Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. data pipelines) to support.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Managing this level of oversight requires adept handling of large volumes of data.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Precisely

FEBRUARY 9, 2024

Data Integrity for Compliance Remains in the Spotlight Data privacy and security concerns remain top of mind for organizations across industries. As consumer standards for protecting their personal identifiable information (PII) grow, so do the consequences for organizations that don’t live up to those expectations.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Dataconomy

MAY 26, 2023

Data quality Ensure that the right data sources are tapped within your organization to avoid unreliable results. Invest in data quality monitoring and management to detect and correct data defects, setting a strong foundation for better model predictions. Who co-pilots the co-pilots?

Data Quality 70

Data Quality AI AI DataOps 70

Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). It enables a big-picture understanding of the health of your organization’s data through continuous AI/ML-enabled monitoring – detecting anomalies throughout the data pipeline and preventing data downtime.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

Precisely

DECEMBER 12, 2023

quintillion exabytes of data every da y. That information resides in multiple systems, including legacy on-premises systems, cloud applications, and hybrid environments. It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

Alation

AUGUST 17, 2022

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? However, it has to be led and managed.

Data Governance 92

Data Governance Data Quality DataOps 92

Alation

MAY 31, 2023

The Data Governance & Information Quality Conference (DGIQ) is happening soon — and we’ll be onsite in San Diego from June 5-9. If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. The best part?

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

Alation

OCTOBER 12, 2021

Enterprise data analytics integrates data, business, and analytics disciplines, including: Data management. Data engineering. DataOps. … In the past, businesses would collect data, run analytics, and extract insights, which would inform strategy and decision-making. Evaluate and monitor data quality.

Analytics 52

Analytics Analytics Data Governance DataOps 52

AWS Machine Learning Blog

JULY 31, 2023

It considers precision and recall, providing a more informative evaluation metric that reflects the model’s ability to correctly classify positive instances and avoid false positives and false negatives. We perform data preparation and use feature engineering techniques to improve performance.

ML 98

ML ML Data Preparation Machine Learning 98

Dataversity

JUNE 29, 2022

The quality of the data you use in daily operations plays a significant role in how well you will generate valuable insights for your enterprise. You want to rely on data integrity to ensure you avoid simple mistakes because of poor sourcing or data that may not be correctly organized and verified.

Data Observability 98

Data Observability DataOps Data Quality Data Governance 98

Alation

AUGUST 11, 2022

DataOps sprung up to connect data sources to data consumers. The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. managing supply chains, providing better customer service, enabling doctors to make data-informed bedside decisions].

Data Warehouse 52

Data Warehouse Data Engineering Data Engineer Data Engineering 52

Dataversity

JUNE 23, 2021

The role of the chief data officer (CDO) has evolved more over the last decade than any of the C-suite. The post Speed Up AI Development by Hiring a Chief Data Officer appeared first on DATAVERSITY. Click to learn more about author Jitesh Ghai. As companies plan for a rebound from the pandemic, the CDO […].

AI 59

AI AI DataOps Data Quality 59

Dataversity

JULY 1, 2022

Businesses rely on data to drive revenue and create better customer experiences – […]. A 20-year-old article from MIT Technology Review tells us that good software “is usable, reliable, defect-free, cost-effective, and maintainable. And software now is none of those things.” Today, most businesses would beg to differ.

Analytics 52

Analytics Analytics DataOps Data Pipeline 52

Snorkel AI

OCTOBER 31, 2023

However, one of the fundamental ways to improve quality and thereby trust and safety for models with billions of parameters is to improve the training data quality. Higher quality curated data is very important to fine-tune these large multi-task models. Our researchers did it in two days.

AI 111

AI AI DataOps Data Governance 111

Snorkel AI

OCTOBER 31, 2023

However, one of the fundamental ways to improve quality and thereby trust and safety for models with billions of parameters is to improve the training data quality. Higher quality curated data is very important to fine-tune these large multi-task models. Our researchers did it in two days.

AI 103

AI AI DataOps Data Governance 103

Snorkel AI

OCTOBER 31, 2023

However, one of the fundamental ways to improve quality and thereby trust and safety for models with billions of parameters is to improve the training data quality. Higher quality curated data is very important to fine-tune these large multi-task models. Our researchers did it in two days.

AI 52

AI AI DataOps Data Governance 52