O'Reilly on Data

NOVEMBER 19, 2024

A Latent Space Theory for Emergent Abilities in Large Language Models ” by Hui Jiang presents a statistical explanation for emergent LLM abilities, exploring a relationship between ambiguity in a language versus the scale of models and their training data. “ Do LLMs Really Adapt to Domains?

Unstructured Data 318

Unstructured Data Structured Data Modeling Statistics 318

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

O'Reilly on Data

MARCH 24, 2020

” One of his more egregious errors was to continually test already collected data for new hypotheses until one stuck, after his initial hypothesis failed [4]. You may picture data scientists building machine learning models all day, but the common trope that they spend 80% of their time on data preparation is closer to the truth.

Machine Learning 364

Machine Learning Statistics Data Quality Modeling 364

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

Data Quality 97

Data Quality Statistics Data Lake Visualization 97

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

AWS Big Data

APRIL 3, 2024

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

Data Quality 104

Data Quality Visualization Metadata Metrics 104

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 104

Data Quality Measurement Testing Visualization 104

AWS Big Data

MAY 23, 2024

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules assess the data based on fixed criteria reflecting current business states. We are excited to talk about how to use dynamic rules , a new capability of AWS Glue Data Quality.

Data Quality 101

Data Quality Metrics Sales Data Lake 101

AWS Big Data

JUNE 6, 2023

Alerts and notifications play a crucial role in maintaining data quality because they facilitate prompt and efficient responses to any data quality issues that may arise within a dataset. This proactive approach helps mitigate the risk of making decisions based on inaccurate information.

Data Quality 82

Data Quality Metrics Data-driven Visualization 82

AWS Big Data

JUNE 6, 2023

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

Data Quality 79

Data Quality Metrics Visualization Dashboards 79

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.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

Smarten

NOVEMBER 26, 2024

If the data is not easily gathered, managed and analyzed, it can overwhelm and complicate decision-makers. Data insight techniques provide a comprehensive set of tools, data analysis and quality assurance features to allow users to identify errors, enhance data quality, and boost productivity.’

Data Quality 64

Data Quality Machine Learning Predictive Modeling Metadata 64

CIO Business Intelligence

FEBRUARY 6, 2025

In life sciences, simple statistical software can analyze patient data. While this process is complex and data-intensive, it relies on structured data and established statistical methods. It also means establishing clear data governance frameworks to ensure data quality, security and ethical use.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

DataKitchen

MAY 10, 2021

An automated process that catches errors early in the process gives the data team the maximum available time to resolve the problem – patch the data, contact data suppliers, and rerun processing steps. We liken this methodology to the statistical process controls advocated by management guru Dr. Edward Deming.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

Data Quality 64

Data Quality Analytics Machine Learning Visualization 64

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

DataKitchen

APRIL 11, 2024

The goal is to reduce errors and operational overhead, allowing data teams to focus on delivering value. Statistical Process Control in Data Operations: Gil touched upon applying statistical process control techniques to data operations to monitor and control data quality and process performance.

Statistics 152

Statistics Manufacturing Data Processing Data Quality 152

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. The status and statistics of the CDC load are published into CloudWatch.

Data Integration 98

Data Integration Data Lake Statistics Data-driven 98

DataKitchen

DECEMBER 29, 2022

Data integrity checks can verify that the data is consistent with other sources or systems and that it has not been tampered with or corrupted in any way. Data quality checks: These checks ensure that the data being processed or stored in a production system is high quality and meets the required standards.

Testing 130

Testing Statistics Data Quality Data Integration 130

datapine

APRIL 16, 2019

As a direct result, less IT support is required to produce reports, trends, visualizations, and insights that facilitate the data decision making process. From these developments, data science was born (or at least, it evolved in a huge way) – a discipline where hacking skills and statistics meet niche expertise.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

CIO Business Intelligence

JANUARY 1, 2025

Referring to the latest figures from the National Institute of Statistics, Abril highlights thatin the last five years, technological investment within the sector has grown more than 40%. This reflects the growing dependence on digital solutions to maintain competitiveness, he says.

Strategy 115

Strategy Digital Transformation Optimization Technology 115

Ontotext

MAY 26, 2021

If these sample sets are not of high quality, clean and representative, we cannot hope to train the algorithms to get useful results. Beyond training, each and every AI system, be it based on symbolic rules or statistical models, has to be evaluated, in the same way children take exams to graduate.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

O'Reilly on Data

JULY 15, 2019

We should clarify that SR 11-7 also covers models that aren’t necessarily based on machine learning: "quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates." Sources of model risk.

Machine Learning 51

Machine Learning Management Enterprise Risk Management 51

O'Reilly on Data

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% AI users are definitely facing these problems: 7% report that data quality has hindered further adoption, and 4% cite the difficulty of training a model on their data.

Enterprise 342

Enterprise Testing Modeling Reporting 342

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

Anmut

JULY 12, 2021

Facts, events, statements, and statistics without proper context have little value and only lead to questions and confusion.?This This is true for life in general, but it’s especially applicable to the data you use to power your business. Data quality vs data condition: basic definitions & differences.

Data Quality 52

Data Quality Measurement Metrics Dashboards 52

DataKitchen

MARCH 12, 2024

Director, Data Analytics Team “We had some data issues. I used to be very, very careful when changing anything data-related, but I think you’re starting to see that it’s not that big a deal anymore because [if something goes wrong] we can now figure out what happened pretty quickly, and we can adjust it.“

Metrics 117

Metrics Software Cost-Benefit Testing 117

CIO Business Intelligence

FEBRUARY 20, 2025

By collecting and evaluating large amounts of data, HR managers can make better personnel decisions faster that are not (only) based on intuition and experience. However, it is often unclear where the data needed for reporting is stored and what quality it is in.

Big Data 84

Big Data Measurement Visualization Reporting 84

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

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

erwin

APRIL 29, 2020

For that reason, businesses must think about the flow of data across multiple systems that fuel organizational decision-making. The CEO also makes decisions based on performance and growth statistics. Seeing data pipelines and information flows further supports compliance efforts. Data Quality.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

Data Virtualization

NOVEMBER 28, 2024

One surprising statistic from the Rand Corporation is that 80% of artificial intelligence (AI). appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Reading Time: 8 minutes It’s an interesting question. The post How Do You Know When You’re Ready for AI?

Statistics 52

Statistics Data Integration Management Data Quality 52

CIO Business Intelligence

SEPTEMBER 30, 2024

Like others, Bell’s data scientists face challenges such as data cleanliness and interoperability, and Mathematica will at times partner with other organizations to overcome those challenges.

Data Quality 116

Data Quality Experimentation Insurance Statistics 116

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