Alation

SEPTEMBER 9, 2021

Banks collect and manage a lot of sensitive data. And, the data collection doesn’t stop there — rich insights like transactions and purchasing information help to round out customer profiles. In banks, this means: Setting data format standards. Tagging data types. Incorporating automation.

Data Governance 52

Data Governance Data Strategy Strategy Risk 52

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

SEPTEMBER 7, 2023

It has an end-to-end process for building and testing foundation models and generative AI — starting with data collection and ending in control points for tracking the responsible deployments of models and applications — focused on governance, risk assessment, bias mitigation and compliance.

Modeling 102

Modeling Risk Unstructured Data Enterprise 102

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

Data Collection 77

Data Collection Visualization Data-driven Unstructured Data 77

Domino Data Lab

JANUARY 21, 2021

There is a risk of injecting bias. First, I load the dataset and do a quick check to see the size of the data we’re working with: Note: the full dataset, with data collection back to 1987, is significantly larger than 300,000 samples. Working With Unstructured Data & Future Development Opportunities.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance.

Snapshot 83

Snapshot Data Lake Testing Strategy 83

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues.

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Metadata 65

Sisense

JULY 23, 2021

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

Unstructured Data 105

Unstructured Data Internet of Things Data-driven Data Processing 105

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Before going all-in with data collection, cleaning, and analysis, it is important to consider the topics of security, privacy, and most importantly, compliance. Think of security, privacy, and compliance.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

IBM Big Data Hub

AUGUST 4, 2023

Data within a data fabric is defined using metadata and may be stored in a data lake, a low-cost storage environment that houses large stores of structured, semi-structured and unstructured data for business analytics, machine learning and other broad applications.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

Jet Global

MAY 9, 2024

The architecture may vary depending on the specific use case and requirements, but it typically includes stages of data ingestion, transformation, and storage. Data ingestion methods can include batch ingestion (collecting data at scheduled intervals) or real-time streaming data ingestion (collecting data continuously as it is generated).

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52