DataKitchen

MARCH 5, 2024

Stakeholders, from internal teams to external clients, seek confidence in the data they use and depend on. This confidence can only be built through transparency, proactive error detection, and a robust data quality and observability framework. It’s just a matter of time.

Data Quality 146

Data Quality Measurement Testing Data-driven 146

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. Tests assess important questions, such as “Is the data correct?”

Metrics 117

Metrics Software Cost-Benefit Testing 117

DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure.

Data Quality 117

Data Quality Unstructured Data Testing Modeling 117

Ontotext

MAY 26, 2021

Originally, the Gold Standard was a monetary system that required countries to fix the value of their currencies to a certain amount of gold, aiming to replace the unreliable human control with a fixed measurement that could be used by everyone. Simply put, we need to be able to measure and evaluate our results against clearly set criteria.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

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

DataKitchen

MARCH 16, 2023

How to measure your data analytics team? So it’s Monday, and you lead a data analytics team of perhaps 30 people. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. The Active Data Ratio metric determines the percentage of datasets that deliver value.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

DataKitchen

JUNE 7, 2021

Hopefully, with metrics in place, you can show measured improvements in productivity and quality that will win converts. DataOps includes four key objectives: Measure Your Process – As data professionals, we advocate for the benefits of data-driven decision making. Measure Your Process. DataOps Objectives.

Testing 130

Testing IT Measurement Data-driven 130

Rocket-Powered Data Science

FEBRUARY 15, 2023

Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired! Test and refine the chatbot. Suggestion: take a look at MACH architecture.)

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. Monitoring is another pillar of Data Journeys, extending down the stack. The image above shows an example ‘’data at rest’ test result. For example, a test can check the top fifty customers or suppliers.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). The Core Responsibilities of the AI Product Manager.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

MAY 8, 2023

This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI. Finally, establish policy enforcement measure to set the norms, roles and accountabilities, approval processes, and maintenance guidelines across AI development lifecycles.

Insurance 72

Insurance Risk Testing Data Quality 72

CIO Business Intelligence

JUNE 9, 2023

It’s a time-tested truth: Getting a head start improves outcomes. A few years ago, Gartner found that “organizations estimate the average cost of poor data quality at $12.8 million per year.’” Beyond lost revenue, data quality issues can also result in wasted resources and a damaged reputation.

Strategy 116

Strategy Management Key Performance Indicator ROI 116

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. 3) Where will your data come from?

IT 317

IT Statistics KPI Data-driven 317

CIO Business Intelligence

NOVEMBER 13, 2023

Transformational leaders must ensure their organizations have the expertise to integrate new technologies effectively and the follow-through to test and troubleshoot thoroughly before going live. Ensuring data quality, privacy, and security is essential.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

Alation

FEBRUARY 3, 2022

They make testing and learning a part of that process. Using this methodology, teams will test new processes, monitor performance, and adjust based on results. In order for data governance to continuously improve , practitioners need to think of data governance as a cyclical process. Monitor and Measure Curation.

Data Governance 98

Data Governance Measurement Metadata Testing 98

Anmut

APRIL 6, 2023

What are the Benefits of Doing a Data Maturity Assessment? How do you do a Data Maturity Assessment? What does a Data Maturity Assessment Measure? What makes a Good Data Maturity Assessment? What is a Data Maturity Assessment? What does a Data Maturity Assessment Measure?

Cost-Benefit 52

Cost-Benefit Data Quality Data Governance Measurement 52

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.

Metrics 87

Metrics Machine Learning Risk Statistics 87

CIO Business Intelligence

JUNE 14, 2023

Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications. The number of data analytics certs is expanding rapidly.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Ontotext

FEBRUARY 14, 2024

How do you measure its utility? If you ask it to generate a response, and maybe it hallucinates, you can then constrain the response it gives you, from the well-curated data in your graph. 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

CIO Business Intelligence

APRIL 19, 2024

In doing so, they could build something, test it out, and then go through various cycles of changes and improvements so the final product was production ready when it was launched. From the data, we see these messages have made a tremendous impact,” he says. “So So much so that we’re now extending the project to other territories.”

Data Processing 52

Data Processing Data Quality Measurement Testing 52

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

DataKitchen

NOVEMBER 18, 2022

Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement.

Data Quality 130

Data Quality Testing Measurement Data Integration 130

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 191

Machine Learning Modeling Testing Risk Management 191

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against. Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

CIO Business Intelligence

MARCH 7, 2024

Public LLMs in the sandbox: Safely test publicly available Large Language Models (LLMs) in a sandbox environment, separate from the production setting, to assess their impact without risking operational disruptions. Many businesses are in the exploratory phases, creating unique business cases and testing their theory on uses.

Digital Transformation 72

Digital Transformation Machine Learning Risk Testing 72

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

erwin

APRIL 15, 2020

If we reflect on the coronavirus example, we can ask: How do individual countries report their data? Do individual countries use the same testing protocols? Are infections universally defined the same way (based on widely administered tests or only hospital admissions)? Are asymptomatic infections reported?

Data Governance 134

Data Governance Metadata Testing Reporting 134

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. Be data driven?" " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love!

KPI 124

KPI Metrics Measurement ROI 124

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataRobot

APRIL 20, 2021

In this installment, I’ll cover four key elements of trusted AI that relate to the performance of a model: data quality, accuracy, robustness and stability, and speed. Quality Input Means Quality Output. Trustworthy AI is built on a foundation of high-quality data. Use Multiple Tools and Visualizations.

Measurement 52

Measurement Visualization Advertising Metrics 52

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

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. Data management teams face a choice: how long do we sit on the fence — and when do we dive in, and test the waters?

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. How do we think about the quality of human ratings, and how do we quantify our understanding is the subject of this post. While human-labeled data is critical to many important applications, it also brings many challenges.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

DataKitchen

APRIL 18, 2024

The most effortless way to institute comprehensive, agile data quality testing is to derive actionable information, start testing and measuring immediately, and then iterate, using tests and results to refine. DataOps TestGen is the silent warrior that ensures the integrity of your data.

Testing 117

Testing Data Quality Enterprise Data Analytics 117

O'Reilly on Data

MARCH 31, 2020

The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. This has serious implications for software testing, versioning, deployment, and other core development processes.

Management 332

Management Machine Learning Experimentation Metrics 332

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40