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.

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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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DataRobot Blog

OCTOBER 9, 2017

Excel has long been the tool for business analysts to perform lightweight data preparation tasks – identifying outliers and errors, aggregating values, and combining data into one spreadsheet for analytics. However, all too often, business users waste time using Excel to manually profile and process data.

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Data Preparation Data Profiling Data Governance Machine Learning 52

Dataconomy

FEBRUARY 23, 2023

Cloud computing: Cloud computing provides a scalable and cost-effective solution for managing and processing large volumes of data. Cloud providers offer various services such as storage, compute, and analytics, which can be used to build and operate big data systems.

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Big Data Big Data Data Engineering Data Engineering 91

Pickl AI

OCTOBER 19, 2023

This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. Define data ownership, access rights, and responsibilities within your organization. How Do You Fix Poor Data Quality?

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Data Quality Data Governance Data Warehouse Machine Learning 52

Pickl AI

OCTOBER 18, 2023

Data Enrichment Services Enrichment tools augment existing data with additional information, such as demographics, geolocation, or social media profiles. This enhances the depth and usefulness of the data. It defines roles, responsibilities, and processes for data management. How to Use AI in Quality Assurance?

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Precisely

JUNE 26, 2023

Forward-thinking businesses invest in digital transformation, cloud adoption, advanced analytics and predictive modeling, and supply chain resiliency. 2023 Data Integrity Trends & Insights Results from a Survey of Data and Analytics Professionals Read the report Here are some of the top takeaways that stood out to panelists.

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Pickl AI

OCTOBER 11, 2023

Key Features Benefit from the real-time surveillance thus, it helps in identifying potential issues in real-time It comes with advanced analytical capacities contributing to well-informed decision-making; Intuitively explore and grasp the intricacies of data. In such a case, you need to integrate with the Data Observability platform.

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IBM Journey to AI blog

JANUARY 5, 2023

Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable. Perform data profiling (the process of examining, analyzing and creating summaries of datasets). Creating a data architecture roadmap.

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Data Quality Data Lakes Data Warehouse Big Data 52

Alation

JANUARY 13, 2022

The definition we are going with here is Gartner’s and, to them, there is no single vendor that addresses the complete set of needs required to build a data fabric (at least not today). Gartner defines data fabric as a “design concept that serves as an integrated layer (fabric) of data and connecting processes.”.

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Data Lakes Data Governance Data Quality Data Warehouse 59

The MLOps Blog

JUNE 27, 2023

A self-service infrastructure portal for infrastructure and governance. Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. It could help you detect and prevent data pipeline failures, data drift, and anomalies.

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Precisely

SEPTEMBER 11, 2023

By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends, data relationships, and error resolution.

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Alation

DECEMBER 21, 2022

Early on, analysts used data catalogs to find and understand data more quickly. Increasingly, data catalogs now address a broad range of data intelligence solutions, including self-service analytics , data governance , privacy , and cloud transformation.

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Data Quality Data Warehouse Data Profiling Data Governance 52

Dataversity

FEBRUARY 1, 2021

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].

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Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

Alation

JULY 6, 2021

Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. And one enterprise alone can generate a world of data.

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phData

SEPTEMBER 1, 2023

Thankfully, Sigma Computing and Snowflake Data Cloud provide powerful tools for HCLS companies to address these data analytics challenges head-on. In this blog, we’ll explore 10 pressing data analytics challenges and discuss how Sigma and Snowflake can help.

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Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

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