Thanks to a partnership with CNN Chile, our analyses were aired every Monday as part of a weekly program devoted to Plataforma Telar, with more details posted on our website and social media accounts. Our results were regularly met with high engagement, shared by media companies and personalities, and even by convention members.a Plataforma Telar thus had a noticeable impact on how people understood the convention (for an analysis of how data-driven political communication impacts public opinion, see Daud2). Given the diversity, scale and dynamics of the data, our cloud infrastructure was increasingly becoming unwieldy, with relevant information about particular entities (for example, convention members) scattered around different tables. In order to better structure these data, we structured these data as a knowledge graph, called TelarKG, which could then be queried using MillenniumDB: an open-source graph database also developed within the IMFD.

There have been a lot of hot technologies to generate a buzz over the years, but this one feels a bit different. The rapid adoption of ChatGPT and other generative AI tech has led to a major investment and opportunity for Microsoft (followed by a buggy demo of Google''s AI ChatGPT competitor, Bard), and the halo effect of anything related to Artificial Intelligence has spread from tech stocks to crypto and across almost any function and industry you can imagine. The growth of ChatGPT's user base in particular has been incredible and has created a widespread excitement far beyond most of other recently hyped tech. It's not often that an early stage technology like this has captured such mainstream attention. Despite the mad dash to get involved in everything AI and feel of a potential bubble around the space, this technology has the potential to be a true game changer in a number of areas including education, programming, content creation and, yes, real estate.

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

Imagine for a moment, if you will, that you were a "dude" from the 1970s. And when I mean "The Dude" I really mean the "Dude". I mean the dude who is so much like the dude that it will make the protagonist of Big Lebowski shiver. And now think, if you had a friend called "Walter". And he says, "Dude, there is like this big company in like… 10 years. It'll sell computers and stuff and will revolutionize the tech industry."

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Latest reports show that the global master data management (MDM) market is expected to reach $27.9 billion by 2025. Looking at the humongous amount of data that is being generated -- 1.145 trillion MB per day -- the growth of the MDM market is inevitable. Fueled by data-heavy new technologies like artificial intelligence (AI), machine learning (ML), internet of things (IoT), robotic process automation (RPA), augmented reality (AR), virtual reality (VR) and social media platforms, this market will continue to grow at an unimaginable rate in the coming days. Enterprises need robust data management capabilities to make better business decisions, improve operational efficiency and drive superior customer experience (CX).

You might ask: what do artificial intelligence (AI) and AI-based tools have to do with customer experience (CX)? Here's what -- when asked which technologies most improve customer experience, 34% of sales and marketing leaders believe AI is the biggest game-changer. What's more, 73% of global consumers say they are open to businesses using AI if it makes life easier. From these stats (and hundreds more), it's evident that AI is here to stay. Be it inventory management, product design, marketing or even a simple email pitch, AI has helped eCommerce brands advance in leaps and bounds.

Tech conglomerate Tencent caused a stir last year with the announcement that it would comply with China's directive to incorporate facial recognition technology into its games in the country. The move was in line with China's strict gaming regulation policies, which impose limits on how much time minors can spend playing video games--an effort to curb addictive behavior, since gaming is labeled by the state as "spiritual opium." The state's use of biometric data to police its population is, of course, invasive, and especially undermines the privacy of underage users--but Tencent is not the only video game company to track its players, nor is this recent case an altogether new phenomenon. All over the world, video games, one of the most widely adopted digital media forms, are installing networks of surveillance and control. In basic terms, video games are systems that translate physical inputs--such as hand movement or gesture--into various electric or electronic machine-readable outputs.

As we enter 2022, CIOs and other IT leaders are predicting more of the same issues: a tech talent shortage that will stress organizations still working on modernization efforts and increased use of artificial intelligence and analytics, and enhancing security, among other innovative technologies. Among its predictions for 2022, Forrester believes "a tech talent panic will create broad gaps until new sourcing models go mainstream." IT organizations face a 13.8% attrition rate, reflecting a slow move to "future fit" talent strategies, according to Forrester. "The demand for technology talent maintains a frenetic pace with an emphasis on data and analytics, information security, architecture, cloud and engineering," said Craig Stephenson, managing director, North America Technology Officers Practice, at Korn-Ferry. Here's how they plan to cope.

Eugenie was incepted with an intent to solve two of the biggest problems the process industry faces today. First was the democratization of data-driven insights to drive complex operational decisions with a bottom-line impact. Second, to build an eco-system that leverages both human and machine intelligence to improve reliability, efficiency, and sustainability of assets as well as processes. Eugenie's unique decision intelligence and execution platform enables enterprises to make efficient and optimal operational decisions about their assets and processes. The company addresses issues related to anomalies in operations such as unscheduled downtime detection, production quality issue detections, process-deviation detection, etc., using its descriptive, prescriptive, and predictive analytics products.

Software delivery is a continuous process for most modern software teams On any given day, new code is being written, old code is refactored, third-party libraries are added and removed, external APIs are integrated, and plenty more. Software delivery is no longer an explicit stage at the end of development, but instead, it is a continual procedure within the daily development process, with deployments occurring daily or even hourly. Machine learning processes are now more than ever being applied to software deployment to save time and optimize processes so that software companies can continue to develop and deploy efficiently. What