Analysts Corner

MARCH 9, 2023

Asking computer science engineers to work on Excel can disappoint candidates who are looking forward to working on more sophisticated tools such as Tableau, Python, SQL, and other data quality and data visualisation tools. This is on top of the data analysis that I have done using SQL or profiling tools such as Alteryx.

Data Quality 130

Data Quality Data Analytics Data Analytics Skillset 130

Actian

JUNE 13, 2019

The most effective data solutions are formed by generalizing your user community into a few key groups by skillset and role objectives, data consumption habits and applied tools associated with those data needs, and then deploying the right set of solutions for each group.

Skillset 40

Skillset Finance Data Management Data Quality 40

Data Pine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

IBM cost 117

IBM cost Data Warehouse Business Intelligence Business Intelligence 117

Sisense

FEBRUARY 10, 2021

Utilities employ skilled professionals as knowledge workers, but creating a simple, visual way to analyze their data is a hard skillset to find in abundance. It started with implementing data validation rules, including basics like only accepting numbers for numerical inputs, etc.

Skillset 52

Skillset Data Requirement Data Quality Documentation 52

Data Pine

OCTOBER 25, 2022

With ‘big data’ transcending one of the biggest business intelligence buzzwords of recent years to a living, breathing driver of sustainable success in a competitive digital age, it might be time to jump on the statistical bandwagon, so to speak. click for book source**. click for book source**.

Business Intelligence 117

Business Intelligence Business Intelligence Big Data Data Mining 117

Domo

NOVEMBER 15, 2021

One of the defining characteristics of the AI developmental process is its need to be iterative; because of the heightened need for data quality and data volume, and the interrelationships between the training data, the model, and the interface data, iteration is critical to ending up with a robust, scalable, and deployable model.

Digital Transformation 52

Digital Transformation Skillset Innovation Monitoring 52