AWS Big Data

JUNE 14, 2023

Enabling teams to build their own analyses at scale The Insights team builds dashboards and supports thousands of internal consultants and hundreds of analysts and engineers across the globe who drive local products and insights. Last year, this team also reported over 29,600 distinct views on their 19 dashboards.

Dashboards 76

Dashboards KPI Scorecard Metadata 76

FineReport

FEBRUARY 5, 2020

They prefer self-service development, interactive dashboards, and self-service data exploration. Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. Analytics dashboards. Embed analysis content.

Metadata 98

Metadata Dashboards Informatics Visualization 98

AWS Big Data

NOVEMBER 16, 2023

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault.

Enterprise 85

Enterprise Data Warehouse Data Lake Optimization 85

AWS Big Data

JUNE 15, 2023

QuickSight is a unified BI service providing modern interactive dashboards, natural language querying, paginated reports, machine learning (ML) insights, and embedded analytics at scale. Q uses the same QuickSight datasets you use for your dashboards and reports so your data is governed and secured.

Sales 68

Sales Dashboards Visualization Testing 68

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. As new data appears, you will have to continuously fine-tune or further train the model.

Data Lake 90

Data Lake Unstructured Data Management Modeling 90

Rita Sallam

APRIL 2, 2023

Interpretation of our machine learning model suggests buying $50k in building coverage and $100k in warehouse content coverage, as the latter significantly boosts predicted flood claim payouts. Based on these estimators, SAS created an easy to use what-if dashboard. Such a specific goal enabled a far more useful analysis.

Optimization 52

Optimization Machine Learning Insurance Risk 52