Anmut

MARCH 22, 2023

This ultimately allows for more effective goal-setting, with targets determined according to both your data maturity right now and the desired stage you want to attain in the future. Why do we need data maturity models? A data maturity model helps your company measure its data and business health.

Measurement 52

Measurement Modeling Data Quality Data Governance 52

Rocket-Powered Data Science

MARCH 19, 2021

The vast scope of this digital transformation in dynamic business insights discovery from entities, events, and behaviors is on a scale that is almost incomprehensible. Traditional business analytics approaches (on laptops, in the cloud, or with static datasets) will not keep up with this growing tidal wave of dynamic data.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

Cloudera

AUGUST 6, 2021

We have increasingly moved purchases online in recent years fueled by the COVID-19 pandemic and general trends in Digital Transformation. Like most labels, “data-driven” is not a binary, black and white measure of capability. Data Strategy. Data and decision culture. Conclusion.

Data-driven 104

Data-driven B2B Data Strategy Strategy 104

Alation

JUNE 14, 2023

Every organization wants to better serve its customers, and that goal is often achieved through data. Situationally, it was a really good time to deploy a data mesh architecture and its principles and invest in this space because we were doing so much tech modernization,” Lavorini says. That’s a problem!

Data-driven 52

Data-driven Finance Uncertainty Digital Transformation 52

Anmut

APRIL 6, 2021

Enterprise digital transformation and data. Most organisations undergoing a digital transformation understand that data is critical, but how many are actually managing data as an asset ? Your data isn’t fit for purpose. Your digital transformation initiatives fail. The result?

Digital Transformation 52

Digital Transformation Enterprise Data Quality Data-driven 52

Sisense

NOVEMBER 23, 2020

Some challenges still remain, however, such as underdeveloped data capabilities, the ability to clearly articulate a business problem, and then measure the data/technical solution in a quantifiable way. The changing role of the data professional. Democratization of data.

Analytics 98

Analytics Data-driven Marketing Reporting 98