Ease of incorporating Big Data sets AI up for rapid growth
Thu 14 Oct 2021 | Richard Ellis
There are few innovations that have received as much hype as artificial intelligence. Cutting-edge AI solutions such as machine learning and natural language processing (NLP) are increasingly found in both multi-national conglomerates and small enterprises, with these technologies benefiting operations.
One of the key issues that has constrained the scope and power of AI tools in the past has been the restrictions brought about by data storage limitations. As the name suggests, Big Data tools require an extremely large amount of data to be used effectively and produce the most impactful insights.
Quickly processing vast amounts of data demands strong computing power, as well as an equally comprehensive and scalable storage system. While text-based data is relatively easy to process in large amounts, unstructured data like images and video is far more resource-intensive and requires much more advanced analysis.
AI has rapidly become a major investment that businesses are making to stay ahead of the innovation curve. According to the latest edition of IDC’s Worldwide Semiannual Artificial Intelligence Tracker, enterprises are forecast to spend close to $342 billion on AI software, hardware, and services this year. The AI market is on track to record extremely strong growth over the next few years with 18.8% growth next year, which will bring the market to the $500 billion mark by 2024.
Training AI models are data-intensive, with the best models needing a great deal of data. Due to the unique and diverse nature of AI workloads, businesses will need to seek out the relevant architecture that works for them, as no one-size-fits-all system exists.
Advanced AI projects that utilise machine learning and neural networks place additional demands on the data storage operations of an enterprise. Not only does this form of project typically require massive amounts of data storage but low latency is also a must.
Depending on the business, a combination of strong performance storage, such as NVMe, could be paired with large capacity solutions including performance spinning disks, as well as cloud storage. No matter what selection is made, the pressures on IT departments are likely to be significant.
Delivering cost-effective, scalable, reliable and powerful data storage is a challenge for even the most well resourced and skilled IT department. By ensuring a solid data platform is in place before wide-scale AI projects are embarked on is essential due to the complex nature of such data warehousing.
There’s no question that the rise of cloud storage will continue to support the use of intensive AI processing, especially for industries with large data volumes. But for some companies, on-premise storage and processing is the most effective approach due to a range of reasons including price and regulatory compliance.
Businesses that are still working with legacy systems, where data can be stored in hard-to-reach silos, face additional challenges in fostering a strong Big Data framework where AI is effectively used. If poor and incomplete data is fed into AI systems, the results will be equally weak.
Data management may not be the most glamorous or high-profile business function but a well-planned data strategy can mean the difference between success and failure when it comes to Big Data.
The advances in Big Data technologies have set the scene for AI to reach its full potential. While finding the right balance between usability, cost and scalability is not easy, ensuring a business has a future-focused plan to deal with the growth of data, as well as how to best process it, is vital.
Written by Richard Ellis Thu 14 Oct 2021
Tags:AI Big Data growth
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