Data management has always been crucial to maintaining business continuity for enterprise organizations. For a long time, though, data management referred to the storage of information and the occasional need to access that information. And, for much of that time, the importance of data management has come second to data analytics techniques like machine learning and artificial intelligence (AI). Now, in 2022, the critical nature of data management for enterprise organizations can no longer be understated. Organizations have so much data through which they need to sift that they can't afford to view data management as an afterthought to their data analytics efforts, especially considering that as much as 90% of data worldwide is unstructured.

Image by Riskified (used with permission). Managing Riskified's Data Science department entails a lot of recruiting -- we've more than doubled in less than a year-and-a-half. As the hiring manager for several of the positions, I also read through a lot of CVs. Recruiters screen through a CV in 7.4 seconds, and after recruiting for several years my average time is pretty fast, but not that extreme. In this blog, I'm going to walk you through my personal heuristics ('cheats') that help me screen a resume.

Managing Riskified's Data Science department entails a lot of recruiting -- we've more than doubled in less than a year-and-a-half. As the hiring manager for several of the positions, I also read through a lot of CVs. Recruiters screen through a CV in 7.4 seconds, and after recruiting for several years my average time is pretty fast, but not that extreme. In this blog, I'm going to walk you through my personal heuristics ('cheats') that help me screen a resume. While I can't guarantee that others use the same heuristics, and different roles will differ in the importance of each point, paying attention to these points can help you conquer the CV screen stage.

Deloitte considers the digital workplace to be the "natural evolution" of the workplace, and defines it as "all the technologies people use to get work done in today's workplace". That might be a simple definition, but the digital workplace already exists in most companies. The image below charts the direction of a simple intranet system employed by many companies to a fully digital workspace. Below we're going to discuss some of the must-haves to make your digital workspace even better than it already might be. Before you start thinking about anything to do with your digital workplace, you need to have a vision about exactly what you want to achieve.

From being not much more but a catchy phrase to one of the most promising and powerful technologies, AI has come a long way. While communication and digital service providers start to implement ever more solutions based on artificial intelligence and machine learning, the technology is growing stronger by the minute. The predictions, in fact, are that without AI-driven systems, telecoms won't be able to survive in a highly competitive, digital-first market. So, what is the state of AI implementation in telcos? What solutions are the most popular at the moment, and which are expected to surge in the coming months?

Artificial intelligence (AI) has been the most far-flung goal of mankind since the birth of the computer. However, we can certainly say that we are closer to that goal than ever with the advent of new cognitive computing models. In a layman's terms, cognitive computing is a mashup of cognitive science and computing science, where cognitive science studies the human brain and how it works and computing science deals with the innovative ways of using computers for the betterment of the community. Cognitive computing systems are used to find solutions to complex situations where answers are uncertain or ambiguous, using computerized models that simulate the human cognition process. Although the term is often used alongside AI, it is closely related to Watson, IBM's cognitive computer system.

Companies are today hard-pressed to find good machine learning talent, What they want from the pool of candidates, is one who already comes to the table equipped with the skill-sets, theories and coding ability needed for the task. The skill requirement is not only restricted to the knowledge of machine learning algorithms and when to apply what, but also how to integrate and interface. The core skills required are technical, with a good understanding of mathematics, analytical thinking and problem-solving. The theories of probability are the mainstays of most machine learning algorithms. If you are familiar with probability, you are equipped to deal with the uncertainty of data.

How would you describe 7LYTIX in a few words? What inspired you to create the company? We're experts in Artificial Intelligence (AI) and Data Science focused on very precise predictions for retail & supply chain management (SCM). Franziskos Kyriakopoulos & Emmerich Danner, a brilliant AI expert and a retail consultant met, had a good idea, and founded 7LYTIX in 2016. In contrast to regular enterprise resource planning (ERP) systems which just tell you something about what happened, we predict the future with more than 90% precision.

Training an algorithm to predict future outcomes, using a PCA algorithm to uncover clients' personality traits, uploading a corpus of text to extract sentiment and grouping 650 000 lines of CRM and weblog data to cluster clients with machine learning all sounded like unreachable rocket science to me a while ago. Now I do it as easily as I use Excel or Illustrator. They are my new secret weapons. In 5 years "proficient in machine learning" will be a must-have on any manager's resume (just like project management or strategic thinking/analytical thinking is today). Harvard Business Review says "The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML). But for now it's still a rare skill, a strong competitive advantage that only early adopters hold. And this might surprise you, but it's already within arm's reach. It's changed my brain a bit. As our lead data scientist Bernardo Nunes puts it…I've invested in a "growth ...

Barely a day goes by without a story taking the internet by storm about the use of Artificial Intelligence (A.I.), Machine Learning and Big Data, how these technologies will impact marketing, advertising agencies and nearly every type of company and industry. What's lesser discussed, is why these technologies are gaining so much traction. Digerati sat down with technology observer Cami Rosso to ask why, and why now. What do you think is driving this interest in A.I., Big Data and Machine Leaning? One aspect of this demand is that machine learning has quickly become the new'must-have' capability for forward-thinking software providers, principally because Machine Learning, a subset of A.I., enables computers to learn without hard-coding.