Effective data management is crucial for organizations of all sizes and in all industries because it helps ensure the accuracy, security, and accessibility of data, which is essential for making good decisions and operating efficiently. Properly organizing and maintaining your data can help ensure that it is accurate and up to date. This is important because inaccurate data can lead to incorrect conclusions and poor decision-making. Well-managed data is easier to access and use, which can help you save time and reduce the risk of errors. In some cases, proper data management is required by law, such as the General Data Protection Regulation (GDPR) in the European Union. Database management system vendors are now deploying artificial intelligence, particularly machine learning, into the database itself.

The gap between leaders versus laggards in AI has widened significantly in the last 6 months, even as leaders are investing big time on pilot projects to transform business teams with AI and Deep Learning. In a powerful survey finding, market research firm ESI ThoughtLab has found out APAC region leads (14.1 Billion USD) in average revenue earned through the adoption of AI applications in 2020. North America ($13.9 billion) and EU ($12.7 Billion) have also reported significant revenue growth from AI adoption. Laggards in AI can drive home success with AI investments by developing a culture of learning and sharing knowledge. ESI ThoughtLab reports AI leaders are constantly amplifying their data science talent pool by acquiring AI businesses.

Most Artificial Intelligence (AI) applications are about having the right data at the right time, but also about being able to process them in intelligent ways. The global leaders on AI are already collecting, aggregating, processing and managing very large volume of data. In the future, a proliferating number of enterprises will have to manage very large datasets, as a means of empowering their AI-based processes. This has already a significant impact on the databases of these organizations, which must be more scalable and more intelligent than ever before. However, the relationship between AI systems and modern databases is a two-way one. On the one hand, the quality of the data management infrastructure of an enterprise is a decisive factor for its ability to adopt and fully leverage AI.

Swedish shipping company Stena Line is expanding the reach of artificial intelligence (AI) as a fuel-efficiency and cost-saving tool within the transport group's shipping fleet. Initial pilot tests have produced promising results, indicating that AI/digital technologies have the potential to revolutionise how ships operate. AI-supported automation trials have delivered cost savings and ship operational efficiencies. Stena plans to expand the use of AI to cut fuel costs on its vessels, and digitisation to enhance productivity across the group. There is an appetite for this in the Nordic region.

The world is in the era of AI, and we are quickly moving toward the age of intelligence. Organizations across the globe have realized the potential hosted by AI, and how leveraging that potential can help them in extracting the best possible results. We are seeing a massive increase in the investment companies across the globe are making in AI. AI is being called a game-changer in this regard, as companies look to improve management of resources and extraction of intelligence through the use of the mechanism that is AI. Businesses want to implement AI for improving their competitive advantage over others around them, and to improve the current business model they have in place.

Machine Learning algorithms can predict patterns based on previous experiences. The overarching practice of Machine Learning includes both robotics (dealing with the real world) and the processing of data (the computer's equivalent of thinking). These algorithms find predictable, repeatable patterns that can be applied to eCommerce, Data Management, and new technologies such as driverless cars. The full impact of Machine Learning is just starting to be felt, and may significantly alter the way products are created, and the way people earn a living. Machine Learning algorithms are trained with large amounts of data, allowing the "robot" to learn and anticipate problems and patterns.

The case for learned index structures Kraska et al., arXiv Dec. 2017 Welcome to another year of papers on The Morning Paper. With the rate of progress in our field at the moment, I can't wait to see what 2018 has in store for us! Two years ago, I started 2016 with a series of papers from the'Techniques everyone should know' chapter of the newly revised'Readings in Database Systems.' So much can happen in two years! I hope it doesn't take another ten years for us to reach the sixth edition of the'Red Book,' but if it does, in today's paper choice Kraska et al., are making a strong case for the inclusion of applied machine learning in a future list of essential techniques for database systems.

Tim Kraska will speak on "Learned Index Structures", at the AI Conference in New York, April 29 to May 2. Hurry--best price ends February 2. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Tim Kraska, associate professor of computer science at MIT. To take advantage of big data, we need scalable, fast, and efficient data management systems. Database administrators and users often find themselves tasked with building index structures ("indexes" in database parlance), which are needed to speed up data access.

Yet, with so many connected devices and widely available sensor technologies, access to data isn't the problem. Indeed, most companies are swimming in data. The real challenge is managing all that data and putting it to use. But once the hard part's figured out, companies are able to improve products, services and their business operations by leaps and bounds. As we learn in our October feature story, inexpensive sensors and internet of things connectivity capable of collecting big data have improved supply chain visibility.

I chose this book after looking at a number of options. The text is clearly written for individuals with an bachelor-level education in computer science. The author prefers pseudocode and text explanations of algorithms to equations, and when he does use equations they use clear, commonly understandable notation rather than the terse greek alphabet soup preferred by many of the more mathematically oriented authors. It should be pointed out that about 10% of the text of this book is devoted simply as a user manual for an open source MLA package called Weka. When I first realized this I almost flipped; I really didn't want a book that was devoted to gaining a surface understanding of a particular implementation of a set of algorithms.