Sisense

DECEMBER 30, 2019

If you’ve read our Building Data Dashboards for Business Professionals and 6 Tips for Data Teams , then you know how important planning and communication are when building dashboards. In this very visual post, we’ll discuss the elements that make or break a dashboard and dissect two examples. Happy building!

Dashboards 89

Dashboards Visualization Metrics Contextual Data 89

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 83

Data Science Data Analytics Prescriptive Analytics Analytics 83

Jet Global

AUGUST 14, 2019

Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

Jet Global

OCTOBER 2, 2019

Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Occam's Razor

JULY 15, 2014

It also handy explanations of the metrics, with key context where necessary. Allow me to visualize the problem above, and leverage that visualization to present the solution. In order to make smart decisions about the data you need four things. Hence your CXOs should definitely not get a data puke like the one above.

Dashboards 159

Dashboards Key Performance Indicator Snapshot Slice and Dice 159

Alation

FEBRUARY 20, 2020

It’s a truism that data is the most important asset in the 21 st century economy. But, today too many enterprises exist in a data fog, with poorly contextualized data scattered across millions of tables. Dispelling this data fog is one of the key challenges for the next generation enterprise.

Contextual Data 52

Contextual Data Data Lake Metadata Enterprise 52