Take-Two Interactive Software, Inc. is a leading developer, publisher, and marketer of interactive entertainment for consumers around the globe. For more than 25 years, our development teams have created some of the most critically acclaimed and commercially successful entertainment experiences, captivating, and engaging audiences around the world. We are incredibly proud of our ability to consistently deliver the highest-quality titles, as well as our colleagues who help to create our unique culture and work environment that is inclusive, diverse, and dynamic. While our offices are casual and inviting, we are deeply committed to our core tenets of creativity, innovation and efficiency, and individual and team development opportunities. Our industry and business are continually evolving and fast-paced, providing numerous opportunities to learn and hone your skills.

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. A critical challenge in enterprise data science is to enable an effective whole-of-enterprise data understanding and data-driven discovery and decision-making on all-round enterprise DNA. We introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science.

From analyzing large volumes of data to building contact tracing applications or using machine learning algorithms to discover effective treatments for COVID-19 quickly, the demand for data scientists with diverse skill sets and backgrounds has soared. While Glassdoor has ranked data science as one of the Top 10 Best Jobs in America every year since 2015, the field, unfortunately, remains dominated by men and often fails to attract female talent, with only 16% of women making up the data science workforce. It can be hard for women who are just starting out to know what their career paths should look like, especially during challenging times. Having spent the past eight years working in enterprise data science within the science, government and enterprise sectors, I've learned what it takes to stand out in a male-dominated field and how critical it is to show the impact of your work, understand which skills are important to hone and how to overcome imposter syndrome. Here are the four things I wish I knew before getting into the field of data science.

AI is having a profound impact on customer experience, revenue, operations, risk management and other business functions across multiple industries. When fully operationalized, AI and Machine Learning (ML) enable organizations to make data-driven decisions with unprecedented levels of speed, transparency, and accountability. This dramatically accelerates digital transformation initiatives delivering greater performance and a competitive edge to organizations. ML projects in data science labs tend to adopt black-box approaches that generate minimal actionable insights and result in a lack of accountability in the data-driven decision-making process. Today with the advent of AutoML 2.0 platforms, a white-box model approach is becoming increasingly important and possible.

Nassim Nicholas Taleb, in his book "The Bed of Procrustes" noted that "people focus on role models; it is more effective to find anti-models -- people you don't want to resemble when you grow up." While Taleb's quote is commonly referenced across journalism and social media today, it isn't as applicable to real life as we might think -- at least not to real business life. Rather than seeking out contrarian voices, industry leaders tend to be overly influenced by mainstream thinking. Consider all the searches for those SEO-optimized webpages that perennially comprise page-one search results, reinforcing the same timeworn, made-for-PowerPoint sets of ideas over and over again. "Establish a data lake," they instruct business users. It is staggering to consider the number of companies fundamentally built on such "best practices", their leaders devoutly believing that in doing so they will roll out inevitably trend-setting products and features and win the battle for customers. Thinking in the fields of Data Sciences and Artificial Intelligence (AI) has been no different. In fact, "machine learning" has been the poster child of enterprise success stories for the last decade.

Check out Steve Touw's session "How will the GDPR impact machine learning?" Much has been made about the potential impact of the EU's General Data Protection Regulation (GDPR) on data science programs. But there's perhaps no more important--or uncertain--question than how the regulation will impact machine learning (ML), in particular. Given the recent advancements in ML, and given increasing investments in the field by global organizations, ML is fast becoming the future of enterprise data science. This article aims to demystify this intersection between ML and the GDPR, focusing on the three biggest questions I've received at Immuta about maintaining GDPR-compliant data science and R&D programs. Granted, with an enforcement data of May 25, the GDPR has yet to come into full effect, and a good deal of what we do know about how it will be enforced is either vague or evolving (or both!).

Announced in a press release on Friday, IBM said the new platform will be compatible with "all" cloud networks. It's based on the Kubernetes container architecture, an open-source modularisation system originally created by Google. Applications developed using Cloud Private for Data can be deployed in minutes to cloud environments. The platform is engineered to assist businesses in building new apps and utilising data. IBM is specifically positioning the cloud to uncover "previously unobtainable insights" that could help companies streamline their operations.