Today's Tech Digest

Using Big Data to transform business processes

Too often, businesses build data centers that are fragmented into unusable silos, which bar them from gaining the actionable insights they seek. One of the most overlooked of these silos is the call centre audio data, which is tremendously valuable since it holds the very voice of the customer in a specific moment in time," he says. "This is where the expertise and technology available with established analytics programs make the difference. Figuring out how to pull Big Data into one usable trove of information is a large part of the task, ultimately breaking open the floodgates for gaining valuable insights that allow businesses to operationalise on their findings." ... "A treasure trove of Big Data doesn't provide answers. A carefully managed analytics program designed around business goals and desired outcomes, alongside constant review of where the program is successful or needs improvement, is how organisations ultimately rise into the sweet spot of fast and efficient decision making and operationalisation of insights," he adds.

5 Reasons the Cybersecurity Labor Shortfall Won't End Soon

In late 2013, Cisco projected there were 1 million job openings globally. For several years after that, cybersecurity labor figures were only minimally updated. Various surveys (as opposed to research) have drastically underestimated the problem because they relied on polls that didn't sample enough companies, or they focused on information/IT security and failed to take the broader cybersecurity market into consideration. This leaves out heaps of workers involved with Internet of Things security, ICS (industrial control systems) security, automotive security, embedded security, and numerous other large categories. Some surveys, ..., portray a workforce with the number of unfilled cybersecurity jobs not even doubling in nearly a decade, from 2013 to 2022. This is a stark departure from my own research, which shows the number of unfilled positions actually is expected to grow 3.5 times during an even shorter timeframe, from 1 million in 2013 to 3.5 million in 2021.

5 top machine learning use cases for security

In principle, machine learning can help businesses better analyze threats and respond to attacks and security incidents. It could also help to automate more menial tasks previously carried out by stretched and sometimes under-skilled security teams. Subsequently, machine learning in security is a fast-growing trend. Analysts at ABI Research estimate that machine learning in cyber security will boost spending in big data, artificial intelligence (AI) and analytics to $96 billion by 2021, while some of the world’s technology giants are already taking a stand to better protect their own customers. Google is using machine learning to analyze threats against mobile endpoints running on Android -- as well as identifying and removing malware from infected handsets, while cloud infrastructure giant Amazon has acquired start-up harvest.AI and launched Macie, a service that uses machine learning to uncover, sort and classify data stored on the S3 cloud storage service.

Android vulnerability allows attackers to modify apps without affecting their signatures

“Although Android applications are self-signed, signature verification is important when updating Android applications. When the user downloads an update of an application, the Android runtime compares its signature with the signature of the original version. If the signatures match, the Android runtime proceeds to install the update,” Guard Square researchers explained. “The updated application inherits the permissions of the original application. Attackers can, therefore, use the Janus vulnerability to mislead the update process and get unverified code with powerful permissions installed on the devices of unsuspecting users.” The vulnerability (CVE-2017-13156) can be exploited to replace any kind of app, even a system app, without the user noticing anything or Android preventing the installation.

AI is a Business Imperative and Boardroom Agenda

In the age of the connected customer, the most effective method of closing the customer experience gap is for companies to invest in advanced predictive analytics and artificial intelligence (AI) powered customer relationship management (CRM) platforms. According the research, forward-looking companies have invested in new technologies capable of consolidating and analyzing key customer data and have reorganized to be able to act on that customer insight in a more nimble way. The biggest and most significant shift will be the use of advanced predictive analytics to drive data-driven customer experience decisions. The competitive battleground is now squarely based on superior customer experience, and only companies that invest in AI technologies can meet the ever-growing expectations of the hyper connected, and knowledge-sharing stakeholder - employees, partners and customers.

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