datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning. Data unification and integration.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. 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.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

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

O'Reilly on Data

DECEMBER 12, 2019

Small residuals usually mean a model is right, and large residuals usually mean a model is wrong. 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.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

BizAcuity

NOVEMBER 13, 2024

Challenges in Achieving Data-Driven Decision-Making While the benefits are clear, many organizations struggle to become fully data-driven. Challenges such as data silos, inconsistent data quality, and a lack of skilled personnel can create significant barriers.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

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. Using automated data validation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand. Data engineers are unable to make these business judgments.

Testing 100

Testing Data Quality Metrics Cost-Benefit 100