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

Precisely

AUGUST 3, 2023

They reported facing challenges to the success of their data programs — including cost (50%), lack of effective data management tools (45%), poor data literacy/program adoption (41%), and skills shortages (36%) as well as poor data quality (36%).

DataOps 98

DataOps Data Quality Analytics Analytics 98

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Organizations must support quality enhancement across structured, semistructured and unstructured data alike.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

Precisely

FEBRUARY 10, 2023

Read Here are the top data trends our experts see for 2023 and beyond. DataOps Delivers Continuous Improvement and Value In IDC’s spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment , Research Director Stewart Bond highlights the advent of DataOps as a distinct discipline.

DataOps 52

DataOps Data Observability ML ML 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Precisely

FEBRUARY 9, 2024

For example: data integration captures the necessary data from diverse sources and makes it available in real time data governance provides positive control over data storage, access, use, etc., That approach assumes that good data quality will be self-sustaining.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Precisely

SEPTEMBER 18, 2023

Regardless of your industry or role in the business, data has a massive role to play – from operations managers who rely on downstream analytics for important business decisions, to executives who want an overview of how the company is performing for key stakeholders. Trusted data is crucial, and data observability makes it possible.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

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

JUNE 26, 2023

Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. Only 46% of respondents rate their data quality as “high” or “very high.” Only 46% of respondents rate their data quality as “high” or “very high.” The biggest surprise?

Data Quality 52

Data Quality Data Silos Analytics Analytics 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

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

Precisely

DECEMBER 12, 2023

For some time now, data observabilit y has been an important factor in software engineering, but its application within the realm of data stewardship is a relatively new phenomenon. Data observability is a foundational element of data operations (DataOps). Data observability helps you manage data quality at scale.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

Alation

JANUARY 25, 2022

Automated Data Orchestration (AKA DataOps). Automated data orchestration interweaves data with connecting processes. DataOps is the leading process concept in data today. See Gartner’s “ How DataOps Amplifies Data and Analytics Business Value ”). Alation Data Catalog for the data fabric.

DataOps 52

DataOps SQL ML ML 52

Precisely

JULY 15, 2024

Multiple domains often need to share data assets. Quality and formatting may differ with more autonomous domain teams producing data assets, making interoperability difficult and data quality guarantees elusive. Data discoverability and reusability.

Data Governance 72

Data Governance DataOps Data Silos Data Pipeline 72

Alation

MAY 31, 2023

In “The House of Data”, I’ll share a practical framework for data governance, sharing how catalog-led governance empowers data stewards to implement a data management system that addresses organizational needs (including data quality, privacy, compliance, security and DataOps – with an eye to enablement).

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

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. So we have to be very careful about giving the domains the right and authority to fix data quality. Tools became stacks.

Data Warehouse 52

Data Warehouse Data Engineering Data Engineering Data Engineer 52

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

AWS Machine Learning Blog

JULY 31, 2023

He works with customers to realize their data analytics and machine learning goals through adoption of DataOps and MLOps practices and solutions. He designs modern application architectures based on microservices, serverless, APIs, and event-driven patterns.

ML 98

ML ML Data Preparation Machine Learning 98

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

phData

MAY 11, 2023

Many open-source and free tools exist, such as Flyway, Liquibase, schemachange, or DataOps. This can be done in many ways, such as using a data quality testing framework like Soda.io A schema management tool allows teams to house their code in a Git repository and automate the deployment of that code to multiple environments.

Database 52

Database SQL DataOps Data Warehouse 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