Flipboard

SEPTEMBER 28, 2023

Revefi also sells an AI feature for insights, helping data teams move quickly to identify and correct “critical data issues.” ” The company is led by CEO Sanjay Agrawal , who was previously co-founder and head of the Seattle office for AI analytics company ThoughtSpot.

Data Observability 137

Data Observability Cloud Data Artificial Intelligence Artificial Intelligence 137

Precisely

NOVEMBER 18, 2024

Read on for the highlights from this panel – including actionable tips to ensure success in your 2025 data, analytics, and AI initiatives. Yoğurtçu also states that you need to “ensure that trusted data is served in a timely fashion.” Take a proactive approach. Leverage AI to enhance governance.

Analytics 59

Analytics Analytics AI Data Governance 59

Precisely

SEPTEMBER 18, 2023

Author’s note: this article about data observability and its role in building trusted data has been adapted from an article originally published in Enterprise Management 360. Is your data ready to use? That’s what makes this a critical element of a robust data integrity strategy. What is Data Observability?

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

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.

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

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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Precisely

DECEMBER 12, 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 prompt you to rethink your data observability strategy. In either case, the change can affect analytics.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

Precisely

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?

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

Dataversity

OCTOBER 21, 2022

Right now, over 12% of Fortune 1000 businesses have invested more than $500 million into big data and analytics, according to a NewVantage Partners survey. The post How Enterprises Can Leverage Data Observability for Digital Transformation appeared first on DATAVERSITY. But are they using it effectively?

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Data Observability Big Data Big Data Analytics 52

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.

Data Observability 52

Data Observability Data Silos Data Quality Data Governance 52

Data Science Dojo

OCTOBER 10, 2023

Generative AI and Data Storytelling (Virtual event | 27th September – 2023) A virtual event on generative AI and data storytelling. The event is hosted by Data Science Dojo and will be held on September 27, 2023. The speaker is Andrew Madson, a data analytics leader and educator.

AI 243

AI AI Data Observability Artificial Intelligence 243

Data Science Dojo

NOVEMBER 15, 2023

IMPACT is a great opportunity to learn from experts in the field, network with other professionals, and stay up-to-date on the latest trends and developments in data and AI. The summit will be held on November 8th, 2023.

Machine Learning 195

Machine Learning Machine Learning AI Big Data 195

IBM Journey to AI blog

FEBRUARY 2, 2024

Instead of developing a custom solution solely for the immediate concern, IBM sought a widely applicable data validation solution capable of handling not only this scenario but also potential overlooked issues. That is when I discovered one of our recently acquired products, IBM® Databand® for data observability.

Data Pipeline 109

Data Pipeline Data Observability Machine Learning Machine Learning 109

insideBIGDATA

APRIL 8, 2023

Grafana Labs, the company behind the open and composable operational dashboards, announced the findings of the Grafana Labs Observability Survey 2023.

Data Observability 369

Data Observability Big Data Big Data Analytics 369

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.

Data Governance 81

Data Governance Data Quality Data Observability Data Pipeline 81

ODSC - Open Data Science

FEBRUARY 28, 2023

With Tangent Works companies are able to solve challenges such as losses resulting from poor forecasting and missed ROI from time-series data. Blueprint Blueprint utilizes its expertise in data management, including analytics, migration, governance, and centralization to enable its clients to get the most from their data.

Data Science 96

Data Science Machine Learning Machine Learning AI 96

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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Data Pipeline Data Quality Data Preparation Data Governance 130

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!

Analytics 52

Analytics Analytics Data Quality Data Pipeline 52

IBM Journey to AI blog

OCTOBER 22, 2024

Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement. Indeed, IDC has predicted that by the end of 2024, 65% of CIOs will face pressure to adopt digital tech , such as generative AI and deep analytics.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

Precisely

MAY 8, 2023

In any strategic undertaking, trusted data is key to making better decisions that unlock new opportunities for your organization. One of the first steps in the process is to access and replicate the data you need to the cloud for robust analytics and reporting. But the process doesn’t end there.

