DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

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Testing Metrics Data Quality Dashboards 124

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 92

Data Quality Data Governance Data Lake Testing 92

Octopai

JULY 11, 2022

And when business users don’t complain, but you know the data isn’t good enough to make these types of calls wisely, that’s an even bigger problem. How are you, as a data quality evangelist (if you’re reading this post, that must describe you at least somewhat, right?), Tie data quality directly to business objectives.

Data Quality 79

Data Quality Strategy Business Objectives Sales 79

DataKitchen

FEBRUARY 23, 2024

Webinar: Beyond Data Observability: Personalization DataKitchen DataOps Observability Problem Statement White Paper: ‘Taming Chaos’ Technical Product Overview Four-minute online demo Detailed Product: Documentation Webinar: Data Observability Demo Day DataKitchen DataOps TestGen Problem Statement White Paper: ‘Mystery Box Full Of Data Errors’ (..)

Testing 117

Testing Data-driven Metrics Data Quality 117

DataKitchen

NOVEMBER 10, 2023

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

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. The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded.

Marketing 362

Marketing Experimentation Metrics Testing 362

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

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. SPC is the continuous testing of the results of automated manufacturing processes.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

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

JUNE 7, 2021

Hopefully, with metrics in place, you can show measured improvements in productivity and quality that will win converts. 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.

Testing 130

Testing IT Measurement Data-driven 130

DataKitchen

OCTOBER 2, 2023

Data Journeys track and monitor all levels of the data stack, from data to tools to code to tests across all critical dimensions. A Data Journey supplies real-time statuses and alerts on start times, processing durations, test results, and infrastructure events, among other metrics.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

CIO Business Intelligence

APRIL 25, 2024

Without automated evaluation, LinkedIn reports that “engineers are left eye-balling results and testing on a limited set of examples and having a more than a 1+ day delay to know metrics.” User experience When dealing with such a large member base, accuracy and relevancy metrics can “give a false sense of comfort,” Bottaro said.

IT 139

IT Experimentation Cost-Benefit Metrics 139

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 81

Metrics Machine Learning Statistics Risk 81

Smart Data Collective

AUGUST 11, 2022

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence.

Data Quality 129

Data Quality Machine Learning Digital Transformation Big Data 129

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

CIO Business Intelligence

MARCH 18, 2024

Our data team uses gen AI on Amazon cloud to explore sustainability metrics. Laying the foundation To develop POC implementations, Menon and her team are establishing a lab that is expected to debut in March 2024 for testing AI tools before rollout. AI tools rely on the data in use in these solutions.

Risk 133

Risk Cost-Benefit Data Processing Testing 133

Cloudera

MARCH 22, 2023

Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily. There are many logs and metrics, and they are all over the place.

Metrics 91

Metrics Data Governance Cost-Benefit Dashboards 91

AWS Big Data

NOVEMBER 9, 2023

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. Third-party APIs – These provide analytics and survey data related to ecommerce websites.

Data Warehouse 106

Data Warehouse Testing Data Quality Reporting 106

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 113

Strategy Management Key Performance Indicator ROI 113

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

CIO Business Intelligence

JUNE 1, 2022

Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. In the McLaren factory, the sensor data is streamed to digital twins of the engine and different car components or features like aerodynamics at 100,000 data points per second ?

Manufacturing 116

Manufacturing Digital Transformation Predictive Analytics Strategy 116

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 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

Ontotext

JUNE 22, 2023

The long wait comes from the need for extensive testing in order to ensure that a drug is safe and efficient before it can be available to those who need it. The confidence score and provenance metrics enable researchers to trace back any decisions and have more confidence to make evidence-driven decisions. Our customer is a U.S.-based

Metrics 52

Metrics Statistics Visualization Metadata 52

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 333

Management Machine Learning Experimentation Metrics 333

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. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

CIO Business Intelligence

SEPTEMBER 8, 2022

In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years. The ability to pivot quickly to address rapidly changing customer or market demands is driving the need for real-time data.

Cost-Benefit 120

Cost-Benefit Internet of Things Data-driven Data Lake 120

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.

Measurement 52

Measurement Visualization Advertising Metrics 52

Ontotext

MAY 2, 2024

But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy. Every new dataset and new user adds a little more friction that hits the core metric of the velocity of data and brings it down to zero. As a result, they could cut the time from 4 months down to about 2 weeks.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

CIO Business Intelligence

FEBRUARY 20, 2023

To earn the Salesforce Data Architect certification , candidates should be able to design and implement data solutions within the Salesforce ecosystem, such as data modelling, data integration and data governance. Prerequisites include earning Salesforce Application Architect certification (see above).

Consulting 87

Consulting B2B Cost-Benefit Sales 87

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.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

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Testing Machine Learning Consulting Data Quality 300

AWS Big Data

NOVEMBER 16, 2023

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge and adherence to battle-tested best practices, and using the right tools and features in the right scenario. system implemented with Amazon Redshift.

Enterprise 85

Enterprise Data Warehouse Snapshot Cost-Benefit 85

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

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Modeling Machine Learning Testing Metrics 193