Alation

FEBRUARY 20, 2020

The rise of data lakes, IOT analytics, and big data pipelines has introduced a new world of fast, big data. This new world of analytics has introduced a different set of complexities that have propelled IT organizations to build new technology infrastructures. How Data Catalogs Can Help.

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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.

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ODSC - Open Data Science

JANUARY 9, 2024

This will become more important as the volume of this data grows in scale. Data Governance Data governance is the process of managing data to ensure its quality, accuracy, and security. Data governance is becoming increasingly important as organizations become more reliant on data.

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Tableau

OCTOBER 8, 2021

However, most organizations struggle to become data driven. Data is stuck in siloes, infrastructure can’t scale to meet growing data needs, and analytics is still too hard for most people to use. Google's Cloud Platform is the enterprise solution of choice for many organizations with large and complex data problems.

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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.

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Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?

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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?

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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?

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Precisely

MARCH 28, 2025

The data generated was as varied as the departments relying on these applications. Some departments used IBM Db2, while others relied on VSAM files or IMS databases creating complex data governance processes and costly data pipeline maintenance.

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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IBM Journey to AI blog

FEBRUARY 26, 2024

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. That’s why data pipeline observability is so important.

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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. 2023 will continue to see a major shift in organizations increasing their investment in business-first data governance programs.

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Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

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IBM Journey to AI blog

OCTOBER 12, 2023

Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc. To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture.

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IBM Journey to AI blog

APRIL 10, 2023

To further the above, organizations should have the right foundation that consists of a modern data governance approach and data architecture. It’s becoming critical that organizations should adopt a data architecture that supports AI governance.

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Alation

FEBRUARY 20, 2020

Data is not automatically valuable by virtue of merely being available. Self-service analytics tools have been democratizing data-driven decision making, but also increasing the risk of inaccurate analysis and misinterpretation. Get the latest data cataloging news and trends in your inbox. Subscribe to Alation's Blog.

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Alation

FEBRUARY 20, 2020

We believe that this offering, Alation Tableau Edition, realizes the full promise of self-service analytics by allowing analysts to self-serve without making any of the errors of omission or commission that traditionally accompany an ungoverned data environment. We characterize this offering as Governance for Insight.

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Pickl AI

NOVEMBER 4, 2024

Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? ETL is vital for ensuring data quality and integrity. from 2025 to 2030.

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Pickl AI

OCTOBER 10, 2023

Significance of Data For delving deeper into the concepts of Data Observability and Data Quality, it’s important to understand the relevance of data in the modern business realm. Data empowers organizations to understand customer behavior, streamline operations, and make data-driven decisions.

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Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

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Pickl AI

OCTOBER 11, 2023

What is Data Observability? It is the practice of monitoring, tracking, and ensuring data quality, reliability, and performance as it moves through an organization’s data pipelines and systems. Data quality tools help maintain high data quality standards. Tools Used in Data Observability?

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Precisely

JULY 15, 2024

Key Takeaways Data Mesh is a modern data management architectural strategy that decentralizes development of trusted data products to support real-time business decisions and analytics. It’s time to rethink how you manage data to democratize it and make it more accessible. What is Data Mesh?

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Precisely

JUNE 5, 2023

While one approach is to move entire datasets from their source environment into a data quality tool and back again, it’s not the most efficient or ideal – particularly now, with countless businesses moving to the cloud for data and analytics initiatives. And great news , all of the content is now available on demand!

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IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

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Alation

SEPTEMBER 22, 2022

This new partnership will unify governed, quality data into a single view, granting all stakeholders total visibility into pipelines and providing them with a superior ability to make data-driven decisions. For people to understand and trust data, they need to see it in context. Data Pipeline Strategy.

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Precisely

SEPTEMBER 30, 2024

Companies should implement a data governance program to ensure the comprehensive application of best practices in data management Artificial intelligence and machine learning have matured, and cloud technologies are providing the robust on-demand computing power, scalability, and integration necessary to support advanced analytics.

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ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

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IBM Journey to AI blog

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. IBM Cloud Pak for Data Express solutions provide new clients with affordable and high impact capabilities to expeditiously explore and validate the path to become a data-driven enterprise. AI governance.

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IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

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IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

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phData

NOVEMBER 9, 2023

The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance. As this happens, spending shifts from ELT to analytics.

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Alation

APRIL 19, 2022

Modern data architectures, like cloud data warehouses and cloud data lakes , empower more people to leverage analytics for insights more efficiently. Access the resources your data applications need — no more, no less. Data Pipeline Automation. Data governance is the cornerstone of data modernization.

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AWS Machine Learning Blog

DECEMBER 18, 2024

All data generation and processing steps were run in parallel directly on the SageMaker HyperPod cluster nodes, using a unique working environment and highlighting the clusters versatility for various tasks beyond just training models. She specializes in AI operations, data governance, and cloud architecture on AWS.

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ODSC - Open Data Science

MAY 26, 2025

From predictive analytics to automation, AIs capabilities are reshaping industries at breakneck speed. Itneeds: Scalable cloud infrastructure Clean, structured data pipelines Skilled professionals who can fine-tune and monitormodels Cutting corners leads to short-term projects that fizzle out. AI doesnt operate in a vacuum.

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phData

JUNE 20, 2024

Data is integral to many processes and decisions when a data culture thrives. More complex analyses can be performed on trusted data as the analytics capability matures to gain further insight. Data as the foundation of what the business does is great – but how do you support that?

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Alation

AUGUST 3, 2021

But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. Data Operations, or DataOps, is like DevOps in that both are based in agile, continuous improvement thinking.

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Alation

APRIL 14, 2022

This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. The Cloud Data Migration Challenge. Data pipeline orchestration.

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