Data Mining

We are convinced that in the age of Artificial Intelligence and Industry 4.0, provi ding the most open possible access to data for all users serves as the basis for innova tion and progress. This starts with you as an interested private indivi dual and leads to your business enter prise, which wants to monetise its own data or use external data for new insights. Use open data to open up new perspec tives and lift your data project to the next level. In the sense of free access to data as a democratic basis for innova tions, we see open data as a core aspect of our offering. This is why we already combine almost 2 million open data records from over 8000 unique provi ders.

Artificial intelligence (AI) is one of several disruptive technologies that consumer products' organizations can implement to additionally propel their journey to digital maturity. Artificial intelligence innovations "perform or potentially augment tasks, assist better with illuminating decisions, and achieve goals that have generally required human insight, for example, planning and thinking from partial or uncertain information and learning. As such, AI advances can conceivably fortify an organization's upper hand in the commercial marketplace and improve the customer experience. Artіfісіаl intеllіgеnсе (AI) is as of now overwhelming the retail world and will progressively keep on doing so. Thе mаrkеt ѕіzе оf AI ѕоftwаrе аnd ѕуѕtеmѕ is relied upon to reach $38 million by 2025, and the potential opportunities for connecting with customers in new and progressively tweaked ways are making retailers put resources into such innovations.

Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed.

Predictive analytics is the technology that enables a look into the future. What data do you need? How do you get started with predictive analytics? What methods can you use?

Data is fundamental to the success of any organization. Data analytics gives insights into customer behaviour which thusly is utilized to fuel the vital activities of the business. Well-curated and comprehensive customer information can open up a universe of new opportunities dependent on solid numbers. Today, data reaches out past hard numbers. While realizing what number of changes you're getting from your site or having the option to figure the return on investment (ROI) of a marketing effort is as yet significant.

From working to accelerate insights into Coronavirus, to striving to manage supply chains the midst of social distancing, governments and enterprises are figuring out how to make optimal decisions during times of uncertainty and emotion. Sadly, despite many advances in predictive analytics, AI technologies – unless used within an augmented intelligence framework that combines both machine and human intelligence – often fall short of expectations. Join Genpact's Analytics Business Leader Amaresh Tripathy and guest Dr. Kjell Carlsson from Forrester to discuss how smart organizations are leveraging augmented intelligence to help ensure accuracy and relevancy of decisions and better outcomes. Genpact and Forrester will share examples from Fortune 500 leaders – across banking, consumer goods, retail, life sciences and health care – who are harnessing the power of augmented intelligence during this period of crisis.

Job seekers interact more with advancing tech than they realize as more companies turn to automated tools in talent acquisition. The hiring process has come a long way from the days of paper resumés and cold calls via landline. Online job sites are now staples in talent acquisition, but artificial intelligence (AI) and machine learning are elevating the recruiting and hiring landscape. When asked about the current status of AI and machine learning in hiring, Mark Brandau, principal analyst on Forrester's CIO team said, "All vendors are moving in that direction without question. The power of AI lies in its ability to process high volumes of data at fast speeds, improving efficiency and productivity for organizations. Those same features and benefits can also be applied to the hiring process. "As organizations look to AI and machine learning to enhance their practices, there are two goals in mind," said Lauren Smith, vice president of Gartner's HR practice. "The first is how do we drive more efficiency in the process?

In coping with an emerging crisis, the need for accurate and actionable information is paramount for effective response – but there has never before been a scenario like the current COVID-19 pandemic. Responders are looking to new technologies including IoT and AI to help tackle this outbreak, but their deployment may have a far-reaching impact on our privacy. How can these technologies contribute to response, both globally and locally – and what privacy concerns could they raise, both now and in the months to follow? The evolution of IoT and AI has grown to the point where these technologies can now be called on to make a real contribution to responding to a crisis manifesting both globally and locally. Globally, modern analytics can learn about the factors of spread that can help analysts identify where actions need to be taken.

Today, the world is all about industry 4.0 and the technologies brought in by it. From Artificial Intelligence (AI) to Big Data Analytics, all technologies are transforming one or the other industries in some ways. AI-powered Cognitive Computing is one such technology that provides high scale automation with ubiquitous connectivity. More so, it is redefining how IoT technology operates. The need for Cognitive computing in the IoT emerges from the significance of information in present-day business.

Comet provides a meta-machine learning platform, runnable in the cloud or on-premise/VPC, that allows data science teams reproduce full experiments (and not just code), manage users across large distributed data science teams, and provide managers insight into team contributions and performance. This article was written in collaboration with Tyler Folkman, Head of AI at Branded Entertainment Network. To read more of Tyler's writing, check out his Medium Blog. In 1973, at the height of the OPEC oil crisis and skyrocketing fuel prices, NASA scientist and USC professor Jack Nilles began thinking about ways work could be done without the need for commuting. To this day, Nilles remains one of the principal evangelists for remote work as a viable alternative to a traditional office.