Shadow IT can have negative business impact because it breaks with all the processes and rigors that the IT department is so diligently trying to put in place for their employer’s digital transformation! Digital Transformation needs rigor to succeed and has to start with the IT department identifying and implementing the right DevOps tools. Once the development tools are in place, then we can put in the methodologies for interaction with the various lines of business and make sure that we have a governing board and Center of Excellence in place to make sure that we can successfully fail fast. Digital transformation will fail if the lines of business are left on their own and go rogue. The key to an organization being able to successfully implement its digital transformation absolutely does require strong DevOps.

What’s clear is that law enforcement agencies deploying Palantir have run into a host of problems. Exposing data is just the start. In the documents our requests produced, police departments have also accused the company, backed by tech investor and Trump supporter Peter Thiel, of spiraling prices, hard-to-use software, opaque terms of service, and “failure to deliver products” (in the words of one email from the Long Beach police). Palantir might streamline some criminal investigations—but there’s a possibility that it comes at a high cost, for both the police forces themselves and the communities they serve. These documents show how Palantir applies Silicon Valley’s playbook to domestic law enforcement. New users are welcomed with discounted hardware and federal grants, sharing their own data in return for access to others’.

Previous generations of business intelligence tools assisted with the cognitive awareness of clicks and page loads on the web and mobile arenas, but required a sprawling architecture that included brick-and-mortar data warehouses and separate visualization tools. This created a complicated workflow and failed to provide a full and efficient analysis of business analytics. In fact, data warehouses still have a complicated integration process, requiring knowledge of SQL and a development team. Not to mention the separate products to build out the warehouse are a huge expense, Mixpanel for funnel analysis, Amazon Redshift data system, and Tableau for dashboarding and BI. However, “Cloud deployments of BI and analytics platforms have the potential to reduce cost of ownership and speed time to deployment,” according to Rita Sallam, research vice president at Gartner.

The ramifications of skills and staff deficiencies are also apparent in the research. Cybersecurity operations staffs are particularly weak at things such as threat hunting, assessing and prioritizing security alerts, computer forensics, and tracking the lifecycle of security incidents. Of course, many CISOs propose an easy fix — simply hire more cybersecurity staff to bridge the knowledge and staffing gaps. In fact, 81% of the cybersecurity professionals surveyed say their organization plans to add cybersecurity headcount this year. Unfortunately, that isn’t always easy to do. According to the ESG research, 18% of organizations find it extremely difficult to recruit and Hire Additional Staff for cybersecurity analytics and operations jobs, while another 63% find it somewhat difficult to recruit and hire additional staff for cybersecurity analytics and operations.

“First off, GDPR is a good thing as it is to protect all of our data and aims at preventing breaches. There is a lot of scaremongering about the new regulation, which needn’t be the case. “However, that doesn’t mean it shouldn’t be taken very, very seriously indeed. We are way, way behind still unfortunately but thankfully there does seem to be growing awareness,” he said. According to cyber security experts, under the new regulation, Irish firms will have to comply with up to 90 principles relating to data protection. Mr Murphy added: “What it boils down to is that data protection officers will be able to ask how data is stored, protected, kept and used on customers, consumers, employees, etc. It will affect companies, government agencies, private public partnerships, universities,” he said. He said he would advise firms to carry out a readiness assessment to see how prepared they were for the new law.

The most important step that companies can take is to build your culture change on a solid foundation of good policies. These need to be security policies customized to what you really want people to do, and not just paperweight. The policies need to be written and delivered in a way that is realistic for people to read and understand, and also as a quick reference in any given situation. These policies are critical not only to ensure your company is protected, but also in building trust among your customers and partners. At CA, we created five short, focused policy documents on different aspects of information security. We based these on the NIST Cybersecurity Framework and use the information security functions listed in the framework as the foundation for each document.

Behavioral marketing consumes online data and uses this data to power tailored messages to the user. These individualized messages can be sent in real-time, making it easier for brands to stay top-of-mind with busy consumers. More than any other industry, banks and credit unions have a wealth of data at their disposal. So, creating a personalized customer experience should be easier for the financial services industry than other, less connected alternatives. The challenge, however, has been access to real-time data to deliver responsive, personalized customer interactions that make doing business with you easier and more convenient. Behavioral marketing platforms have made it possible for companies to access real-time and historical behaviors of customers to identify key trends and to individualize customer interactions through responsive digital marketing.

With traditional machine learning, the computer has to be told which cat features -- whiskers, paws and tails -- to look for in the images. These hand-engineered models then make predictions based on those features. If an image doesn't follow the rules, the machine can't adapt. If a cat's tail is out of the frame, for example, the computer might not even know it's a cat. A baby, on the other hand, needs no such guidance. After viewing enough images, the baby will build a mental framework to distinguish what is or isn't a cat. Deep Learning, like the baby, takes unstructured input without guidance and determines for itself, while considering all pixel values, which among the images contains a cat. Given enough time and data, deep learning models can make sense of virtually any unstructured data set.

The impact of employee engagement on loyalty, productivity, innovation and customer satisfaction has revealed that an engaged workforce is not simply a nice-to-have, it’s a necessity for creating an innovative business that can withstand the constant flow of new competition. This has led executives to look to HR to recreate and drive the shift toward employee-centered processes, environments and strong value-based cultures, otherwise known as the employee experience. However, a gap still exists between translating data into actionable changes. Employee engagement is an abstract metric. Identifying a dip in your most recent engagement survey may not yield the information you need to find out why your people are less satisfied with their work life than the month before and how to address it. The challenge HR faces today is how to make people data human.

Big data and deep learning are technologies cut from the same cloth. They’re both enabled by the explosion of data brought about by the digital world. But to deal effectively with mountains of big data, data scientists have developed data architectures, supporting infrastructure, and software tools—like Apache Hadoop* and Spark*—that distribute data over networks of industry-standard servers, process it where it lives, and rapidly consolidate the results. Now, an Intel initiative called BigDL promises to quickly bring machine learning into the mainstream by enabling deep learning apps to piggyback on the same familiar infrastructure and take advantage of the same data architectures that enterprises and cloud service providers (CSPs) already put in place for big data analytics.