Rocket-Powered Data Science

FEBRUARY 15, 2023

Know thy data: understand what it is (formats, types, sampling, who, what, when, where, why), encourage the use of data across the enterprise, and enrich your datasets with searchable (semantic and content-based) metadata (labels, annotations, tags). Test early and often. Test and refine the chatbot.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

DECEMBER 19, 2018

have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.

Machine Learning 279

Machine Learning Software Metadata Testing 279

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

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 95

Data Quality Data Lake Data Warehouse Metrics 95

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

TDAN

MARCH 16, 2021

The purpose of this article is to provide a model to conduct a self-assessment of your organization’s data environment when preparing to build your Data Governance program. Take the […].

Data Governance 105

Data Governance Testing Modeling Data Quality 105

DataKitchen

AUGUST 16, 2024

Data Pipeline Observability: Optimizes pipelines by monitoring data quality, detecting issues, tracing data lineage, and identifying anomalies using live and historical metadata. This capability includes monitoring, logging, and business-rule detection.

Marketing 124

Marketing Data Quality Testing Metadata 124

erwin

DECEMBER 9, 2024

If you are not observing and reacting to the data, the model will accept every variant and it may end up one of the more than 50% of models, according to Gartner , that never make it to production because there are no clear insights and the results have nothing to do with the original intent of the model.

Metadata 52

Metadata Data Quality Sales Modeling 52

DataKitchen

AUGUST 3, 2021

If you’ve been following DataKitchen at all, you know we are all about transferring software development methods to data analytics. . The organizational concepts behind data mesh are summarized as follows. A five to nine-person team owns the dev, test, deployment, monitoring and maintenance of a domain.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

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. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Alation

DECEMBER 7, 2021

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Do we have end-to-end data pipeline control?

Data Quality 52

Data Quality Metadata Data Governance Testing 52

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

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

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

IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. Scalability and elasticity.

Metadata 56

Metadata Machine Learning Data Quality Statistics 56

Wayne Yaddow

FEBRUARY 18, 2025

Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis. However, these two processes are essentially distinct, and their testing needs differ in manyways.

Testing 52

Testing Data Transformation Data-driven Data Quality 52

AWS Big Data

DECEMBER 13, 2023

Manually upgrading, testing, and deploying over 5,000 jobs every few quarters was time consuming, error prone, costly, and not sustainable. EDLS job steps and metadata Every EDLS job comprises one or more job steps chained together and run in a predefined order orchestrated by the custom ETL framework.

Metadata 100

Metadata Data Lake Visualization Data Quality 100

IBM Big Data Hub

MARCH 25, 2022

Automated data enrichment : To create the knowledge catalog, you need automated data stewardship services. These services include the ability to auto-discover and classify data, to detect sensitive information, to analyze data quality, to link business terms to technical metadata and to publish data to the knowledge catalog.

Metadata 104

Metadata Data Quality Data Governance Data Integration 104

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Ontotext

NOVEMBER 9, 2023

While everyone may subscribe to the same design decisions and agree on an ontology, there may be differences in the data quality. In such situations, data must be validated. Let’s build a very simple test dataset. For our test set, we can simply use sh:path. Instead, they provide metadata about the shapes.

Data Quality 64

Data Quality Testing Reporting Metadata 64

Alation

OCTOBER 18, 2022

In the modern data stack, dbt is a key tool to make data ready for analysis. Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Conclusion.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 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 363

Management Machine Learning Experimentation Metrics 363

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

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 86

Metadata Data Lake Data Processing Data-driven 86

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections.

Data Architecture 96

Data Architecture Optimization Data Warehouse Metadata 96

CIO Business Intelligence

FEBRUARY 2, 2024

The Art of Service recommends candidates spend a minimum of 18 hours on the course to pass the certification test. It consists of three separate, 90-minute exams: the Information Systems (IS) Core exam, the Data Management Core exam, and the Specialty exam. How to prepare: The fee includes an online training program and PDF textbook.

Management 116

Management Data Governance Cost-Benefit Testing 116

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback.

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

DataKitchen

JUNE 24, 2021

Bergh added, “ DataOps is part of the data fabric. You should use DataOps principles to build and iterate and continuously improve your Data Fabric. Automate the data collection and cleansing process. Education is the Biggest Challenge. Take a show-me approach.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

CIO Business Intelligence

JUNE 12, 2024

“Most enterprise data is unstructured and semi-structured documents and code, as well as images and video. For example, gen AI can be used to extract metadata from documents, create indexes of information and knowledge graphs, and to query, summarize, and analyze this data. But sometimes there isn’t enough data,” says Thurai.

Modeling 140

Modeling Testing Cost-Benefit Metadata 140

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. However, because most institutions lack a modern data architecture , they struggle to manage, integrate and analyze financial data at pace.

Data Architecture 85

Data Architecture Risk Management Risk Management 85

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 an approach based on continuous improvement, organizations must identify key assets so their metadata can be ingested and analyzed. Monitor and Measure Curation.

Data Governance 98

Data Governance Measurement Metadata Testing 98

Ontotext

SEPTEMBER 15, 2020

The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems. Knowledge Graphs are the Warp and Weft of a Data Fabric. To implement any Data Fabric approach, it is essential to be able to understand the context of data.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

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. The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” Yet finding data is just the beginning.

Metadata 64

Metadata Data Governance Dashboards Software 64

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

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52