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. Thats where data integration comes in.

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Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

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IBM Data Science in Practice

APRIL 26, 2024

In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption. Understanding Data Fabric and IBM Knowledge Catalog A data fabric is an architectural blueprint that helps transcending traditional data silos and complexities.

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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.

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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?

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Flipboard

JANUARY 7, 2025

Almost half of AI projects are doomed by poor data quality, inaccurate or incomplete data categorization, unstructured data, and data silos. Avoid these 5 mistakes

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ODSC - Open Data Science

APRIL 28, 2023

As critical data flows across an organization from various business applications, data silos become a big issue. The data silos, missing data, and errors make data management tedious and time-consuming, and they’re barriers to ensuring the accuracy and consistency of your data before it is usable by AI/ML.

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Flipboard

NOVEMBER 22, 2024

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

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Dataversity

AUGUST 4, 2023

Generating actionable insights across growing data volumes and disconnected data silos is becoming increasingly challenging for organizations. Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360.

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AWS Machine Learning Blog

APRIL 3, 2025

By analyzing their data, organizations can identify patterns in sales cycles, optimize inventory management, or help tailor products or services to meet customer needs more effectively. The company aims to integrate additional data sources, including other mission-critical systems, into ODAP.

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Tableau

OCTOBER 7, 2024

Spencer Czapiewski October 7, 2024 - 9:59pm Madeline Lee Product Manager, Technology Partners Enabling teams to make trusted, data-driven decisions has become increasingly complex due to the proliferation of data, technologies, and tools.

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IBM Data Science in Practice

NOVEMBER 28, 2022

Insights from data gathered across business units improve business outcomes, but having heterogeneous data from disparate applications and storages makes it difficult for organizations to paint a big picture. How can organizations get a holistic view of data when it’s distributed across data silos?

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IBM Journey to AI blog

MAY 9, 2024

At the same time, implementing a data governance framework poses some challenges, such as data quality issues, data silos security and privacy concerns. Data quality issues Positive business decisions and outcomes rely on trustworthy, high-quality data.

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IBM Journey to AI blog

OCTOBER 22, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges, specifically as the growth of data spans multiple formats: structured, semistructured and unstructured.

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IBM Journey to AI blog

JANUARY 23, 2023

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

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Precisely

JANUARY 17, 2023

As they do so, access to traditional and modern data sources is required. Poor data quality and information silos tend to emerge as early challenges. Customer data quality, for example, tends to erode very quickly as consumers experience various life changes.

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Data Quality Machine Learning Machine Learning Data Silos 97

Alation

APRIL 13, 2021

Much of his work focuses on democratising data and breaking down data silos to drive better business outcomes. In this blog, Chris shows how Snowflake and Alation together accelerate data culture. He shows how Texas Mutual Insurance Company has embraced data governance to build trust in data.

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Data Governance Data Silos Data Quality 98

Dataversity

OCTOBER 25, 2021

It’s common for enterprises to run into challenges such as lack of data visibility, problems with data security, and low Data Quality. But despite the dangers of poor data ethics and management, many enterprises are failing to take the steps they need to ensure quality Data Governance.

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Data Governance Data Quality Data Silos Cloud Data 98

Dataconomy

APRIL 6, 2023

While data democratization has many benefits, such as improved decision-making and enhanced innovation, it also presents a number of challenges. From lack of data literacy to data silos and security concerns, there are many obstacles that organizations need to overcome in order to successfully democratize their data.

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Dataversity

DECEMBER 24, 2020

.” The series covers some of the most prominent questions in Data Management such as Master Data, the difference between Master Data and MDM, “truth” versus “meaning” in data, Data Quality, and so much […].

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Data Quality Data Silos Data Governance 97

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Analytics data catalog. Data quality and lineage. Metadata management. Orchestration.

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Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Analytics data catalog. Data quality and lineage. Metadata management. Orchestration.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Alation

OCTOBER 5, 2021

It’s on Data Governance Leaders to identify the issues with the business process that causes users to act in these ways. Inconsistencies in expectations can create enormous negative issues regarding data quality and governance. Roadblock #3: Silos Breed Misunderstanding. Picking the Right Data Governance Tools.

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Data Governance Data Quality Data Silos Analytics 96

Precisely

JUNE 27, 2024

Incremental data updates can be tricky to implement as part of an ETL integration solution, but the time it takes is worth it. Maximize data quality The old saying “crap in, crap out” applies to ETL integration. The post ETL Best Practices for Optimal Integration appeared first on Precisely.

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Precisely

SEPTEMBER 8, 2023

For data teams, that often leads to a burgeoning inbox of new projects, as business users throughout the organization strive to discover new insights and find new ways of creating value for the business. In the meantime, data quality and overall data integrity suffer from neglect.

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Precisely

NOVEMBER 11, 2024

This involves integrating customer data across various channels – like your CRM systems, data warehouses, and more – so that the most relevant and up-to-date information is used consistently in your customer interactions. Focus on high-quality data. Data quality is essential for personalization efforts.

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Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.

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Precisely

JUNE 26, 2023

Those who have already made progress toward that end have used advanced analytics tools that work outside of their application-based data silos. Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. The biggest surprise?

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Data Quality Data Silos Analytics Analytics 52

Precisely

NOVEMBER 11, 2024

This involves integrating customer data across various channels – like your CRM systems, data warehouses, and more – so that the most relevant and up-to-date information is used consistently in your customer interactions. Focus on high-quality data. Data quality is essential for personalization efforts.

Data Silos 52

Data Silos Data Warehouse Artificial Intelligence Artificial Intelligence 52

Precisely

NOVEMBER 13, 2024

Challenges around data literacy, readiness, and risk exposure need to be addressed – otherwise they can hinder MDM’s success Businesses that excel with MDM and data integrity can trust their data to inform high-velocity decisions, and remain compliant with emerging regulations. Today, you have more data than ever.

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Precisely

JANUARY 23, 2024

The report concluded that there are reliable, data-driven reasons why companies should invest in building or maturing their data governance programs. The topmost value-generating benefit, according to respondents with mature programs, is the ability of such initiatives to strengthen overall data quality.

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Precisely

SEPTEMBER 12, 2023

As a proud member of the Connect with Confluent program , we help organizations going through digital transformation and IT infrastructure modernization break down data silos and power their streaming data pipelines with trusted data. Book your meeting with us at Confluent’s Current 2023. See you in San Jose!

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Data Silos Apache Kafka Data Pipeline Data Quality 52

Dataversity

FEBRUARY 6, 2023

Organizations seeking responsive and sustainable solutions to their growing data challenges increasingly lean on architectural approaches such as data mesh to deliver information quickly and efficiently.

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Data Silos Data Quality Cloud Data 52

Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Insufficient skills, limited budgets, and poor data quality also present significant challenges. To learn more, read our ebook.

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Precisely

AUGUST 8, 2024

But the truth is, it’s harder than ever for organizations to maintain that level of data quality. As your business applications grow, so do fragmented data silos that hold you back. How confident are you that your data management practices are up to the task of supporting your evolving objectives?

Data Silos 52

Data Silos Data Quality 52

Dataconomy

APRIL 6, 2023

While data democratization has many benefits, such as improved decision-making and enhanced innovation, it also presents a number of challenges. From lack of data literacy to data silos and security concerns, there are many obstacles that organizations need to overcome in order to successfully democratize their data.

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Data Governance Data Silos Data Analysis Data Analysis 70

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.

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