Sisense

OCTOBER 6, 2020

Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” The challenge comes when the data becomes huge and fast-changing.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

datapine

JULY 17, 2019

Typically presented in the form of an interactive dashboard , this kind of report provides a visual representation of the data associated with your predetermined set of key performance indicators – or KPI data, for short. Consider your data sources. Set up a report which you can visualize with an online dashboard.

KPI 223

KPI Reporting Key Performance Indicator Dashboards 223

AWS Big Data

NOVEMBER 29, 2023

These announcements drive forward the AWS Zero-ETL vision to unify all your data, enabling you to better maximize the value of your data with comprehensive analytics and ML capabilities, and innovate faster with secure data collaboration within and across organizations.

Data Warehouse 103

Data Warehouse Data Lake Analytics Machine Learning 103

Rocket-Powered Data Science

SEPTEMBER 18, 2018

The way that a data scientist resolves that degeneracy (another data science word) is to introduce more parameters (higher variety data) in order to “look at” those overlapping clusters from different angles and perspectives, thus resolving the different diagnosis clusters.

Big Data 103

Big Data Data Science Measurement Data Collection 103

Andrew White

JANUARY 11, 2021

As such any Data and Analytics strategy needs to incorporate data sovereignty as per of its D&A governance program. Coding skills – SQL, Python or application familiarity – ETL & visualization? Do you have an example of how an organization improved data literacy in a really practical useful way?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52