Dataversity

FEBRUARY 4, 2022

But decisions made without proper data foundations, such as well-constructed and updated data models, can lead to potentially disastrous results. For example, the Imperial College London epidemiology data model was used by the U.K. Government in 2020 […].

Data Modeling 52

Data Modeling Data Models Data Quality Data Governance 52

Data Science Blog

AUGUST 22, 2024

This shift not only saves time but also ensures a higher standard of data quality. Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed. Foster a Data-Driven Culture Promote a culture where data quality is a shared responsibility.

Data Preparation 130

Data Preparation Data Quality AI AI 130

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Smart Data Collective

JULY 9, 2023

By harmonising and standardising data through ETL, businesses can eliminate inconsistencies and achieve a single version of truth for analysis. Improved Data Quality Data quality is paramount when it comes to making accurate business decisions.

ETL 105

ETL Data Quality Data Warehouse Analytics 105

AWS Machine Learning Blog

FEBRUARY 27, 2024

Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary. Workflow B corresponds to model quality drift checks.

Machine Learning 140

Machine Learning Machine Learning ML ML 140

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

ODSC - Open Data Science

APRIL 28, 2023

These are critical steps in ensuring businesses can access the data they need for fast and confident decision-making. As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation. How does this all tie into AI/ML?

ML 98

ML ML Data Silos Data Quality 98

The Data Administration Newsletter

OCTOBER 5, 2021

Doug has spoken many times at our Data Modeling Zone conferences over the years, and when I read the book, I can hear him talk in his distinct descriptive and conversational style. The Enrichment Game describes how to improve data quality and data useability […].

Data Quality 52

Data Quality Data Modeling Data Models 52

Tableau

APRIL 18, 2022

We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage. Data modeling.

Tableau 102

Tableau Data Quality Analytics Analytics 102

Tableau

APRIL 18, 2022

We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage. Data modeling.

Tableau 98

Tableau Data Quality Analytics Analytics 98

The MLOps Blog

JUNE 27, 2023

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? Your data team can manage large-scale, structured, and unstructured data with high performance and durability.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

NOVEMBER 4, 2021

The practitioner asked me to add something to a presentation for his organization: the value of data governance for things other than data compliance and data security. Now to be honest, I immediately jumped onto data quality. Data quality is a very typical use case for data governance.

Data Governance 94

Data Governance Data Quality ML ML 94

The Data Administration Newsletter

DECEMBER 20, 2022

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

Data Governance 52

Data Governance Data Quality Data Modeling Data Models 52

Precisely

OCTOBER 23, 2023

An ERP does not do data quality very well. CRM’s, likewise, does a poor job of undating data according to consistent standards. Very often, key business users conflate MDM with various tasks or components of data science and data management. Others regard it as a data modeling platform.

Data Quality 109

Data Quality Data Warehouse Analytics Analytics 109

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

MARCH 16, 2023

Allowing you to keep your data models clean and up to date while you work on any fixes needed for the malformed XML/JSON. It is often better to prevent the bad XML/JSON from processing through your pipeline.

Data Quality 105

Data Quality Data Modeling Data Models 105

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

phData

MARCH 22, 2023

Sigma Computing’s Metrics are a powerful tool for simplifying this complexity and making it easier for business users to access and understand data. In this blog, we will explore what Metrics are, how they work, and why they should be used in data modeling. What Are Metrics From Sigma?

Data Modeling 52

Data Modeling Data Models Data Quality Database 52

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance 102

Data Governance Analytics Analytics Tableau 102

IBM Journey to AI blog

NOVEMBER 29, 2023

It automatically identifies vulnerable individual data points and introduces “noise” to obscure their specific information. Although adding noise slightly reduces output accuracy (this is the “cost” of differential privacy), it does not compromise utility or data quality compared to traditional data masking techniques.

Data Scientist 100

Data Scientist Machine Learning Machine Learning AI 100

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance 97

Data Governance Analytics Analytics Tableau 97

phData

AUGUST 10, 2023

Utilize dbt’s incremental materialization to process new feeds from Snowflake streams or implement any intermediary Ephemeral models in dbt to achieve the same. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Implementing a data catalog first will make MDM more successful.

Data Quality 52

Data Quality Data Warehouse Data Profiling Data Governance 52

Heartbeat

JUNE 12, 2023

Data Velocity: High-velocity data streams can quickly overwhelm monitoring systems, leading to latency and performance issues. Data Quality: The accuracy and completeness of data can impact the quality of model predictions, making it crucial to ensure that the monitoring system is processing clean, accurate data.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Alation

MAY 31, 2023

They collaborate with IT professionals, business stakeholders, and data analysts to design effective data infrastructure aligned with the organization’s goals. Their broad range of responsibilities include: Design and implement data architecture. Maintain data models and documentation.

Data Governance 52

Data Governance Data Quality Data Silos Data Modeling 52

Precisely

MARCH 27, 2023

Transforming Go-to-Market After years of acquiring and integrating smaller companies, a $37 billion multinational manufacturer of confectionery, pet food, and other food products was struggling with complex and largely disparate processes, systems, and data models that needed to be normalized. million in annual recurring savings.

Data Quality 52

Data Quality Data Lakes Data Governance Data Modeling 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Precisely

MARCH 7, 2024

According to a 2023 study from the LeBow College of Business , data enrichment and location intelligence figured prominently among executives’ top 5 priorities for data integrity. 53% of respondents cited missing information as a critical challenge impacting data quality. What is data integrity?

Data Quality 52

Data Quality Database Administration Analytics Analytics 52

Alation

AUGUST 16, 2022

But do they empower many user types to quickly find trusted data for a business decision or data model? Many data catalogs suffer from a lack of adoption because they are too technical. And with our Open Connector Framework , customers and partners can easily build connectors to even more data sources.

Data Quality 52

Data Quality Data Governance Cloud Data Data Engineering 52

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

Pickl AI

MARCH 3, 2025

Ensuring data accuracy and consistency through cleansing and validation processes. Data Analysis and Modelling Applying statistical techniques and analytical tools to identify trends, patterns, and anomalies. Developing data models to support analysis and reporting. Identifying and resolving data quality issues.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Pickl AI

JUNE 28, 2023

Data Quality: It provides mechanisms to cleanse and transform data. Thereby, improving data quality and consistency. Scalability : ETL processes can handle large volumes of data and complex integration scenarios. Data integration is a vital component of successful data mining initiatives.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

Precisely

OCTOBER 16, 2023

That means that for data to be trustworthy and ready to power the enterprise it should be accurate, timely, and contextually relevant. Consistency, accuracy, and completeness are aspects of data integrity, of course, but true data integrity extends much further than just data quality.

Analytics 71

Analytics Analytics Big Data Analytics Big Data Analytics 71

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines.

AWS 127

AWS Machine Learning Machine Learning ML 127

Dataconomy

AUGUST 7, 2023

These formats play a significant role in how data is processed, analyzed, and used to develop AI models. Structured data is organized in a highly organized and predefined manner. It follows a clear data model, where each data entry has specific fields and attributes with well-defined data types.

Database 168

Database AI AI ML 168

Alation

FEBRUARY 17, 2023

Some data seems more analytical, while other is operational (external facing). We recommend identifying the data sources and tables that need to be considered to be governed, establishing the governance owner & data quality details, and saving those details in the catalog. This is a very good thing. Here’s an example.

Data Governance 52

Data Governance Data Quality Data Analyst Data Pipeline 52