Alation

JANUARY 20, 2022

In Ryan’s “9-Step Process for Better Data Quality” he discussed the processes for generating data that business leaders consider trustworthy. To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute.

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Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications. What is Data Observability and its Significance?

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Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

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AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies. stored: where is it located?

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IBM Journey to AI blog

AUGUST 23, 2023

Because of this, when we look to manage and govern the deployment of AI models, we must first focus on governing the data that the AI models are trained on. This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. and watsonx.data.

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MAY 4, 2023

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may require consider your data observability strategy. Is your data governance structure up to the task?

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Dataversity

APRIL 25, 2022

If data processes are not at peak performance and efficiency, businesses are just collecting massive stores of data for no reason. Data without insight is useless, and the energy spent collecting it, is wasted. The post Solving Three Data Problems with Data Observability appeared first on DATAVERSITY.

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Data Science Dojo

OCTOBER 10, 2023

IMPACT 2023- The Data Observability Summit (Virtual event – November 8) Focus on Data and AI : The summit will illuminate how contemporary technical teams are crafting impactful and performant data and AI products that businesses can rely on.

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Dataversity

AUGUST 15, 2022

So, what can you do to ensure your data is up to par and […]. The post Data Trustability: The Bridge Between Data Quality and Data Observability appeared first on DATAVERSITY. You might not even make it out of the starting gate.

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The Data Administration Newsletter

NOVEMBER 14, 2023

Data empowers businesses to gain valuable insights into industry trends and fosters profitable decision-making for long-term growth. No wonder businesses of all sizes are switching to data-driven culture from conventional practices.

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Precisely

JANUARY 9, 2023

Read the Report Improving Data Integrity and Trust through Transparency and Enrichment Read this report to learn how organizations are responding to trending topics in data integrity. 2023 will continue to see a major shift in organizations increasing their investment in business-first data governance programs.

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Dataversity

JANUARY 19, 2024

Do you know the costs of poor data quality? Below, I explore the significance of data observability, how it can mitigate the risks of bad data, and ways to measure its ROI. Data has become […] The post Putting a Number on Bad Data appeared first on DATAVERSITY.

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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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IBM Data Science in Practice

NOVEMBER 28, 2022

Customer 360 : create a comprehensive view of client Multicloud data integration : integrate data across any hybrid and multicloud landscapes Data governance and privacy : automate to manage data trust, protection and compliance MLOps and trustworthy AI : enable an end-to-end AI workflow infused with data governance and privacy Data observability : (..)

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Precisely

MAY 8, 2023

Once you’ve connected key business goals and initiatives, it’s time to establish policies and procedures, including defining data ownership, establishing access controls, and managing data retention and deletion. Learn more Now, let’s talk about monitoring your data. How does it work for real-world use cases?

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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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JULY 12, 2024

Data integrity is based on four main pillars: Data integration : Regardless of its original source, on legacy systems, relational databases, or cloud data warehouses, data must be seamlessly integrated in order to gain visibility into all your data in a timely fashion.

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MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. Data Quality and Data Governance Insurance carriers cannot effectively leverage artificial intelligence without first having a clear data strategy in place.

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NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Leverage AI to enhance governance. Take a proactive approach.

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Dataversity

DECEMBER 14, 2021

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

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Precisely

JULY 31, 2023

In the context of improving their organizations’ data integrity , respondents cite data quality and data integration as priorities for 2023 and as challenges to data integrity. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

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JUNE 6, 2023

That means finding and resolving data quality issues before they turn into actual problems in advanced analytics, C-level dashboards, or AI/ML models. Data Observability and the Holistic Approach to Data Integrity One exciting new application of AI for data management is data observability.

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Precisely

JULY 17, 2023

The Suite comprises seven interoperable cloud services: Data Quality, Data Integration, Data Observability, Data Governance, Data Enrichment, Geo Addressing, and Spatial Analytics.

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NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Leverage AI to enhance governance. Take a proactive approach.

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Precisely

MAY 18, 2023

Customer Voices from Trust ’23: the Precisely Data Integrity Summit Jean-Paul Otte from Degroof Petercam shares why data governance is essential to linking data to business value – and why improving data quality is the first step of any governance journey.

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MAY 31, 2023

Data Integrity Processes Run Where Data Lives Traditional data management solutions have required that data be brought to where the tools run. As a result, organizations have had to bring copies of their data to the tools – one copy for data quality, one copy for data governance, and so on.

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JULY 18, 2024

While data fabric is not a standalone solution, critical capabilities that you can address today to prepare for a data fabric include automated data integration, metadata management, centralized data governance, and self-service access by consumers.

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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. It primarily caters to large organizations with complex data environments.

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Alation

DECEMBER 7, 2021

Alation and Bigeye have partnered to bring data observability and data quality monitoring into the data catalog. Read to learn how our newly combined capabilities put more trustworthy, quality data into the hands of those who are best equipped to leverage it.

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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.

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phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Having model-level data validations along with implementing a data observability framework helps to address the data vault’s data quality challenges.

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IBM Journey to AI blog

DECEMBER 16, 2022

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.

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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. grant everyone self-service access to data.

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JUNE 29, 2023

Forty percent say they are taking steps involving people, including improving data literacy skills and addressing staffing and resources. Another 40% credit having a data governance program for enhancing decision-making data, while 30% employ data observability tools to proactively identify data issues.

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MARCH 14, 2023

Whatever your unique objectives may be, the Data Integrity Suite’s Data Quality module will play a critical role in your ongoing data integrity journey – ready to help you tackle new use cases with data that’s accurate, consistent, and fit for purpose where you need it most.

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APRIL 11, 2023

Replicate data into the data cloud of your choice using hundreds of connectors. Step 3: Observe the data With the data replicated to the cloud, you can observe the data using the Data Observability service.

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MAY 3, 2023

ET: Preventing Data Downtime with Effective Data Governance, Observability & Quality Strategies Data downtime is a significant challenge faced by organizations of all sizes and from all industries, resulting in lost revenue, missed opportunities, and reduced customer satisfaction.

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