As AI shifts from leveraging information provided by humans to making decisions on their behalf, tech leaders must weave an intelligent data fabric to unlock the full potential of agentic AI while shoring up enterprise-wide resilience. Digital resilience--the ability to prevent, withstand, and recover from digital disruptions--has long been a strategic priority for enterprises. With the rise of agentic AI, the urgency for robust resilience is greater than ever. Agentic AI represents a new generation of autonomous systems capable of proactive planning, reasoning, and executing tasks with minimal human intervention. As these systems shift from experimental pilots to core elements of business operations, they offer new opportunities but also introduce new challenges when it comes to ensuring digital resilience. That's because the autonomy, speed, and scale at which agentic AI operates can amplify the impact of even minor data inconsistencies, fragmentation, or security gaps.

There are many benefits of GPUs in scaling AI, ranging from faster model training to GPU-accelerated fraud detection. While planning AI models and deployed apps, scalability challenges--especially performance and storage--must be accounted for. Of these elements, data storage is often the most neglected during the planning process. Because data storage needs, over time, are not always considered while creating and deploying an AI solution. Most requirements for an AI deployment are quickly confirmed through a POC or test environment.

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.

Post-pandemic, the demand for AI is surging, as many organizations ascertain the need for AI to keep pace with the current business landscape in the face of a looming recession. AI can help enterprises improve business processes, increase speed and accuracy, and help make predictions to optimize their performance. In 2023, there will be many ways that enterprises can implement AI but for more traditional organizations, we suggest the following trends will play an important role. This includes the need for companies to get their data fabric in place before implementing AI, new and interesting ways to "white-label" AI, and the need to develop a Center of Excellence to ensure the entire company is aligned with an AI strategy. As more enterprises look to implement AI projects in 2023 to increase productivity, gain better insights, and have the ability to make more accurate predictions regarding strategic business decisions, the challenge will be for traditional enterprises to establish a robust data framework that will allow their organizations to leverage data effectively for AI purposes.

Big Data and AI are, perhaps, the most important business technologies of the century, and they are intrinsically related. Every year, the use of AI algorithms and information sets grows and improves. Because of this, businesses become faster and more effective, and the public gets what they want quicker and more often. But what is the state of AI and Big Data, right now? In this article, we take a snapshot look at the world of information processing as it stands in the present.

Making use of artificial intelligence takes more than just buying the technology and flipping the "on" switch. Companies need to understand the goals they want to accomplish -- and ensure that they have the right data to get there. "You can't expect the AI to come up with the solution for you," said Phil Crawford, Chief Technology Officer at Nashville-based CKE Restaurants. "You really have to think about your end goal. Are you trying to achieve speed? CKE operates thousands of Carl's Jr. and Hardee's restaurants around the world and wanted to use artificial intelligence to help with drive-through automation. That goal required the aggregation of different kinds of data from different sources, including drive-through timers, personnel information, sales data, and audio from the drive-through speakers. As a result, the first step the company took was to create a data lake to aggregate the data sources. CKE opted for Snowflake as their platform and began implementing it in the last quarter of 2021. "We couldn't skip it," said Crawford. "There's no other way to do it.

The surge in digital transformation initiatives across businesses and the heightened need for real-time insights has led to an explosion in data creation. But few organisations have a proper understanding of where all their data exists in the first place. Every company has different siloed data sets running on-premises and across multiple public and private clouds and various servers. A recent global survey commissioned by IBM with Morning Consult found 9 out of 10 IT professionals in India reporting that their company draws from 20 or more different data sources to inform its AI, BI, and analytics systems. "This has led to data silos and complexity and as a result most data remains unanalysed, inaccessible or untrusted," says Siddhesh Naik, Data, AI & Automation sales leader, IBM Technology Sales, IBM India/South Asia.

Digital experiences are constantly being pushed to new limits by new technology and market shifts. Businesses can't just set up data analytics once and forget about them. They must be adaptable and rely on real-time data and insights to keep up. Startups, SMEs, and large corporations are increasingly turning to data analytics to cut costs, improve customer experience, optimize existing processes, and achieve better-targeted marketing. Besides these, many businesses are interested in Big Data because of its ability to enhance data security.

Vendors, consultants, and their clients have been talking in data fabric terms for close to a decade now, if not longer. If "big data" was the problem to solve, then a data fabric suggested a ready solution. John Mashey, then chief scientist at Silicon Graphics, used the term "big data" to describe the wave of large, less structured datasets and its impact on infrastructure in a slide deck in 1998. Apache Hadoop gained popularity after an engineer at the New York Times wrote a blog post in 2009 about automating a PDF integration task using Hadoop. The term "data lake" came into vogue in the early 2010s to describe an informal means of making data of various kinds accessible to analyst teams.

Data management agility has become of key importance to organizations as the amount and complexity of data continues to increase, along with the desire to avoid creating new data silos. The concept of creating a'data fabric' as an agile design concept has been proposed by leading analysts, such as Mark Beyer, Distinguished VP Analyst at Gartner. "The emerging design concept called'data fabric' can be a robust solution to ever present-day management challenges, such as the high-cost and low-value of data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing, and more," says Mark Beyer. As a data fabric readily connects and provides singular access to all data sources distributed throughout the enterprise, semantic knowledge graphs provide the foundation that makes this design possible. Semantic knowledge graphs and aspects of AI are necessary for the data fabric architecture to work.