AWS Big Data

MAY 23, 2024

Hundreds of thousands of organizations build data integration pipelines to extract and transform data. They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. We also show how to take action based on the data quality results.

Data Quality 101

Data Quality Metrics Data Lake Sales 101

Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

DataKitchen

DECEMBER 29, 2022

Data contracts are a new idea for data and analytic team development to ensure that data is transmitted accurately and consistently between different systems or teams. One of the primary benefits of using data contracts is that they help to ensure data integrity and compatibility.

Testing 130

Testing Statistics Data Quality Data Integration 130

DataKitchen

MAY 10, 2021

An automated process that catches errors early in the process gives the data team the maximum available time to resolve the problem – patch the data, contact data suppliers, and rerun processing steps. We liken this methodology to the statistical process controls advocated by management guru Dr. Edward Deming.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

Data Quality 64

Data Quality Analytics Machine Learning Visualization 64

Data Virtualization

NOVEMBER 28, 2024

One surprising statistic from the Rand Corporation is that 80% of artificial intelligence (AI). appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. The post How Do You Know When You’re Ready for AI?

Statistics 52

Statistics Data Integration Management Data Quality 52

IBM Big Data Hub

NOVEMBER 4, 2022

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. and its leading data observability offerings.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

DataKitchen

JANUARY 19, 2022

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. DataOps recommends that tests monitor data continuously in addition to checks performed when pipelines are run on demand.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. The CEO also makes decisions based on performance and growth statistics. Data Quality.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

O'Reilly on Data

DECEMBER 12, 2019

Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible. Small residuals usually mean a model is right, and large residuals usually mean a model is wrong.

Machine Learning 278

Machine Learning Modeling Testing Risk Management 278

IBM Big Data Hub

JANUARY 9, 2024

High variance in a model may indicate the model works with training data but be inadequate for real-world industry use cases. Limited data scope and non-representative answers: When data sources are restrictive, homogeneous or contain mistaken duplicates, statistical errors like sampling bias can skew all results.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Ontotext

FEBRUARY 14, 2024

Data integration If your organization’s idea of data integration is printing out multiple reports and manually cross-referencing them, you might not be ready for a knowledge graph. As a statistical model, LLM inherently is random. So, we’ve learned our lesson. How do you do that?

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

Optimization 95

Optimization B2B Data Quality Sales 95

AWS Big Data

JULY 29, 2024

This ensures that each change is tracked and reversible, enhancing data governance and auditability. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks.

Metadata 118

Metadata Snapshot Data Lake Metrics 118

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 69

Analytics Visualization Data Quality Metadata 69

CIO Business Intelligence

JANUARY 9, 2024

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. 1 among the top three risks — followed by statistical validity and model accuracy.

IT 140

IT Risk Cost-Benefit Machine Learning 140

Smart Data Collective

SEPTEMBER 25, 2019

Whether you work remotely all the time or just occasionally, data encryption helps you stop information from falling into the wrong hands. It Supports Data Integrity. Something else to keep in mind about encryption technology for data protection is that it helps increase the integrity of the information alone.

Technology 102

Technology Statistics Strategy Insurance 102

Wayne Yaddow

MARCH 5, 2025

We also go into exploratory data analysis and statistical methodologies for discovering problems that simpler checks overlook and end-to-end or regression testing with production-like data to ensure real-world reliability. Key Tools & Processes Data profiling tools (e.g., Statistical tests (e.g.,

Testing 52

Testing Data Transformation Statistics Metadata 52

IBM Big Data Hub

JULY 6, 2022

Businesses of all sizes, in all industries are facing a data quality problem. 73% of business executives are unhappy with data quality and 61% of organizations are unable to harness data to create a sustained competitive advantage 1. The data observability difference .

Metadata 56

Metadata Data Quality Snapshot Cost-Benefit 56

Wayne Yaddow

FEBRUARY 11, 2025

While many tools exist for basic data validationssuch as null checks, referential integrity, and common schema compliancemany advanced or domain-specific transformation scenarios remain insufficiently served by commercial and open-source testing solutions. Real-time data qualitychecks 2.

