Data analytics is no longer a nice-to-have, it is a vital business function, as critical to an organizations' success as IT or HR. Management expects data-based decision-making as a standard, and to make the most of their data, data teams need to be aware of the trends in data analytics, two of which include edge analytics and synthetic data. Edge analytics occurs close to the places where data is collected and where digital content and applications are consumed. It can enable real-time decision making based on data collected from internet-connected sensors on factory floors, transport networks, retail outlets, and remote locations. Though it is becoming increasingly cheap to send and store colossal volumes of data in the cloud, as data generation outpaces network capacity this becomes increasingly unsustainable.

AI Chatbots–AI chatbots represent a class of software that is able to simulate a user conversation with a natural language through messaging applications. The main attraction of the technology is that it increases user response rate by being available 24/7 on your website in order to provide better customer satisfaction. Chatbots use machine learning and natural language processing (NLP) to deliver a near human like conversational experience. AutoML–Automated machine learning or AutoML is the process of automating the end-to-end process of applying machine learning to achieve the goals of data science projects. AutoML is an attempt to make machine learning available to people without strong expertise in the field, although more realistically it is designed to help increase productivity of experienced data scientists by automating many steps in the data science process. Some of the advantages of using AutoML include: (i) increasing productivity by automating repetitive tasks which enables a data scientist to focus more on the problem rather than the models; (ii) automating components of the data pipeline helps to avoid errors that might slip in with manual processes; and (iii) AutoML is a step towards democratizing machine learning by making the power of machine learning accessible to those outside the data science team.

By Sciforce, software solutions based on science-driven information technologies. One of the major IoT trends for 2019 that are constantly mentioned in ratings and articles is edge analytics. It is considered to be the future of sensor handling, and it is already, at least in some cases, preferred over usual clouds. First of all, let's go deeper into the idea. Edge analytics refers to an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch, or another device instead of sending the data back to a centralized data store. What this means is that data collection, processing, and analysis are performed on-site at the edge of a network in real-time.

Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT (IIoT). For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? I find it essential that the implementation of the industrial internet is incorporated into the company's strategy and business development.

I grew up in a small manufacturing town in Northeast Iowa. The factory in my hometown made tractors (no surprise given that it was Iowa), but eventually the economics of cheap foreign labor and an interconnected global economy caught up with that factory – as it did with many US-based manufacturers – and soon the factory closed, and many people were laid off. But the technology world continues to evolve – especially with respect to IoT, Data Science and AI/ML – and so comes an opportunity for manufacturing to make a big return to the US. However, tomorrow's manufacturing battles won't be fought with cheap labor. In fact, measuring a country's manufacturing strength by the number of manufacturing jobs is fighting yesteryear's battle.

It suddenly seems as if artificial intelligence (AI) is all around us. It is already having a huge impact in a wide range of sectors, including health care. We are now starting to see it move into the manufacturing sector as an important part of Industry 4.0, the digitisation of manufacturing. For example, an innovative quality control model has been developed in a microchip manufacturing facility in Taiwan using the full potential of AI. The microchip industry is the backbone of the digital age.

Let me start with a piece of trivia. As of today, research suggests there are already more connected "things" than human beings on earth. Not surprising, really, considering that each of us possesses multiple devices to simply stay connected. Think of these countless everyday physical objects being connected to the internet. Some studies predict that the number of connected devices will touch 50 billion by 2020.

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.

To solidify its position at the center of the industrial internet of things (IIoT), GE Digital is adding features to its Predix platform as a service (PaaS) that will let industrial enterprises run predictive analytics as close as possible to data sources, whether they be pumps, valves, heat exchangers, turbines or even machines on the move. The main idea behind edge computing is to analyze data in near real-time, optimize network traffic and cut costs. At its annual Minds Machines conference this week in San Francisco, GE Digital, the software arm of industrial conglomerate GE, is offering an array of new applications and features designed to run at the edge network and let companies more efficiently and precisely plan service times and predict equipment failure. The new apps, which extend the Predix Edge platform announced at Minds Machines last year, also are meant to connect information and operational technology (OT and IT) systems to better manage companywide assets, for example bringing data from the factory floor and inventory facilities into ERP and supply-chain systems that may reside in corporate data centers or in the cloud. As enterprises try to get a handle on the vast amount of data generated by IoT devices, some of the biggest names in tech are offering a variety of apps and cloud services to help.

The concept of an IOT application platform is already an anachronism. Scan the web and you'll find over 400 vendors, from startups to huge firms such as GE making the case that their platforms are the future of edge analytics for IOT. Their narrative has roots in the fabulous innovation of the last 10 years by the major cloud vendors, starting with the concept of Infrastructure as a Service (IaaS), and progressing (at least theoretically) to Platform as a Service (PaaS). Of course Software as a Service (SaaS) is a huge winner here, spanning everything from Office 365 to Salesforce.com. This narrative says that success depends on how well you build, orchestrate and run your new application in the cloud.