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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Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Dataversity

AUGUST 15, 2022

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.

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Dataversity

NOVEMBER 7, 2022

Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality.

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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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DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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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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Dataversity

MARCH 13, 2023

These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another. As these pipelines become more complex, it’s important […] The post Data Observability vs. Monitoring vs. Testing appeared first on DATAVERSITY.

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Precisely

JULY 31, 2023

For the report, more than 450 data and analytics professionals worldwide were surveyed about the state of their data programs. 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.

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IBM Journey to AI blog

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

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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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Dataversity

JUNE 29, 2022

You want to rely on data integrity to ensure you avoid simple mistakes because of poor sourcing or data that may not be correctly organized and verified. The post Data Observability and Its Impact on the Data Operations Lifecycle appeared first on DATAVERSITY. That requires the […].

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Precisely

MARCH 14, 2023

Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms. eBook 4 Ways to Measure Data Quality To measure data quality and track the effectiveness of data quality improvement efforts you need data.

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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. Extract data quality information.

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Data Quality Data Observability Data Pipeline Data Profiling 52

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. Do we have end-to-end data pipeline control?

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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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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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Precisely

JANUARY 9, 2023

With this in mind, below are some of the top trends for data-driven decision-making we can expect to see over the next 12 months. 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.

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Precisely

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. Plan for data quality and governance of AI models from day one.

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Precisely

MAY 8, 2023

Reduce errors, save time, and cut costs with a proactive approach You need to make decisions based on accurate, consistent, and complete data to achieve the best results for your business goals. That’s where the Data Quality service of the Precisely Data Integrity Suite can help. How does it work for real-world use cases?

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Precisely

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. Plan for data quality and governance of AI models from day one.

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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. Over 10,000 people from all over the world attended the event.

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IBM Data Science in Practice

NOVEMBER 28, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

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Precisely

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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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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Precisely

MAY 31, 2023

When attempting to build a data strategy, the primary obstacle organizations face is a lack of resources. Teams are building complex, hybrid, multi-cloud environments, moving critical data workloads to the cloud, and addressing data quality challenges.

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Precisely

JUNE 6, 2023

As the scale and scope of data continue to increase, that creates new challenges with respect to compliance, governance, and data quality. To create more value from data, organizations must take a very proactive approach to data integrity.

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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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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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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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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. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

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Precisely

SEPTEMBER 11, 2023

It provides a unique ability to automate or accelerate user tasks, resulting in benefits like: improved efficiency greater productivity reduced dependence on manual labor Let’s look at AI-enabled data quality solutions as an example. Problem: “We’re unsure about the quality of our existing data and how to improve it!”

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Precisely

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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IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

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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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Dataversity

MAY 26, 2023

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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Precisely

JUNE 5, 2023

As more organizations prioritize data-driven decision-making, the pressure mounts for data teams to provide the highest quality data possible for the business. Reach new levels of data quality and deeper analysis – faster So then, what are the options for data practitioners?

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