Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. So why are organizations not able to scale governance? Meet Governance Requirements.

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IBM Data Science in Practice

JANUARY 2, 2025

These SQL assets can be used in downstream operations like data profiling, analysis, or even exporting to other systems for further processing. Conclusion Creating microsegments represents a significant advancement in data fabric capabilities in CP4D. With this, businesses can unlock granular insights with minimal effort.

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Dataconomy

DECEMBER 17, 2024

Actionable steps: Inventory existing data : Identify what data is currently available and assess its quality. Define data needs : Specify datasets, attributes, granularity, and update frequency. Address data governance : Ensure requirements include compliance with regulations like GDPR or CCPA.

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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. A high overall score indicates that a dataset is reliable, easily accessible, and relevant.

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Pickl AI

OCTOBER 10, 2023

Quality Data quality is about the reliability and accuracy of your data. High-quality data is free from errors, inconsistencies, and anomalies. To assess data quality, you may need to perform data profiling, validation, and cleansing to identify and address issues like missing values, duplicates, or outliers.

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Dataconomy

FEBRUARY 23, 2023

It includes various processes such as data profiling, data cleansing, and data validation. Master data management: Master data management involves creating a single, unified view of master data, such as customer data, product data, and supplier data.

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DataRobot Blog

OCTOBER 9, 2017

2) Data Profiling : To profile data in Excel, users typically create filters and pivot tables – but problems arise when a column contains thousands of distinct values or when there are duplicates resulting from different spellings. 3) Data Governance and Trust: With Excel, there is no actual audit trail or data lineage.

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Pickl AI

JULY 19, 2023

By maintaining clean and reliable data, businesses can avoid costly mistakes, enhance operational efficiency, and gain a competitive edge in their respective industries. Best Data Hygiene Tools & Software Trifacta Wrangler Pros: User-friendly interface with drag-and-drop functionality. Provides real-time data monitoring and alerts.

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Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

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Pickl AI

OCTOBER 19, 2023

This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. Define data ownership, access rights, and responsibilities within your organization. How Do You Fix Poor Data Quality?

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Pickl AI

OCTOBER 18, 2023

Data Enrichment Services Enrichment tools augment existing data with additional information, such as demographics, geolocation, or social media profiles. This enhances the depth and usefulness of the data. It defines roles, responsibilities, and processes for data management. How to Use AI in Quality Assurance?

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DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. If any of these is missing, the client data is considered incomplete.

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Pickl AI

OCTOBER 11, 2023

While they provide various data-related tools, they may also offer features related to Data Observability within their platform. Informatica might enable organizations to monitor data flows and ensure data quality as part of their data management processes. It aims to address issues promptly as they arise.

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The MLOps Blog

JUNE 27, 2023

It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. LakeFS facilitates data reproducibility, collaboration, and data governance within the data lake environment. Share features across the organization.

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IBM Journey to AI blog

JANUARY 5, 2023

Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable. Perform data profiling (the process of examining, analyzing and creating summaries of datasets).

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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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Alation

JANUARY 13, 2022

But how do you identify the best data, and best practices for using it? Metadata is the key to fueling data intelligence use cases across the board, including data search & discovery and data governance. In fact, data intelligence technologies support building a data fabric and realizing a data mesh.

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Precisely

JUNE 26, 2023

Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. As organizations embark on data quality improvement initiatives, they need to develop a clear definition of the metrics and standards suited to their specific needs and objectives.

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Pickl AI

JULY 24, 2024

Key Components of Data Quality Assessment Ensuring data quality is a critical step in building robust and reliable Machine Learning models. It involves a comprehensive evaluation of data to identify potential issues and take corrective actions. Conduct thorough data quality assessments to identify and prioritise issues.

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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.

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Alation

DECEMBER 7, 2021

Customers enjoy a holistic view of data quality metrics, descriptions, and dashboards, which surface where they need it most: at the point of consumption and analysis. Trust flags signal the trustworthiness of data, and data profiling helps users determine usability.

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Alation

DECEMBER 21, 2022

Early on, analysts used data catalogs to find and understand data more quickly. Increasingly, data catalogs now address a broad range of data intelligence solutions, including self-service analytics , data governance , privacy , and cloud transformation.

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phData

JUNE 26, 2023

Data Source Tool Updates The data source tool has a number of use cases, as it has the ability to profile your data sources and take the resulting JSON to perform whatever action you want to take. If you were to try and translate thousands of SQL statements manually, it would be tedious, expensive, and error-prone.

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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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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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Snorkel AI

MAY 12, 2023

Our data teams focus on three important processes. First, data standardization, then providing model-ready data for data scientists, and then ensuring there’s strong data governance and monitoring solutions and tools in place. For example, where verified data is present, the latencies are quantified.

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Snorkel AI

MAY 12, 2023

Our data teams focus on three important processes. First, data standardization, then providing model-ready data for data scientists, and then ensuring there’s strong data governance and monitoring solutions and tools in place. For example, where verified data is present, the latencies are quantified.

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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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phData

SEPTEMBER 1, 2023

Data Quality Inaccurate data can have negative impacts on patient interactions or loss of productivity for the business. Sigma and Snowflake offer data profiling to identify inconsistencies, errors, and duplicates.

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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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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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