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 Data Lake Sales 101

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

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

AWS Big Data

OCTOBER 9, 2024

Some customers build custom in-house data parity frameworks to validate data during migration. Others use open source data quality products for data parity use cases. This takes away important person hours from the actual migration effort into building and maintaining a data parity framework.

Data Quality 96

Data Quality Data Lake Data Warehouse Metrics 96

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

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

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

DataKitchen

MARCH 16, 2023

So it’s Monday, and you lead a data analytics team of perhaps 30 people. But wait, she asks you for your team metrics. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. Where is your metrics report? What should be in that report about your data team?

Metrics 130

Metrics Data Analytics Analytics Measurement 130

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 104

Data Quality Data Governance Data Lake Testing 104

O'Reilly on Data

MARCH 31, 2020

There may even be someone on your team who built a personalized video recommender before and can help scope and estimate the project requirements using that past experience as a point of reference. Without large amounts of good raw and labeled training data, solving most AI problems is not possible.

Management 363

Management Machine Learning Experimentation Metrics 363

DataKitchen

SEPTEMBER 21, 2023

.’ It’s not just about playing detective to discover where things went wrong; it’s about proactively monitoring your entire data journey to ensure everything goes right with your data. What is Data in Place? There are multiple locations where problems can happen in a data and analytic system.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 83

Data Quality Data-driven Data Lake Metrics 83

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

Data Quality 76

Data Quality Data Lake Visualization Data-driven 76

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. It’s not about data quality .

Testing 214

Testing Manufacturing Data Quality Statistics 214

Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

Data Quality 52

Data Quality IT Data Governance Sales 52

AWS Big Data

FEBRUARY 13, 2025

This plane drives users to engage in data-driven conversations with knowledge and insights shared across the organization. Through the product experience plane, data product owners can use automated workflows to capture data lineage and data quality metrics and oversee access controls.

Data Analytics 72

Data Analytics Analytics Modeling Management 72

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. Adding Tests to Reduce Stress.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

FEBRUARY 29, 2024

Implement data privacy policies. Implement data quality by data type and source. Let’s look at some of the key changes in the data pipelines namely, data cataloging, data quality, and vector embedding security in more detail. Link structured and unstructured datasets.

Data Governance 110

Data Governance Unstructured Data Metadata Data Lake 110

AWS Big Data

NOVEMBER 6, 2023

Refer to the Configuration reference in the User Guide for detailed configuration values. The following graph describes a simple data quality check pipeline using setup and teardown tasks. To learn more about Setup and Teardown tasks, refer to the Apache Airflow documentation. Set up a new Apache Airflow v2.7.2

Metrics 117

Metrics Metadata Snapshot Management 117

datapine

JANUARY 31, 2022

BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

datapine

DECEMBER 4, 2019

We can safely say that chatbots will have the power to restructure business processes, enable easier communication between humans and data while ensuring that chatbot technologies such as natural language processing will bring added value to companies. This data analytics buzzword is somehow a déjà-vu. Augmented Analytics.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

AWS Big Data

SEPTEMBER 11, 2024

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. The data quality (DQ) checks are managed using DQ configurations stored in Aurora PostgreSQL tables.

Data Architecture 97

Data Architecture Optimization Data Warehouse Metadata 97

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. Review the details and choose Create and launch integration.

Data Integration 98

Data Integration Data Lake Statistics Data-driven 98

Occam's Razor

DECEMBER 21, 2009

Web analytics gems lie deep in the data and we spend our lives looking at the top ten rows of data. Referring URL's. But remember I have twenty six thousand keywords referring traffic to this blog. Metrics and Conversion and Data and Questions (look at that!) It does not matter which report you look at.

Metrics 94

Metrics KPI Reporting Visualization 94

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 79

Analytics Data Warehouse Data Lake Metadata 79

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI).

Data Warehouse 100

Data Warehouse KPI Optimization Cost-Benefit 100

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders. Basic formatting and readability of the data is standardized here.

Optimization 96

Optimization B2B Data Quality Sales 96

Cloudera

AUGUST 26, 2020

Pepperdata software automatically scales system resources in the Cloudera platform while providing a correlated view of the infrastructure and applications using hundreds of real-time system metrics. Unravel Data is an intelligence platform that helps you simplify, optimize and control your Big Data activities.

Machine Learning 84

Machine Learning Big Data Data Warehouse Data-driven 84

datapine

MARCH 27, 2019

Centered on leveraging consumer insights to improve your strategies and communications by using a highly data-driven process can also be referred to as Customer Intelligence (CI). Such inconsistencies can have a huge effect on the way data is organized through a host of different management systems within a company.

Management 129

Management Data-driven Dashboards Visualization 129

Occam's Razor

JULY 24, 2008

Some folks refer to them as "session" and "user" cookies respectively. If you use cookies those numbers will be better (not perfect, see this post: Data Quality Sucks, Let’s Just Get Over It ). Top Visited Pages, Revenue, Referring Websites (URL's), Search Engine Keywords and on and on and on.

Analytics 111

Analytics Key Performance Indicator Measurement Metrics 111

datapine

FEBRUARY 22, 2022

A business intelligence strategy refers to the process of implementing a BI system in your company. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7 It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

DataRobot Blog

MAY 10, 2022

The first step would be to make sure that the data used at the beginning of the model development process is thoroughly vetted, so that it is appropriate for the use case at hand. To reference SR 11-7: . This requirement makes sure that no faulty data variables are being used to design a model, so erroneous results are not outputted.

Risk 64

Risk Modeling Machine Learning Data Quality 64

Jet Global

JANUARY 3, 2020

Valuable as data may be, having an incomplete understanding of something is almost as bad as knowing nothing at all. For example, companies need to track their performance metrics closely. It also minimizes the risk of human errors compromising data quality. Relying on an SSOT for reference does the opposite.

Business Intelligence 69

Business Intelligence Forecasting Data-driven Metrics 69

Andrew White

JULY 5, 2023

One of the points of confusion is with catalogs – or data catalogs – or analytics catalogs or metrics stores. Here I repeat what I wrote in the original blog: Use cases for a data catalog Analytics use cases are quite different to governance use cases. The fact that there are different names is one thing.

Metrics 52

Metrics Metadata Modeling Data Quality 52

AWS Big Data

AUGUST 14, 2023

Goals of DPPM The goals of DPPM can be summarized as follows: Protect value – DPPM protects the value of the organizational data strategy by developing, implementing, and enforcing frameworks to measure the contribution of data products to organizational goals in objective terms. Monitoring and Event Management X X.

Management 87

Management Risk Measurement Optimization 87

datapine

FEBRUARY 17, 2023

That said, data and analytics are only valuable if you know how to use them to your advantage. Poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. In fact, poor data quality management currently costs businesses a combined total of $9.7 million per year.

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

AWS Big Data

MARCH 26, 2024

In 2022, AWS commissioned a study conducted by the American Productivity and Quality Center (APQC) to quantify the Business Value of Customer 360. The following figure shows some of the metrics derived from the study. This consolidated view acts as a liaison between the data platform and customer-centric applications.

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78