To master digital transformation in your business and put data-driven business models into practice, a digital mindset and comprehensive empowerment originating with corporate management is required.
By Trond Bjerkvold.
For a long time the topic of artificial intelligence (AI) has been moving minds as it inspires fantasies and stokes fears. The step from machine intelligence to notably AI has caused the first entertaining and practical applications. This year, AI is starting to make an impact in entire companies, representing another significant leap: from individual to company-wide use.
The targeted, quick use of data in large volumes from various sources – Big Data – is becoming ever more commonplace within companies. As is the protection and security of this data, of course. But a crucial component has to be added, which addresses the responsibility of individuals and companies when dealing with data: data ethics.
Big Data and Cloud are central to pretty much all the technology topics of our time. From Industrial Internet and the Internet of Things to machine learning and deep learning to artificial and business intelligence – and beyond. Seems clear so far, right? To date, however, there have been few answers to the question of how everything will proceed. Answers are now due. It’s time for piecing together the bigger picture, which we will be doing in a small blog post series. Let’s start with Data Thinking.
The first Applied Artificial Intelligence Conference (AAIC) took place in Vienna at the end of May. Solution developers, prospects, and users from different industries came together to exchange views on the application of Artificial Intelligence (AI). Unbelievable Machine was present as partner and exhibitor. The *um Data Scientists Ingo Nader and Clemens Zauchner explain our contribution to the understanding and applicability of the technology.
Against the backdrop of new technologies and the ever-more demanding requirements of customers and/or employees, IT leaders in companies are constantly faced with new challenges.
For the reliable operation and maintenance of technical equipment, machines and elements, regular spot checks and inspections were carried out in analogue times. Replacement or repair in case of unforeseen downtime. In the age of data analysis, machines automatically report when control power decreases or when they need maintenance. Thanks to this practical foresight, problems can be solved before they even occur.
Statistical models are driving today’s wave of artificial intelligence. But this second AI wave creates its own decision models which are pretty much black boxes. So, what’s up for the third wave? Transparent tools for solving real world problems.
Big data swamp? That is what you get, unless you work structured, collect the right metadata and prepare documentation (yes) for your big data lakes.
“We prefer data analytic platform to data lakes. The data analytic platform is your one point of data for all different data sources in your company,” says Ingo Steins, deputy director of operations in The Unbelievable Machine Company (*UM), a part of the Basefarm Group.