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 115

Data Quality Measurement Testing Visualization 115

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. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

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

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

CIO Business Intelligence

FEBRUARY 6, 2025

For example, at a company providing manufacturing technology services, the priority was predicting sales opportunities, while at a company that designs and manufactures automatic test equipment (ATE), it was developing a platform for equipment production automation that relied heavily on forecasting. You get the picture.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

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 96

Data Quality Metrics Visualization Dashboards 96

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% Developers are learning how to find quality data and build models that work.

Enterprise 342

Enterprise Modeling Testing Reporting 342

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

DataKitchen

OCTOBER 11, 2021

The data engineer then emails the BI Team, who refreshes a Tableau dashboard. Figure 1: Example data pipeline with manual processes. There are no automated tests , so errors frequently pass through the pipeline. The pipeline has automated tests at each step, making sure that each step completes successfully.

Testing 152

Testing Metadata Dashboards Statistics 152

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk.

Machine Learning 52

Machine Learning Management Enterprise Risk Management 52

DataKitchen

MARCH 12, 2024

Reducing the errors your customers find and those they do not are key success metrics of Data Observability Using DataKitchen DataOps Observability and DataOps TestGen. We kept adding tests over time; it has been several years since we’ve had any major glitches. Director, Data Analytics Team “We had some data issues.

Metrics 117

Metrics Software Cost-Benefit Testing 117

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

DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. So, the only way for a data journey to truly observe what’s happening is to get his tools and pipelines to auto-report events. Data and tool tests.

Testing 130

Testing Statistics Measurement Metrics 130

Wayne Yaddow

MARCH 5, 2025

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage. PyTest, JUnit,NUnit).

Testing 52

Testing Data Transformation Statistics Metadata 52

DataKitchen

JUNE 7, 2021

Improve Collaboration, both Inter- and Intra -team – If the individuals in your data-analytics team don’t work together, it can impact analytics-cycle time, data quality, governance, security and more. A data arrival report enables you to track data suppliers and quickly spot delivery issues. Lower Error Rates.

Testing 130

Testing IT Data-driven Measurement 130

Ontotext

MAY 26, 2021

In the above case of merging information about companies from different data sources, data linking helps us encode the real-world business logic into data linking rules. But, before we can have any larger scale implementation of these rules, we have to test their validity. How does the Gold Standard help data linking?

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

Wayne Yaddow

FEBRUARY 11, 2025

Managing tests of complex data transformations when automated data testing tools lack important features? Photo by Marvin Meyer on Unsplash Introduction Data transformations are at the core of modern business intelligence, blending and converting disparate datasets into coherent, reliable outputs.

Testing 52

Testing Data Transformation Data Quality Statistics 52

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. Subsequently, the reporting should be set up properly.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

DataKitchen

JANUARY 19, 2022

Imagine a large enterprise yielding significant value from their Matillion-Snowflake integration, but wishing to expand the scope of data pipeline deployment, testing, and monitoring. DataKitchen triggers a Matillion job, then retrieves execution parameters that can be used in DataKitchen tests.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

Jen Stirrup

SEPTEMBER 30, 2021

Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data Understanding is a crucial aspect of all of these areas, and the process will not proceed properly without it.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

CIO Business Intelligence

AUGUST 9, 2022

But data engineers also need soft skills to communicate data trends to others in the organization and to help the business make use of the data it collects. Data engineers and data scientists often work closely together but serve very different functions.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

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

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

CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. The course includes instruction in statistics, machine learning, natural language processing, deep learning, Python, and R. On-site courses are available in Munich.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

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). Additional considerations – Factor in additional tasks beyond schema conversion.

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

Octopai

MAY 2, 2022

A successful data analytics team is one that can increase the quantity of data analytics products they develop in a given time while ensuring (and ideally, improving) the level of data quality. Through jidoka, quality problems are stopped in their tracks and prevented from reaching the consumer. . Enter DataOps.

Manufacturing 98

Manufacturing Testing Data Quality Dashboards 98

CIO Business Intelligence

JUNE 14, 2023

Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training. CDP Data Analyst The Cloudera Data Platform (CDP) Data Analyst certification verifies the Cloudera skills and knowledge required for data analysts using CDP.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

DataKitchen

JUNE 24, 2021

And once we cracked the code on that alternative reality and they saw that we weren’t just talking about running a test but continuous testing every step or instantiating a transit environment to recreate a test environment in seconds rather than days. Automate the data collection and cleansing process.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

Sisense

JANUARY 14, 2021

VP of Business Intelligence Michael Hartmann describes the problem: “When an upstream data model change was introduced, it took a few days for us to notice that one of our Sisense charts was ‘broken.’ With that in mind, the developers at Billie came up with the idea to automatically test Sisense charts.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

CIO Business Intelligence

JANUARY 9, 2024

ChatGPT caused quite a stir after it launched in late 2022, with people clamoring to put the new tech to the test. Can the current state of our data operations deliver the results we seek? Another tough topic that CIOs are having to surface to their colleagues: how problems with enterprise data quality stymie their AI ambitions.

IT 140

IT Risk Cost-Benefit Machine Learning 140

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. This separation means changes can be tested thoroughly before being deployed to live operations. It helps HEMA centralize all data assets across disparate data stacks into a single catalog.

Data Governance 52

Data Governance Publishing Data-driven 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

CIO Business Intelligence

AUGUST 9, 2024

Gartner agrees that synthetic data can help solve the data availability problem for AI products, as well as privacy, compliance, and anonymization challenges. An example is Alpha Fold, widely used in structural biology and bioinformatics,” he says.

Risk 138

Risk Measurement Data Lake Data Collection 138

IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy. How to get started with synthetic data in watsonx.ai

Metrics 80

Metrics Machine Learning Statistics Risk 80

Ontotext

FEBRUARY 14, 2024

As a statistical model, LLM inherently is random. Semantic knowledge graphs combined with LLM allow you to bridge the gap – querying your well-curated and conformed data with natural language. Data quality Knowledge graphs thrive on clean, well-structured data, and they rely on accurate relationships and meaningful connections.

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

Wayne Yaddow

FEBRUARY 26, 2025

The following are primary applications of artificial intelligence among data transformation and conversion verification processes. AI-Driven Automated Data Transformation TestCases Traditional data transformation testing often relies on manually created test cases, which can be time-consuming and prone to human oversight.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

CIO Business Intelligence

JUNE 14, 2022

Better data to power decision making. The mission also sets forward a target of 50% of high-priority data quality issues to be resolved within a period defined by a cross-government framework. Secure, efficient, and sustainable technology. The same bodies are also failing to attract top digital talent.

Strategy 105

Strategy IT Digital Transformation Data Processing 105