Smart Data Collective

MARCH 21, 2022

Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics. Even today, there are a large number of them using data lakes to drive predictive analytics. The cloud age did address that issue to a certain extent.

Data Lakes 75

Data Lakes Hadoop Data Silos Data Warehouse 75

Alation

MARCH 21, 2023

While this industry has used data and analytics for a long time, many large travel organizations still struggle with data silos , which prevent them from gaining the most value from their data. What is big data in the travel and tourism industry?

Analytics 52

Analytics Analytics Data Silos Big Data 52

IBM Journey to AI blog

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

Data Warehouse 58

Data Warehouse Cloud Data Analytics Analytics 58

Pickl AI

DECEMBER 11, 2024

Efficiency emphasises streamlined processes to reduce redundancies and waste, maximising value from every data point. Common Challenges with Traditional Data Management Traditional data management systems often grapple with data silos, which isolate critical information across departments, hindering collaboration and transparency.

Data Silos 52

Data Silos Data Pipeline Artificial Intelligence Artificial Intelligence 52

phData

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. These changes are streamed into Iceberg tables in your data lake. New user sign-up? Workout completed?

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Alation

JANUARY 27, 2022

Raw data includes market research, sales data, customer transactions, and more. Analytics can identify patterns that depict risks, opportunities, and trends. And historical data can be used to inform predictive analytic models, which forecast the future. What Is the Value of Analytics?

Data Governance 52

Data Governance Analytics Analytics Data Quality 52