Security remains one of the top unresolved challenges for businesses. Billions of dollars have been spent on security technology over the last 30 years, yet hackers seem to be more successful than ever. Every organization is now under extreme threat, all the time. Today, hacking is a much more complex art than it used to be: It no longer only involves just scanning and penetrating the network via a vulnerability. Yet the traditional security tools used by most companies are often inadequate because they still focus on this, ignoring what is now a very complex post-compromise chain of events.

Amazon has just launched a retail experience like no other. Customers are now, thanks to AI technology, able to walk in, grab what they want and walk out. And, while still in the beta phase of testing, Amazon Go is set to shake things up on a number of levels including business. Tim Dunlop of The Guardian says it's confirmation that we're moving from a globalized world of manufacturing giants to a networked one of technology giants. So just what does Amazon's Go mean for business?

Over 1,000 attendees, 50 speakers and 30 exhibitors; this is a brief summary of what I was lucky enough to take part in during the first AI Europe 2016 conference held in London on the 5 and 6 December. The attendee list boasted the biggest names from the world of artificial intelligence such as Microsoft, Dell, Uber, Samsung and Nvidia, as well as several innovative start-ups, the likes of Blippar and DreamQuark whose innovations are based on machine or deep learning models. Even if we can say with a degree of certainty that further advances in artificial intelligence are yet to come, leading players are in agreement that most AI techniques and technologies are now well-advanced. Therefore, their major preoccupation today is more about the quality of the data sets being used to train and validate their machine and deep learning models. Whether it's Dell or Uber, Microsoft or Blippar, they all have one thing in common: they all agree on the fact that as of now, the quality of the data used in AI for machine learning is of the utmost importance.

Every year I pick out digital and marketing trends and developments which I think will shape the industry and its planning and thinking in the year ahead. There is an increasingly blurred line between'digital marketing' and'marketing' but the following trends focus on the digital elements of marketing. In part 1, I looked at broad macro trends affecting brands, and in part 2, marketing-specific trends. Econsultancy's recent research on'The New Marketing Reality' with IBM highlights the many challenges facing digital marketing: fragmentation, complexity, challenges in understanding the customer journey, challenges with organisational and data silos, confusion around metrics and what good looks like, managing both generalist and specialist agencies and vendors at the same time, lack of capability in areas like data and customer experience, faltering attempts to be more agile, lack of clarity in strategy and leadership. There is nothing particularly new here and there will not be in 2017.

Ask any marketer what's on their to-do list in 2017, and they'll tell you they have a project underway to achieve a 360-degree view of the customer, Tealium's global CEO, Jeff Lunsford, says. "Any marketer is going to be looking to pull in data about that customer or a prospect from the myriad points where data is available in this new world," he says. "This could be IoT, mobile devices, or customer care. "Every marketer will nod yes, they want to leverage all the data they possibly can. So there's vision sync across the industry, the question is, how to do that." Tealium is one of a growing number of vendors looking to provide that answer with its Universal Data Hub, a software solution aimed at addressing data fragmentation for marketers across online and offline channels. The platform brings together the vendor's AudienceStream and DataAccess solutions with its iQ foundational technology. Since launching six years ago, Tealium has spent several years integrating its offering with more than 1000 applications across the marketing ecosystem, and recently raised another US$35m in capital, off the back of increased investment earlier in 2016, bringing total funding to $112.9m. Tealium now has 750 enterprise customers globally, from small digital-first companies to the largest, mature organisations. Australian clients include Cronulla Sharks, Nude by Nature, Greenstone Financial, and Melbourne University, while Asia-Pacific clients include Cathy Pacific. Speaking to CMO during a visit to Australia this week, Lunsford described Tealium as the "neutral layer down the stack of the marketing cloud", and the common management component organisations need in order to be able to exchange data across multiple best-of-breed systems in real time. Rather than competing with the large marketing cloud providers, he sees Tealium's role as being a complementary component. Not surprisingly, Lunsford sees technology as providing the foundational layer marketers need across customer touchpoints to pull that 360-degree vision off. "Companies use multiple software applications to create the customer experience, each has its own idea of the customer, and most don't talk to each other," he says. "The average Tealium customer has 26 software applications that contribute to the customer experience.

At Curalate, we're fascinated by the future of computer vision and machine learning. Those technologies have made serious strides over the past few years and the future looks incredibly bright. Curalate is doing its part to define how artificial intelligence meets commerce with Intelligent Product Tagging -- technology that can analyze an image and use machine learning to identify the products depicted within that image. For example: If you have a photo of a woman wearing a floral dress, our technology can identify that dress, then visually match it with the corresponding product in a brand's catalog -- making the image shoppable. We expect to start introducing this tool to clients in 2017, but it's actually a much longer-term research effort for Curalate's talented product development team.

The clinical, emotionless computer or robot is a staple of science fiction, but science fact is starting to change: computers are getting much better at understanding emotions. As we turn to computers, smart devices and robots to do more and more functions that have always been the exclusive domain of humans, this emotion-detecting technology will become increasingly important. Automated customer service "bots" will be better able to know if a customer is getting the help they need. Robot caregivers involved with telemedicine may be able to detect pain or depression even if the patient doesn't explicitly talk about it. One insurance company I am working with is even experimenting with call voice analytics that can detect that someone is telling lies to their claims handers.

With Version 6.0, Kinetica introduces user-defined functions (UDFs), enabling GPU-accelerated data science logic to power advanced business analytics, on a single database platform. User-defined functions (UDFs) enable compute as well as data-processing, within the database. Such'in-database processing' is available on several high-end databases such as Oracle, Teradata, Vertica and others, but this is the first time such functionality has been made available on a database that fully utilizes the parallel compute power of the GPU on a distributed platform. In-database processing in Kinetica creates a highly flexible means of doing advanced compute-to-grid analytics. This industry-first functionality stands to help democratize data science.

This heightened interest in AI is also reflected in the sheer volume of popular fiction works such as Wetsworld, HumansandEx Machina, which deal with the moral dilemmas of autonomous robots and thinking machines. Yes as much as we're fascinated by these (as yet) fictional scenarios, many still struggle to grasp exactly how this technology will – and in many cases already does – affect our everyday lives. For businesses this has become an imperative, however, and we have seen the focus of Big Data become much more commercially-oriented, centring on managing, measuring and monetizing so-called information assets. To secure an advantage in this data-driven landscape, organisations must develop real world solutions and applications with big data analytics that impact their bottom line. The CRM industry, for instance, has taken to artificial intelligence in a big way over the past year, with companies such as Salesforce, Oracle and Base developing tools to drive sales interactions through built-in intelligence. Personalization is one way in which that translates into tangible commercial impact, as it enables companies to scale their services without incurring prohibitive costs or compromising quality.

There have been numerous examples over the last two decades of how Natural Language Processing, or NLP, is being used by companies to provide an intelligent voice to gadgets and searches. Think, for instance, how the world of search engines--from Yahoo, Microsoft and Google--have changed the Internet with text-based search algorithms driving and augmenting the World Wide Web. NLP, though, does much more than just that and text analytics. NLP exploration on our current digital planet includes voice searches on automobiles and then, of course, the dictation mechanics of the software world. I have an 18-month-old who thrives on YouTube searches asking for'Peppa Pig' or'Mickey Mouse' series while my 5-year-old is exploring the world of content on YouTube (of course, with restricted parental control)--from watching the world of KungFu Panda to how to make dummy videos.