IBM Journey to AI blog

JULY 13, 2023

Together, these factors determine the reliability of the organization’s data. Data quality uses those criteria to measure the level of data integrity and, in turn, its reliability and applicability for its intended use. Reduced data quality can result in productivity losses, revenue decline and reputational damage.

Data Quality 74

Data Quality Data Profiling Data Governance Analytics 74

Alation

MAY 16, 2023

A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? The answer?

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Dataconomy

MARCH 26, 2025

Creation of reliable datasets: Prepares datasets for analytics use cases, ensuring reliability for thorough analysis. Regulatory compliance: Supports organizations in meeting compliance standards by allowing for the removal of sensitive information before data loading.

ETL 91

ETL Data Pipeline Business Intelligence Business Intelligence 91

IBM Data Science in Practice

NOVEMBER 28, 2022

As organizations steer their business strategies to become data-driven decision-making organizations, data and analytics are more crucial than ever before. The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

Precisely

SEPTEMBER 30, 2024

Key Takeaways By deploying technologies that can learn and improve over time, companies that embrace AI and machine learning can achieve significantly better results from their data quality initiatives. Here are five data quality best practices which business leaders should focus.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

IBM Data Science in Practice

DECEMBER 7, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

Data Quality 130

Data Quality Data Pipeline Data Preparation ETL 130

Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

Data Observability 52

Data Observability Data Quality Data Governance Data Pipeline 52

Precisely

FEBRUARY 20, 2025

Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure data quality and governance, and continuously optimize your integration processes.

Data Silos 69

Data Silos AI AI Data Quality 69

Alation

DECEMBER 7, 2021

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Does the quality of this dataset meet user expectations?

Data Quality 52

Data Quality Data Pipeline Data Silos Data Governance 52

IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

Alation

MAY 24, 2022

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

Data Quality 52

Data Quality Data Pipeline DataOps Analytics 52

Precisely

MAY 4, 2023

Systems and data sources are more interconnected than ever before. A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management. Is your data governance structure up to the task? Read What Is Data Observability?

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? This ensures that the data which will be used for ML is accurate, reliable, and consistent.

ETL 59

ETL Data Pipeline ML ML 59

Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms. Cloud-native data execution is just the beginning.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

Dataversity

MARCH 13, 2023

Companies are spending a lot of money on data and analytics capabilities, creating more and more data products for people inside and outside the company. These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another.

Data Observability 98

Data Observability Data Pipeline Analytics Analytics 98

AWS Machine Learning Blog

APRIL 3, 2025

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. The robust security features provided by Amazon S3, including encryption and durability, were used to provide data protection.

AWS 82

AWS Data Governance Data Silos SQL 82

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Below are 20 essential tools every data engineer should know.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

OCTOBER 22, 2024

Indeed, IDC has predicted that by the end of 2024, 65% of CIOs will face pressure to adopt digital tech , such as generative AI and deep analytics. 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.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

Dataconomy

MAY 26, 2025

As businesses increasingly depend on big data to tailor their strategies and enhance decision-making, the role of these engineers becomes more crucial. They not only manage extensive data architectures but also pave the way for effective data analytics and innovative solutions. What is a big data engineer?

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

Smart Data Collective

SEPTEMBER 21, 2021

Data fabric is an architecture and set of data services that provide capabilities to seamlessly integrate and access data from multiple data sources like on-premise and cloud-native platforms. The data can also be processed, managed and stored within the data fabric. Data quality and governance.

Data Quality 131

Data Quality Data Pipeline Database Internet of Things 131

Alation

MAY 16, 2023

A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? The answer?

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Precisely

JUNE 5, 2023

Reach new levels of data quality and deeper analysis – faster So then, what are the options for data practitioners? You have a list of potential customers in your cloud environment, but the data quality isn’t quite at the level you need.

Analytics 52

Analytics Analytics Data Quality Data Pipeline 52

The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. Your data team can manage large-scale, structured, and unstructured data with high performance and durability.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality ML ML AI 52

AWS Machine Learning Blog

JANUARY 15, 2025

Understanding customer satisfaction and areas needing improvement from raw data is complex and often requires advanced analytical tools. Transactional sales data Amazon Relational Database Service (Amazon RDS) for PostgreSQL is used to hold transactional reports of vehicles on a quarterly or monthly basis.

AWS 100

AWS SQL AI AI 100

Smart Data Collective

JULY 15, 2021

With data catalogs, you won’t have to waste time looking for information you think you have. Once your information is organized, a data observability tool can take your data quality efforts to the next level by managing data drift or schema drift before they break your data pipelines or affect any downstream analytics applications.

Data Quality 133

Data Quality Database Data Pipeline Data Observability 133

Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Precisely

JANUARY 9, 2023

More sophisticated data initiatives will increase data quality challenges Data quality has always been a top concern for businesses, but now the use cases for it are evolving. As data initiatives become more sophisticated, organizations will uncover new data quality challenges.

Data Governance 81

Data Governance Data Quality Data Observability Data Pipeline 81

AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

AWS 124

AWS Natural Language Processing Machine Learning Machine Learning 124

Dataconomy

MAY 16, 2023

Skills and qualifications required for the role To excel as a machine learning engineer, individuals need a combination of technical skills, analytical thinking, and problem-solving abilities. They work with raw data, transform it into a usable format, and apply various analytical techniques to extract actionable insights.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

AWS Machine Learning Blog

DECEMBER 15, 2023

Amazon SageMaker Canvas is a no-code ML workspace offering ready-to-use models, including foundation models, and the ability to prepare data and build and deploy custom models. In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics.

Machine Learning 120

Machine Learning Machine Learning AWS ML 120

Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

IBM Journey to AI blog

MAY 9, 2024

How can a healthcare provider improve its data governance strategy, especially considering the ripple effect of small changes? Data lineage can help.With data lineage, your team establishes a strong data governance strategy, enabling them to gain full control of your healthcare data pipeline.

Data Governance 85

Data Governance Data Silos Data Quality Predictive Analytics 85

IBM Data Science in Practice

MARCH 8, 2023

— Features In machine learning, a feature is data that is used as the input for ML models to make predictions. Source: Advancing Analytics Data scientists and data engineers often spend a large amount of their time crafting features, as they are the basic building blocks of datasets. Spark, Flink, etc.)

Machine Learning 264

Machine Learning Machine Learning ML ML 264