Data and analytics capabilities have advanced leaps and bounds in the last few years. And no company wants to stay away from what data can bring to the table. Business have invested large amounts of money in gathering this information and building solutions on top this data. But have they really been able to reach where they wanted to? Have companies really been able to turn data into profits?

Subex has launched HyperSense, an end-to-end Augmented Analytics platform that helps enterprises make faster, better decisions by leveraging Artificial Intelligence (AI) across the data value chain. Developed based on Subex's extensive data analytics experience, HyperSense contains all the Augmented Analytics capabilities enterprises need in one flexible and modular platform. HyperSense's unique no-code capabilities allow users without a knowledge of coding to easily aggregate data from disparate sources, turn data into insights by building, interpreting, and tuning AI models, and effortlessly share their findings across the organization. First defined by Gartner, Augmented Analytics uses enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation. It empowers experts as well as non-data scientists by automating many aspects of data science, including model development, management and deployment of AI models.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

As technology advances, the amount of data that is being created is growing at a rapid pace. Where throughout the 1970s, '80s and '90s most data that was being collected was structured and placed in databases, emerging technologies like the internet, mobile phones and smart devices, are generating more data than ever before. In 2013, IBM wrote that each day 2.5 quintillion bytes are created, and it is estimated that 40 Zettabytes of data will have been created by 2020. But just as the Volume of Data is rapidly increasing, so is the Variety of Data. Everything from emails, tweets, images, video and music, are now digital and being stored not just on local devices but more and more in the Cloud.

Making sense of that information in a way that is useful, however, requires time and effort. From drug interaction information, to chemical reaction steps, to data on protein folding, a vast array of useful information is currently stored in formats that are designed to be read by humans, such as research articles, but cannot be easily leveraged computationally for other purposes. Until recently, for scientific data to become readily accessible by other means, a human had to interpret documents and enter relevant data into a database by hand. And while humans do an excellent job of comprehending and synthesizing information, human effort doesn't scale up the way computing power does. With the number and density of data sources ever on the rise, Elsevier and its collaborators hope to change the paradigm to enable computers to manage as much of the job of synthesizing data as possible, allowing humans to do better science with the results.

Video is the world's largest generator of data, created every day by over 500 million cameras worldwide. That number is slated to double by 2020. The potential there, if we could actually analyze the data, is off the charts. It's data from government property and public transit, commercial buildings, roadways, traffic stops, retail locations, and more. The result would be what NVIDIA calls AI Cities, a thinking robot, with billions of eyes trained on residents and programmed to help keep people safe.

A while ago Angela Lipscomb from SAS got in touch with me to get me introduced to SAS's concept of a Customer Decision Hub. Their Customer Decision Hub is a solution concept that shall allow organizations to derive insights and to trigger actions from interactions with external parties, like customers based upon rules and the derived insights. At the same time standard communications can get suppressed based upon the same set of rules. In other words, the Customer Decision Hub fosters customer engagement based upon inbound signals that get analyzed and processed through the organization. Why is this remarkable, I hear you asking?

Losing customers means losing revenue. Not surprisingly, then, companies strive to detect potential customer churn through predictive modeling, allowing them to implement interventions aimed at retaining customers. This might sound easy, but it can actually be very complicated: Customers leave for reasons that are as divergent as the customers themselves are, and products and services can play an important, but hidden, role in all this. What's more, merely building models to predict churn for different customer segments--and with regard to different products and services--isn't enough; we must also design interventions, then select the intervention judged most likely to prevent a particular customer from departing. Yet even doing this requires the use of analytics to evaluate the results achieved--and, eventually, to select interventions from an analytical standpoint.

Southfield, MI, June 2016 – Stefanini, a 1B global IT provider, announced today that the company is launching Sophie, its artificial intelligence platform with the ability to turn data into valuable solutions. Aware of the latest trends, Stefanini has invested and developed this platform over the last 3 years as a Research & Development and pilot project for clients in Brazil, and now, the company is launching the platform in the United States. "We are very proud to introduce Sophie for our clients in North America, reinforcing Stefanini's commitment to connect people and technology innovations with a goal to create business value," said Antonio Moreira, Stefanini CEO, North America and Asia Pacific. "Our artificial intelligence platform can improve the end-user experience and deliver smarter and more efficient services," affirmed Mr. Moreira. Technology research firm Gartner forecasts that by 2017, autonomics-based managed services and cognitive platforms will fuel a significant reduction in the cost of IT services by automating repetitive tasks currently tackled by humans.