The New Fuel is Data, and AI is the Accelerator

We have been hearing these quotes so often, “Data is the new economy”, “Data is the new fuel, and “Data is the new currency”. Our daily lives provide ample evidence of how AI has the potential to impact businesses in every sector. According to data from a recent survey, almost 50% of Enterprise IT organizations are testing, developing, and deploying Artificial Intelligence (AI) based solutions.

Seismic analysis for oil and gas explorations, autonomous vehicles, personalized medicine, banking applications, genomic research, drug development in the healthcare and life sciences, manufacturing, and video surveillance applications are just a few of the sector verticals where AI is or can be used.

If Data is the New Fuel, AI is the Advanced Refinement

Many people do not realize that oil is the backbone for numerous market products (thousands of products). Because it serves as the foundation for so many other products, including fuel, textiles, plastics, aspirin, solar panels, fertilizers, crayons, gum, and so forth, it is extremely valuable. Thus, oil and the expertise of how to refine it served as the foundation for our current physical product production system. Our current system of delivering services will be built on AI and data. How artificial intelligence is enhancing everything with data won’t be self-evident. Most of AI’s benefits will be inconspicuous and enhance everything that we do with data, especially in the areas of prediction, pattern identification, and automation. Oil is a crucial, practical commercial product that, despite what most people don’t instantly realize, makes a lot of things possible.

Business Objectives and Goals of Data

Every business today requires reliable, secure, effective, and affordable data management solutions. The importance of data management strategy is increasing as IT firms depend largely on intangible assets to extract value from their “Data.”

Any enterprise data management ecosystem’s main purpose is to assist people, businesses, and networked devices in making the most use of data. Additionally, that is within the predetermined parameters of rules, compliance, and sector requirements. Businesses can take steps that optimize the benefit to the firm by making data-informed decisions.

The capacity to create new services and products while streamlining processes and improving customer experience is one of the key benefits of data and AI for organizations

Data Lifecycle Management Challenges 

Factors like outdated storage solutions, performance bottlenecks, vendor lock-in from earlier NAS implementations, non-linear scalable systems, ineffective storage data services, a lack of multi-protocol storage support, and proprietary architectures, contribute to the poor data management for AI and analytics workloads.

For any workload including data, artificial intelligence, or analytics, proper planning is essential for operational effectiveness and successful deployment.

The fact that even the quickest GPU and CPU servers wait for data access at exactly the same rate exacerbates performance bottlenecks.

Data Monopolies/Oligopolies

This raises an intriguing problem for rival businesses and new ventures. Will upcoming IT startups stand a chance? These already-established big businesses may be able to establish “data monopolies,” in which the player with the most data in a given market sector may create so many insights from the data that they can support (through new data and profits) company developments that no one else can equal. Additionally, “data oligopolies” may develop, in which a small number of already-existing, massive corporations are given automatic ownership of the future of AI in numerous fields simply because they presently control the majority of the important data (real estate) in existence.

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