datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Smart Data Collective

SEPTEMBER 25, 2019

In one case occurring in June 2018, the University of Texas’s MD Anderson Cancer Center received a $4.3 However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely. Time to Take Action.

Technology 85

Technology Statistics Strategy Insurance 85

Sisense

JANUARY 14, 2021

The Billie BI team has decided to share the code for their testing project to help other data teams using Sisense for Cloud Data Teams. “We We believe this can help teams be more proactive and increase the data quality in their companies,” said Ivan. joining the BI team at Billie in 2018.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

Peter James Thomas

AUGUST 12, 2018

Let’s cover what can go wrong (note: this section is not intended to be exhaustive, links are provided to more in-depth articles where appropriate): Accuracy of Data that is captured. A number of factors can play into the accuracy of data capture. Honesty of Data that is captured. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

O'Reilly on Data

APRIL 19, 2021

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

Smarten

SEPTEMBER 6, 2019

’ 2017 has certainly proven this to be true, as businesses embrace the value of self-serve data preparation and analytics tools. Self-Serve Data Prep in Action.

Analytics 51

Analytics IT Business Intelligence Testing 51