-modal data collection, and the study achieved high completion rates with a focus on improving nutrition tracking.

The Consumer-Defined Data Contract

Data Products • 3 implied HN points • 04 Dec 23
🕹 Technology Data Contracts Data Engineering Data Governance Data Quality Software Engineering
  1. Producers need to move towards consumer-defined data contracts to improve data quality and alignment with user needs.
  2. A phased approach of awareness, collaboration, and contract ownership helps in successful data contract adoption.
  3. Starting with consumer-defined contracts drives communication, awareness, and problem visibility, leading to long-term benefits.

Peak Data

East Wind • 2 HN points • 25 Oct 23
🕹 Technology AI Data Quality ML models Training Data
  1. The quality and percentage of human-generated data on the internet may have reached a peak, affecting the efficacy of future AI models.
  2. Models may face challenges with outdated training data and lack of relevant information for solving newer problems.
  3. Potential solutions include leveraging RAG models, proactive data contribution by platform vendors, and maintaining incentives for human contributions on user-generated content platforms.

Improving data quality in online studies

UX Psychology • 19 implied HN points • 23 Nov 21
🔬 Science Data Quality Data Collection Quality Assurance
  1. In online studies, factors like distractions, poor equipment, and cheating can impact data quality.
  2. Engagement levels, accuracy, outliers, and speed of responses are key indicators to assess data quality in online studies.
  3. Strategies like consistency measures, attention checks, bot detection, and serious response checks can help improve data quality in online studies.

The extensive guide for Server-Side Tracking

timo's substack • 1 HN point • 16 May 23
🕹 Technology Data Quality Implementation Architecture Control
  1. Take control of event data by implementing server-side tracking for better data quality and faster implementation.
  2. Incorporate the development team in tracking projects from the start to achieve more effective server-side tracking implementations.
  3. Consider different strategies for implementing server-side tracking, such as close to the API layer, stream, database, third-party applications, or application code.