When it comes to cloud, being an early adopter does not necessarily put you ahead of the game. I know of companies that have been perpetually “doing cloud” for 10 years, but very few that have “done cloud” in a way that democratises and makes data accessible, with minimal pain points. Cloud is an enabler. It makes it easier to collect, analyse, and disseminate information.

The COVID-19 pandemic has shone a bright light on the need for flexible and engaging leadership. Most of the recent leadership-themed articles focus on how to bridge the remote-only workplace relationship gap, the importance of increased engagement and empathy, and the need to provide technology that empowers and provides a more equitable work/life balance.

For decades, the analytics/BI community has suffered from low user adoption (~30%). Dashboards and fancy visualizations have only proven to be the starting point of the analytics journey, not the endpoint. We are living in a world that demands far greater agility than a fixed layer of information or KPI can provide. Poor user adoption is the result when the analytics system fails to support the users full journey: from data – to insight – to action.

Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds.

This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.

In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.