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A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

Following this, in 2002, it began delivering its knowledge to customers in online format, using dashboards and interactive reports that provided easier and faster access to data and analysis. Additionally, it continuously explores reams of data and modern tools to improve its capabilities and adapt to the changing data landscape.

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7 famous analytics and AI disasters

CIO Business Intelligence

PHE uses an automated process to transfer COVID-19 positive lab results as a CSV file into Excel templates used by reporting dashboards and for contact tracing. According to the New York Times , in 2002 Target’s marketing department started wondering how it could determine whether customers are pregnant. The culprit?

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

If your business is using big data and putting dashboards in front of analysts, you’re missing the point.”. For example, a request for a descriptive dashboard to “compare whether a red button or a blue button leads to lower churn” might be better served by a prescriptive model to personalize pages so that customers churn less.

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The Data Visualization Design Process: A Step-by-Step Guide for Beginners

Depict Data Studio

Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations. Sometimes people use dashboard and infographic interchangeably, but these visual modes are quite different. Nothing screams novice!

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A CDO’s Guide to the Data Catalog

Alation

In 2002, Capital One became the first company to appoint a Chief Data Officer (CDO). This has led to mistrust of data as businesspeople find errors in their reports and dashboards over and over again. Since then, many other companies have added a CDO to their c-suite, and the responsibilities of the role have grown.

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Unintentional data

The Unofficial Google Data Science Blog

More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Every data scientist surely has a story of identifying important issues by monitoring metrics on dashboards without having any particular hypothesis about what they are looking for.