Cyber Insurance: State of Play

Cyber risks are real and are constantly evolving with technological advances and pervasiveness. Whether individuals, small business or multi-national – all might face a Cyber incident that can result in costly financial consequences. In times of heavy competition in classic insurance products and negative interest rate headwinds limiting returns from insurer’s bond portfolios, Cyber risks present a major opportunity for the insurance industry. With annual growth rates of up to 100%, global Cyber insurance market size predictions for 2025 range between $ 10 to 20+ bn. However, at this time Cyber also presents a risk least understood by the insurance industry. Read More

When the Cryptolocker Strikes – Reasons for Success of Ransomware

It was at a public sector customer’s site when our shared project mail account received a quite legitimate-looking email with an invoice in a .docm document and a request to verify its contents. The sender seemed to be a lady employed at the customer’s organization. But at second look, something was wrong with it. She was not employed here nor has she ever been. I showed it around the project team and we quickly got very skeptical, deleting it. According to the information security staff at the organization, we became victim to an attempt to infect our IT infrastructure with Locky, a piece of malware infecting more than 5000 PCs per hour in mid-February 2016 just in Germany and encrypting the data on all these PCs. Read More

Leveraging the Value of Big Data with Automated Decision Making

It is a widely accepted fact that we are living in the era of Big Data. Many traditional companies are looking for ways to improve their business through the virtues of Big Data and Data Science. While matured startups born in this era like Facebook and Twitter seem to naturally exploit the value of their data, many traditional companies struggle to find new ways of utilizing their data to leverage its value for their classical businesses. Read More

Predictive Analytics: Applying Machine Learning in Big Data Environments

Big Data applications do not only depend on exceptional technologies to efficiently store and access large amounts of data, but also require highly sophisticated analytics techniques in order to gain as much insight and added value from the available data as possible. Especially in the field of Predictive Analytics the combination of Machine Learning (ML) and computational statistics offers powerful tools to identify and describe trends and behavior patterns from the automated analysis of historical data. Many of these toolkits have been developed for scientific applications, e.g. for high-energy physics, and are very powerful in their capabilities. However, they are not directly targeted for today’s typical Big Data environments and thus no out-of-the box solution is yet available. So, the challenge lies in the integration of powerful analytic components into state-of-the-art Big Data technologies, creating a platform that is able to take advantage of sophisticated analytical techniques on large-scale data collections. Read More

How to Generate Real B2B Leads in the Social Web

Time and again we come across companies that really understand how to map their customer information requirements in a CRM.  So far so good.

However, just as often we come across companies where the sales department complains about insufficient support during the acquisition and entry of data, the sales management complains about insufficient data for controlling and cross selling and the marketing department complains about poor data quality for the next campaign. Often both groups of company are congruent. Read More

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