IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Completeness: Does the data comprise all relevant and available information?

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Data Quality Data Profiling Data Governance Machine Learning 74

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

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Big Data Big Data Data Engineer Data Engineering 91

Pickl AI

JULY 19, 2023

Every business, irrespective of the niche of operations is harnessing the power of data and make their strategies result-oriented. As compared to the earlier times, businesses are inundated with vast amounts of information. To harness this information in the best interest of the business, it is imperative to filter quality inputs.

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Pickl AI

OCTOBER 19, 2023

Hence, the quality of data is significant here. Quality data fuels business decisions, informs scientific research, drives technological innovations, and shapes our understanding of the world. The Relevance of Data Quality Data quality refers to the accuracy, completeness, consistency, and reliability of data.

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Pickl AI

OCTOBER 10, 2023

This involves implementing data validation processes, data cleansing routines, and quality checks to eliminate errors, inaccuracies, or inconsistencies. Reliable data is essential for making informed decisions and conducting meaningful analyses. Quality Data quality is about the reliability and accuracy of your data.

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Data Observability Data Quality Data Governance Data Pipeline 52

Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Pickl AI

OCTOBER 11, 2023

This process involves real-time monitoring and documentation to provide visibility on the data quality, thereby helping the organization detect and address data-related issues. Bigeye Its analytical prowess and data visualization capabilities will help Data Scientists make effective data-driven decision-making.

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Data Observability Data Quality Data Pipeline Data Governance 52

DagsHub

AUGUST 23, 2024

As a result, Gartner estimates that poor data quality costs organizations an average of $13 million annually. High-quality data significantly reduces the risk of costly errors, and the resulting penalties or legal issues. Data Governance Data governance is the foundation of any data quality framework.

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Precisely

SEPTEMBER 11, 2023

By 2025, 50% of data and analytics leaders will be using augmented MDM and active metadata to enhance their capabilities – demonstrating that beyond data quality, automation is also in demand for data governance, data catalog, and security solutions. User interface assistants: Find the information you need, faster.

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Data Quality AI AI Data Profiling 52

Alation

DECEMBER 21, 2022

What Is Master Data Management (MDM)? MDM is a discipline that helps organize critical information to avoid duplication, inconsistency, and other data quality issues. Transactional systems and data warehouses can then use the golden records as the entity’s most current, trusted representation.

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Data Quality Data Warehouse Data Profiling Data Governance 52

The MLOps Blog

JUNE 27, 2023

Can you debug system information? Metadata management : Robust metadata management capabilities enable you to associate relevant information, such as dataset descriptions, annotations, preprocessing steps, and licensing details, with the datasets, facilitating better organization and understanding of the data.

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Pickl AI

JULY 24, 2024

Missing Data Incomplete datasets with missing values can distort the training process and lead to inaccurate models. Missing data can occur due to various reasons, such as data entry errors, loss of information, or non-responses in surveys. To achieve this, a comprehensive approach is essential.

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Alation

DECEMBER 7, 2021

This platform should: Connect to diverse data sources (on-prem, hybrid, legacy, or modern). Extract data quality information. Monitor data anomalies and data drift. Track how data transforms, noting unexpected changes during its lifecycle. Alation and Bigeye have partnered to deliver this platform.

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Data Quality Data Observability Data Pipeline Data Profiling 52

Pickl AI

AUGUST 30, 2024

Data Quality Assessment Evaluate the quality of existing data and address any issues before migration. This may involve data profiling and cleansing activities to improve data accuracy. Testing should include validating data integrity and performance in the new environment.

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Data Quality Data Governance Azure Database 52

phData

JUNE 26, 2023

When errors do happen, we want customers (or employees leveraging the toolkit at a customer) to have the ability to provide enough information back to the development team so we can triage and resolve the issue. To make this easier, we have added a diagnose command to the toolkit! As with any conversion tool, you have a source and a target.

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SQL Data Profiling Data Pipeline Data Governance 52

phData

DECEMBER 29, 2022

The phData Toolkit continues to have additions made to it as we work with customers to accelerate their migrations , build a data governance practice , and ensure quality data products are built. Some of the major improvements that have been made are within the data profiling and validation components of the Toolkit CLI.

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SQL Database Database Administration Data Profiling 52

phData

AUGUST 17, 2023

This enhances the reliability and resilience of the data pipeline. Data Governance and Compliance Orchestration tools can facilitate metadata management, which is vital for effective data governance. They can automatically capture and store metadata about data sources, transformations, and destinations.

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Snorkel AI

MAY 12, 2023

For example, when customers log onto our website or mobile app, our conversational AI capabilities can help find the information they may want. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models.

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Snorkel AI

MAY 12, 2023

For example, when customers log onto our website or mobile app, our conversational AI capabilities can help find the information they may want. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models.

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Machine Learning Machine Learning ML ML 52

Dataversity

DECEMBER 16, 2020

According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].

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Dataversity

FEBRUARY 1, 2021

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].

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Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

Dataversity

APRIL 22, 2022

In today’s digital world, data is undoubtedly a valuable resource that has the power to transform businesses and industries. As the saying goes, “data is the new oil.” However, in order for data to be truly useful, it needs to be managed effectively.

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Alation

JULY 6, 2021

Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. And one enterprise alone can generate a world of data.

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Alation

AUGUST 26, 2021

By answering key questions around the who, what, where and when of a given data asset, DI paints a picture of why folks might use it, educating on that asset’s reliability and relative value. Insights into how an asset’s been used in the past inform how it might be intelligently applied in the future. Why keep data at all?

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phData

SEPTEMBER 1, 2023

Healthcare and Life Sciences (HCLS) companies face a multitude of challenges when it comes to managing and analyzing data. From the sheer volume of information to the complexity of data sources and the need for real-time insights, HCLS companies constantly need to adapt and overcome these challenges to stay ahead of the competition.

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