Remove 2017 Remove Cost-Benefit Remove IoT Remove Risk
article thumbnail

When Private Cloud is the Right Fit for Public Sector Missions

Cloudera

A quick trip in the congressional time machine to revisit 2017’s Modernizing Government Technology Act surfaces some of the most salient points regarding agencies’ challenges: The federal government spends nearly 75% of its annual information technology funding on operating and maintaining existing legacy information technology systems.

article thumbnail

AI Is Reaching New Milestones In Senior Care In 2019

Smart Data Collective

In 2017, the number of seniors over the age of 65 reached a record 1 billion people. New IOT devices will facilitate in-home senior care. The benefits of this are threefold: Artificial intelligence-driven robots reduce the need for human workers. This reduces the costs involved. Cutting marketing costs.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Strengthening cybersecurity in life sciences with IBM and AWS

IBM Big Data Hub

In 2017, 94% of hospitals used electronic clinical data from their EHR. Leading life sciences companies are discovering the power of cloud in enabling analytics and artificial intelligence (AI) , shrinking innovation cycles, and standardizing processes across global operations, among other benefits.

article thumbnail

Cloudera + Hortonworks, from the Edge to AI

Cloudera

We are both convinced that a scale-out, shared-nothing architecture — the foundation of Hadoop — is essential for IoT, data warehousing and ML. Our partners will benefit from a single standard to build on, and a larger company with more customers to work with. We have each innovated separately in those areas.

article thumbnail

Top 10 Analytics And Business Intelligence Trends For 2020

datapine

According to Gartner, poor data quality is estimated to cost organizations an average of $15 million per year in losses. We detailed the benefits and costs of good or bad quality data in our previous article on data quality management , where you can read the five important pillars to follow.