AWS Big Data

DECEMBER 4, 2024

It’s the preferred choice when customers need more control and customization over the data integration process or require complex transformations. This flexibility makes Glue ETL suitable for scenarios where data must be transformed or enriched before analysis. Check CloudWatch log events for the SEED Load.

Data Integration 97

Data Integration Data Lake Statistics Data-driven 97

IBM Big Data Hub

NOVEMBER 28, 2022

Metadata management performs a critical role within the modern data management stack. It helps blur data silos, and empowers data and analytics teams to better understand the context and quality of data. This, in turn, builds trust in data and the decision-making to follow. Improve data discovery.

Metadata 56

Metadata Data Quality Data-driven Data Governance 56

DataKitchen

OCTOBER 11, 2021

Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. While implementing a DataOps solution, we make sure that the pipeline has enough automated tests to ensure data quality and reduce the fear of failure. Data Completeness – check for missing data.

Testing 152

Testing Metadata Dashboards Statistics 152

erwin

APRIL 29, 2020

The CEO also makes decisions based on performance and growth statistics. An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Who are the data owners?

Metadata 111

Metadata Key Performance Indicator Data Governance Data Quality 111

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 363

Management Machine Learning Experimentation Metrics 363

Ontotext

MAY 26, 2021

This happens through the process of semantic annotation , where documents are tagged with relevant concepts and enriched with metadata , i.e., references that link the content to concepts, described in a knowledge graph. Evaluation is for AI systems what quality assurance (QA) is for software systems.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

IBM Big Data Hub

NOVEMBER 4, 2022

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. and its leading data observability offerings.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. Scalability and elasticity.

Metadata 56

Metadata Machine Learning Data Quality Statistics 56

AWS Big Data

JANUARY 18, 2024

Easily and securely prepare, share, and query data – This session shows how you can use Lake Formation and the AWS Glue Data Catalog to share data without copying, transform and prepare data without coding, and query data. DataZone automatically manages the permissions of your shared data in the DataZone projects.

Data Lake 104

Data Lake Metadata Data Governance Statistics 104

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Continuous pipeline monitoring with SPC (statistical process control). Results (i.e.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

AWS Big Data

DECEMBER 19, 2024

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

Wayne Yaddow

MARCH 5, 2025

Then, we validate the schema and metadata to ensure structural and type consistency and use golden or reference datasets to compare outputs to a recognized standard. Schema & Metadata Validation What It Is : Ensuring that incoming data and transformed data conform to expected schemas, data types, constraints, and metadata definitions.

Testing 52

Testing Data Transformation Statistics Metadata 52

Smarten

JANUARY 30, 2024

The right self-serve data prep solution can provide easy-to-use yet sophisticated data prep tools that are suitable for your business users, and enable data preparation techniques like: Connect and Mash Up Auto Suggesting Relationships JOINS and Types Sampling and Outliers Exploration, Cleaning, Shaping Reducing and Combining Data Insights (Data Quality (..)

Analytics 69

Analytics Visualization Data Quality Metadata 69

Alation

JULY 19, 2022

A data catalog can assist directly with every step, but model development. And even then, information from the data catalog can be transferred to a model connector , allowing data scientists to benefit from curated metadata within those platforms. How Data Catalogs Help Data Scientists Ask Better Questions.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Data Quality Metadata Data Governance 52

IBM Big Data Hub

JULY 6, 2022

Businesses of all sizes, in all industries are facing a data quality problem. 73% of business executives are unhappy with data quality and 61% of organizations are unable to harness data to create a sustained competitive advantage 1. The data observability difference . Instead, Databand.ai

Metadata 56

Metadata Data Quality Snapshot Cost-Benefit 56

DataKitchen

JUNE 24, 2021

Bergh added, “ DataOps is part of the data fabric. You should use DataOps principles to build and iterate and continuously improve your Data Fabric. Automate the data collection and cleansing process. Education is the Biggest Challenge. “We Take a show-me approach.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

IBM Big Data Hub

JANUARY 9, 2024

High variance in a model may indicate the model works with training data but be inadequate for real-world industry use cases. Limited data scope and non-representative answers: When data sources are restrictive, homogeneous or contain mistaken duplicates, statistical errors like sampling bias can skew all results.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Cloudera

SEPTEMBER 15, 2022

As shown above, the data fabric provides the data services from the source data through to the delivery of data products, aligning well with the first and second elements of the modern data platform architecture. In June 2022, Barr Moses of Monte Carlo expanded on her initial article defining data observability.

Data Lake 83

Data Lake Data Architecture Data-driven Data Warehouse 83

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted. Data Quality Obstacles.

Data Governance 40

Data Governance Risk Metadata Management 40

Alation

MAY 17, 2022

Those algorithms draw on metadata, or data about the data, that the catalog scrapes from source systems, along with behavioral metadata, which the catalog gathers based on human data usage. These profiles include basic statistics about the asset, like the number of rows and columns or the percentage of null values.

Metadata 52

Metadata Machine Learning Data Quality Reporting 52

Alation

JUNE 2, 2022

All Machine Learning uses “algorithms,” many of which are no different from those used by statisticians and data scientists. The difference between traditional statistical, probabilistic, and stochastic modeling and ML is mainly in computation. Recently, Judea Pearl said, “All ML is just curve fitting.” Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Data-driven Metrics 52

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Data-driven 52

TDAN

JANUARY 3, 2024

Edwards Deming, the father of statistical quality control, said: “If you can’t describe what you are doing as a process, you don’t know what you’re doing.” When looking at the world of IT and applied to the dichotomy of software and data, Deming’s quote applies to the software part of that pair.

Statistics 52

Statistics Software Data Quality IT 52

Andrew White

JANUARY 11, 2021

But we are seeing increasing data suggesting that broad and bland data literacy programs, for example statistics certifying all employees of a firm, do not actually lead to the desired change. New data suggests that pinpoint or targeted efforts are likely to be more effective. We do have good examples and bad examples.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Andrew White

JANUARY 9, 2022

We found anecdotal data that suggested things such as a) CDO’s with a business, more than a technical, background tend to be more effective or successful, and b) CDOs most often came from a business background, and c) those that were successful had a good chance at becoming CEO or CEO or some other CXO (but not really CIO).

Analytics 64

Analytics Measurement Data-driven Modeling 64

O'Reilly on Data

JULY 28, 2020

Acquiring data is often difficult, especially in regulated industries. Once relevant data has been obtained, understanding what is valuable and what is simply noise requires statistical and scientific rigor. Data Quality and Standardization. There are many excellent resources on data quality and data governance.

Marketing 364

Marketing Experimentation Metrics Testing 364

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Domino Data Lab

JULY 21, 2019

how modern approaches can be used to obtain better quality data, and at a lower cost, to help support evidence-based policy decisions. unique challenges of sharing confidential government microdata and the importance of access in generating high quality inference. What am I talking about with the new types of data?

Metadata 65

Metadata Statistics Risk Machine Learning 65

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Anmut

OCTOBER 29, 2020

As a result, concerns of data governance and data quality were ignored. The direct consequence of bad quality data is misinformed decision making based on inaccurate information; the quality of the solutions is driven by the quality of the data.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130