If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Editor's Note: Countless companies fail to implement data management and advanced analytics properly -- and that's understandable, given the changing data landscape, its complexity, the rapidly increasing amount of data, and the accompanying integration challenges. In this piece from Data Management at Scale, Principal Architect, Piethein Strenholt, provides principles, observations, best practices, and patterns to overcome these challenges. Advanced analytics focuses on projecting future trends, events, and behaviors. It is the most complex form of value creation because it requires statistical models for newer technologies, such as machine learning and artificial intelligence. While it is getting easier to train and develop accurate models, deploying them into production -- especially at scale -- is a major challenge.
Ataccama, a data governance vendor best known for its data quality (DQ) and master data management (MDM) tools for big data, is today announcing a new release of its Ataccama ONE platform that the company says heavily leverages artificial intelligence (AI). In the current enterprise environment, data governance and data management are increasing in importance, visibility and complexity, but they have historically carried a certain baggage of tedium. AI -- and automation in general -- are thus competitive necessities for data governance and data management, as they help customers stay data-driven and regulation-compliant, as well as data leak- and breach- vigilant. The Ataccama ONE platform includes modules for the aforementioned DQ and MDM capabilities (an interface for the former is pictured in the figure at the top of this post), as well as for data catalog, business glossary and business rules management on the governance side. Ataccama likens its AI capabilities to those found in autonomous vehicles and, in fact, use the "self-driving" moniker to describe the functionality.
Artificial intelligence is a vital component of digital transformation initiatives as the volume of data that organizations gather continues to swell. A total of 69% of survey respondents to the 2020 IoT Adoption Survey reported using artificial intelligence (AI)/machine learning (ML) as part of IoT initiatives. Top AI priorities include performance monitoring and trend forecasting in addition to sensor and business data integration. Other popular applications of artificial intelligence in IoT projects include ensuring security, computer vision and product design and testing. Those findings are consistent with other data.
Graph Databases are the (not-so-) new must-have in your tech stack. Over the past few decades, more and more tech giants (Facebook, Twitter, and Google leading the way) have started migrating data into graph databases to not only store and retrieve data in a highly scalable way, but also incorporate models on graph structures to produce highly effective recommendations. What makes graph databases so enticing to these companies with gargantuan data? Relational Database Management Systems (RDBMS's) refers to databases that store their data in a structured format with rows and columns, and have a schema that demonstrates the linkage between different tables within a database. RDBMS's are generally queried with SQL (Structured Query Language) or a similar equivalent, and are great for fairly static (non-changing) and normalized data structures.
BMC, a global leader celebrating 40 years of delivering IT solutions for the Autonomous Digital Enterprise, announced enhancements to the Db2 management solutions as part of the BMC Automated Mainframe Intelligence (AMI) portfolio. The BMC AMI Data Management, Compuware Topaz for Total Test, and ISPW solutions include capabilities that help enterprises protect uptime and availability while intelligently managing their mainframe data and streamlining application development as part of their enterprise DevOps strategies. The BMC AMI Data Management for Db2 solutions powered by intelligent automation provide world-class data management that enable 24/7 availability, foster greater resiliency, and drive the agility needed to provide a transcendent customer experience. "Organizations that rely on Db2 for z/OS require highly available, resilient systems, and the tools to keep them that way. Automation that BMC delivers with its BMC AMI solutions can help to assure that enterprises have optimal access to their vital mainframe data," said Craig S. Mullins, President, Mullins Consulting, Inc.
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.
AI strategy: banks need to have a clear vision on what AI is to achieve; how they want to integrate it within their organization; feasibility and impact of investments and possible consequences on their internal dynamics. Data management: invest in the creation and storage of a large amounts of data to train the AI algorithms. Dividends yielded by AI are related to the quality and the quantity of the data recorded or stored. Internal digitization: digitize processes and operations, promote a pro-technology culture, and familiarize their employees with emerging technologies. It is important to educate them AI will complement and enhance their work and not replace them.
The planet's population is at 7.8 billion and it keeps growing. More and more people work from home. Technologies like the Internet of Things, edge computing, and AI are being adopted at increasingly rapid rates. And demand for consumer endpoint devices is growing. All these factors result in the proliferation of enterprise data.
If Data Science was once the sole space of analysts and data scientists, Augmented Data Science represents the democratized perspective on this area. With Augmented Data Science, the average business user can draw in with cutting-edge analytics tools that take into account Automated Machine Learning (AutoML) and leverage refined analytical techniques and algorithms in a guided environment that utilizes auto-proposals and recommendations to lead users through the unpredictable universe of data science with ease and intuitive tools. As companies are progressively standardizing on augmented analytics, a related model is coming to fruition in the data and analytics market – augmented data management. The innovation is changing the information of the data management landscape and the role of data professionals. Augmented data management utilizes AI and machine learning to make enterprise data management disciplines, for example, information quality and integration, metadata management, master data management, and database management frameworks, "self-arranging and self-tuning," as indicated by Gartner.
Cars, trucks, even ships these days can drive themselves, thanks to a confluence of innovations in artificial intelligence and automation. The truth is, we're already there. As most data managers and scientists know, many of the steps in preparing data can be automated; the challenge often occurs where rubber meets road. Register for this special episode of The Briefing Room to find out! A panel of experts from around the world will share perspectives on where we are, and where the industry is going.