EZINE:
In this week's Computer Weekly, we examine the European Parliament's digital vision for the next decade, including controversial plans for a regional internet. GDPR is two years old – we find out how well the law is working. And we look at how the coronavirus crisis is affecting digital skills recruitment and training.
EZINE:
Security remains a major concern for customers and a continuing area of growth for the channel – so what can managed service providers do to help out? Also read about expectations for the last quarter of 2022 and how partnerships are key to digital transformation.
ESSENTIAL GUIDE:
This article in our Royal Holloway Security Series sheds an often uncomfortable light on the privacy risks people incur by using social media, and offers advice on how to minimise those risks
ANALYST REPORT:
Using an online object storage platform with global namespaces can provide full data protection, high availability and easily managed eDiscovery in a way that fully supports the business, say analysts Clive Longbottom and Marcus Austin.
WHITE PAPER:
Find how an ERP Firewall can capture business process and data quality errors, so outsourcing data doesn't create mayhem within your company.
EBOOK:
How can you decide if you should invest in cloud BI tools? This expert guide reveals crucial information on cloud BI and analytics for IT decision-makers. Discover the biggest trends, benefits, and challenges of cloud BI tools.
WHITE PAPER:
The volume, variety, and velocity of big data make it difficult to ensure the information is trusted and protected -- traditional, manual methods of governance just aren't up to the task. In this resource, explore the challenges of managing big data and get best practices for data lifecycle management at the enterprise level.
WHITE PAPER:
Ensuring your data is secure and trustworthy is paramount to harnessing the power of big data, but it's also a difficult task when you've got such a large volume and variety of information coming into the business. Unfortunately, traditional methods of governing and correcting often aren't applicable to big data -- so what can you do?
