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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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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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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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

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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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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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 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

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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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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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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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?

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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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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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IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. Effective data quality management is crucial to mitigating these risks.

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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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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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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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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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Pickl AI

OCTOBER 6, 2024

This phase is crucial for enhancing data quality and preparing it for analysis. Transformation involves various activities that help convert raw data into a format suitable for reporting and analytics. Normalisation: Standardising data formats and structures, ensuring consistency across various data sources.

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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.

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Alation

MAY 31, 2023

How is the data architecture role evolving? As data governance gains importance, data architects must work with data stewards and owners to establish policies, develop governance frameworks, implement data quality programs, and provide guidance to other data professionals.

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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.

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Precisely

SEPTEMBER 26, 2024

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. Building data literacy across your organization empowers teams to make better use of AI tools. The impact?

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Women in Big Data

FEBRUARY 1, 2023

She goes on to explain the one of the most beneficial features of One Data’s enabling technology, One Data Cartography , is record linkage combined with data quality. This feature enables holistic and seamless data tracking across system boundaries, based on algorithms and automatic checks for quality anomalies.

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Precisely

JANUARY 25, 2023

Data Integrity checks and best practices support data management as both strategic and tactical processes that enable companies to improve compliance, reduce costs, transform their customer relationships, and stay on the leading edge of innovation. This post examines the practical implications of poor data integrity.

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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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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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Pickl AI

JUNE 28, 2023

Understanding Data Integration in Data Mining Data integration is the process of combining data from different sources. Thus creating a consolidated view of the data while eliminating data silos. Data Quality: It provides mechanisms to cleanse and transform data.

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phData

SEPTEMBER 20, 2023

A data mesh is a decentralized approach to data architecture that’s been gaining traction as a solution to the challenges posed by large and complex data ecosystems. It’s all about breaking down data silos, empowering domain teams to take ownership of their data, and fostering a culture of data collaboration.

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Precisely

MARCH 4, 2024

How it’s done : An AI recommender system is a sophisticated technology that leverages AI and vast amounts of user data – like past preferences, behaviors, and interactions – to suggest tailored products, content, or services. You need to break down data silos and integrate critical data from all relevant sources.

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IBM Journey to AI blog

JANUARY 25, 2023

While operational data runs day-to-day business operations, gaining insights and leveraging data across business processes and workflows presents a well-known set of data governance challenges that technology alone cannot solve. Silos exist naturally when data is managed by multiple operational systems.

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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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Precisely

MAY 16, 2024

You need to break down data silos and integrate critical data from all relevant sources into Amazon Web Services (AWS). Fuel your AI applications with trusted data to power reliable results. You’re not alone. This ensures your AI models can access a comprehensive dataset, minimizing bias and enhancing accuracy.

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Alation

MARCH 21, 2023

While this industry has used data and analytics for a long time, many large travel organizations still struggle with data silos , which prevent them from gaining the most value from their data. What is big data in the travel and tourism industry?

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Dataversity

OCTOBER 22, 2021

.” 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 more. […].

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