O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

IBM Big Data Hub

JANUARY 9, 2024

Currently, no standardized process exists for overcoming data ingestion’s challenges, but the model’s accuracy depends on it. Increased variance: Variance measures consistency. Insufficient data can lead to varying answers over time, or misleading outliers, particularly impacting smaller data sets.

Enterprise 78

Enterprise Data Integration Data Quality Contextual Data 78

erwin

FEBRUARY 4, 2021

It uncovered a number of obstacles that organizations have to overcome to improve their data operations. 1 bottleneck, according to 62 percent of respondents, was documenting complete data lineage. Overcoming Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Cloudera

SEPTEMBER 7, 2023

This puts the onus on institutions to implement robust data encryption standards, process sensitive data locally, automate auditing, and negotiate clear ownership clauses in their service agreements. But these measures alone may not be sufficient to protect proprietary information. Systematize governance. AI-ify risk management.

Insurance 79

Insurance Risk Data-driven Finance 79

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors.

Cost-Benefit 82

Cost-Benefit Metadata Data Governance Modeling 82

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. – Yes and no.

Analytics 64

Analytics Measurement Modeling Data-driven 64

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

datapine

FEBRUARY 22, 2022

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

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

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

Jet Global

MARCH 4, 2024

Data inconsistencies become commonplace, hindering visibility and inhibiting a holistic understanding of business operations. This lack of integration makes it difficult to track progress, measure performance, and identify potential issues. Data governance and compliance become a constant juggling act.

Management 52

Management Data Governance Reporting Software 52