Data Observability 52

Data Observability Data Governance Data Quality Machine Learning 52

Smart Data Collective

JULY 15, 2021

With data catalogs, you won’t have to waste time looking for information you think you have. Once your information is organized, a data observability tool can take your data quality efforts to the next level by managing data drift or schema drift before they break your data pipelines or affect any downstream analytics applications.

Data Quality 133

Data Quality Database Data Pipeline Data Observability 133

Precisely

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 12, 2024

For instance, you may have a database of customer names and addresses that is accurate and valid, but if you do not also have supporting data that gives you context about those customers and their relationship to your company, that database is not as useful as it could be. That is where data integrity comes into play.

Data Quality 105

Data Quality Data Governance Database Analytics 105

ODSC - Open Data Science

MAY 16, 2023

The mesh approach allows for more streamlined data-driven decisions and processes within specific departments and puts responsibility in the hands of the people who actually use the information. AI in Data Observability Automation has steadily become more common in data management software, but it’ll reach new heights in 2023.

Database 98

Database Data Science Data Lakes Data Observability 98

ODSC - Open Data Science

JULY 20, 2023

Video of the Week: Beyond Monitoring: The Rise of Data Observability Watch as Monte Carlo’s Shane Murray introduces “Data Observability” as the game-changing solution to the costly reality of broken data in advanced data teams.

Machine Learning 52

Machine Learning Machine Learning AI Data Lakes 52

Precisely

JULY 17, 2023

Data-driven decision-making has never been more in demand. A recent survey found that 77% of data and analytics professionals place data-driven decision-making as the leading goal for their data programs. And yet less than half (46%) rate their ability to trust data for decision-making as “high” or “very high.”

Data Governance 52

Data Governance Data Observability Data Quality Analytics 52

Alation

JANUARY 20, 2022

To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute. Quality policies for data and analytics set expectations about the “fitness for purpose” of artifacts across various dimensions. These integrations let us provide a whole product.

Data Quality 94

Data Quality Data Governance Data Profiling Clean Data 94

Precisely

SEPTEMBER 9, 2024

Key Takeaways: • Implement effective data quality management (DQM) to support the data accuracy, trustworthiness, and reliability you need for stronger analytics and decision-making. Embrace automation to streamline data quality processes like profiling and standardization. It reveals several critical insights: 1.

Data Quality 64

Data Quality Data Profiling Data Lakes Analytics 64

Alation

MAY 24, 2022

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. Alation has been leading the evolution of the data catalog to a platform for data intelligence.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

IBM Journey to AI blog

APRIL 10, 2023

Data fabric can help model, integrate and query data sources, build data pipelines, integrate data in near real-time, and run AI-driven applications.

AI 103

AI AI Data Governance Data Pipeline 103

Precisely

JULY 31, 2023

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

Data Quality 52

Data Quality Data Governance Analytics Analytics 52

Precisely

JUNE 29, 2023

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Data-driven decision-making is the top goal for 77% of data programs. One major finding?

Data Silos 52

Data Silos Analytics Data Observability Analytics 52

IBM Journey to AI blog

FEBRUARY 26, 2024

That’s why data pipeline observability is so important. Data lineage expands the scope of your data observability to include data processing infrastructure or data pipelines, in addition to the data itself.

Data Pipeline 112

Data Pipeline Data Engineering Data Engineer Data Engineering 112

IBM Journey to AI blog

JULY 13, 2023

Data quality uses those criteria to measure the level of data integrity and, in turn, its reliability and applicability for its intended use. Data integrity To achieve a high level of data integrity, an organization implements processes, rules and standards that govern how data is collected, stored, accessed, edited and used.

Data Quality 74

Data Quality Data Profiling Data Governance Machine Learning 74

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.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Precisely

MAY 31, 2023

In the same way that big cloud-platform providers offer simplified access to infrastructure, and data cloud providers like Databricks and Snowflake have vastly simplified access to data and analytics, modern data integrity tools must streamline and automate data integrity processes.

Data Quality 52

Data Quality Data Governance Data Observability ETL 52

AWS Machine Learning Blog

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. This dilemma hampers the creation of efficient models that use data to generate business-relevant insights.

ML 115

ML ML AWS Data Science 115

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. Increase metadata maturity.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52