Testing 52

Testing Data Transformation Data Quality Statistics 52

Sisense

JANUARY 14, 2021

The Billie BI team has decided to share the code for their testing project to help other data teams using Sisense for Cloud Data Teams. “We We believe this can help teams be more proactive and increase the data quality in their companies,” said Ivan. He works on reporting, analysis, and data modeling.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

Alation

JUNE 2, 2022

All Machine Learning uses “algorithms,” many of which are no different from those used by statisticians and data scientists. The difference between traditional statistical, probabilistic, and stochastic modeling and ML is mainly in computation. Recently, Judea Pearl said, “All ML is just curve fitting.” Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Smarten

APRIL 19, 2024

The value of an AI-focused analytics solution can only be fully realized when a business has ensured data quality and integration of data sources, so it will be important for businesses to choose an analytics solution and service provider that can help them achieve these goals.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

FineReport

JULY 18, 2022

Key features: As a professional data analysis tool, FineBI successfully meets business people’s flexible and changeable data processing requirements through self-service datasets. FineBI is supported by a high-performance Spider engine to extract, calculate and analyze a large volume of data with lightweight architecture.

Forecasting 52

Forecasting Dashboards Statistics Visualization 52

Alation

JANUARY 27, 2022

Creating a single view of any data, however, requires the integration of data from disparate sources. Data integration is valuable for businesses of all sizes due to the many benefits of analyzing data from different sources. But data integration is not trivial. Establishes Trust in Data.

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

IBM Big Data Hub

AUGUST 23, 2023

If you add in IBM data governance solutions, the top left will look a bit more like this: The data governance solution powered by IBM Knowledge Catalog offers several capabilities to help facilitate advanced data discovery, automated data quality and data protection. and watsonx.data.

Data Governance 52

Data Governance Enterprise Modeling Risk 52

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

Jet Global

FEBRUARY 25, 2025

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies.

Cost-Benefit 52

Cost-Benefit Data Collection Finance Reporting 52

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations. Simply put?

Metadata 52

Metadata IT Data-driven Metrics 52

Peter James Thomas

SEPTEMBER 19, 2018

Data Classification. Data Consistency. Data Controls. Data Curation (contributor: Tenny Thomas Soman ). Data Democratisation. Data Dictionary. Data Engineering. Data Ethics. Data Integrity. Data Lineage. Data Platform. Data Strategy. Information Governance.

Analytics 45

Analytics Testing Data Strategy Data Integration 45

Andrew White

JANUARY 9, 2022

We found anecdotal data that suggested things such as a) CDO’s with a business, more than a technical, background tend to be more effective or successful, and b) CDOs most often came from a business background, and c) those that were successful had a good chance at becoming CEO or CEO or some other CXO (but not really CIO).

Analytics 64

Analytics Measurement Data-driven Modeling 64

CIO Business Intelligence

APRIL 9, 2025

If we dig deeper, we find that two factors are really at work: Causal data versus correlated data Data maturity as it relates to business outcomes. One of the most fundamental tenets of statistical methods in the last century has focused on correlation to determine causation.

Enterprise 102

Enterprise Analytics Experimentation Machine Learning 102

datapine

MAY 17, 2021

If you have multiple databases from different touchpoints, you should look for a tool that will allow data integration no matter the amount of information you want to include. Besides connecting the data, the discovery tool you choose should also support working with big amounts of data. Why are they important?

Visualization 161

Visualization Data-driven Business Intelligence Dashboards 161

Jet Global

MAY 9, 2024

Batch processing pipelines are designed to decrease workloads by handling large volumes of data efficiently and can be useful for tasks such as data transformation, data aggregation, data integration , and data loading into a destination system. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

SEPTEMBER 26, 2023

Data Cleansing Imperative: The same report revealed that organizations recognized the importance of data quality, with 71% expressing concerns about data quality issues. This underscores the need for robust data cleansing solutions.

Data Quality 52

Data Quality Reporting Management Software 52

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130