How to Prep Your IT Infrastucture for AI

By:  |  Category: Blog Thursday, December 14th, 2017  |  No Comments

AI is changing everything. Open any tech website or business journal today, and you will learn about the promise of AI, also known as artificial or augmented intelligence, and how it will transform your business. The truth is: AI will not only transform business, but it will transform technology itself.

Artificial intelligence (AI), machine learning, and deep learning—are the next step after the era of Big Data. Machine learning and AI are the evolution of what was considered revolutionary just a few years ago: assembling, dissecting and making sense of large, previously silo-bound data volumes.

During the first wave of the Big Data, businesses were just getting familiarized with the distribution, variety, and monetary potential of their many silos. The focus was on integration and recognition—or simply understanding what data resided where, and what parts of the organization were accountable for it. This alone is challenging, and it’s still a struggle in companies with large, distributed infrastructures spread around the globe.

The first wave of Big Data ushered in a new host of tools, like Hadoop, predictive analytics, and all the many innovations up and down the stack designed to integrate, centralize, process, store, and analyze Big Data. From 2013 until now, the entire enterprise data stack has been mostly altered, from the hardware infrastructure to the various software underpinnings.

The good news is that there are some radically new elements set to become part of the analytics stack for enterprises, but the requirements aren’t without cost and upgrading legacy systems. The new tools don’t perform well with slower and aging frameworks. The fact is there’s no way to capture the benefits of the new machine learning and AI capabilities without rethinking legacy infrastructure.

Many enterprise IT shops have already done the heavy lifting. This came about because the existing database, processing, and storage engines proved no match for the vast volumes of previously siloed data, and they needed a new set of analytics tools to address that. In many cases, these approaches were designed with familiar analytical methods and interfaces in mind. Some might have made the SQL to NoSQL jump, others adopted Hadoop as the processing and storage framework, and others migrated legacy databases into centralized stores and implemented more complex visualization tools. But no matter which method of beefing up the infrastructure was chosen, if it was in the name of making it more “Big Data ready,” chances are it was “AI ready” as well.

Many analytics vendors are now scrambling to find ways to implement the new wave of AI algorithms into their analytical packages. This is good news for system and IT managers who have already undertaken an extensive overhaul to prepare for Big Data tools and platforms. But there are still some areas that could prove weak points—and some lie at the heart of the enterprise data strategy.

Is it Time to Shore Up Your Infrastructure?

As an example of legacy infrastructure facing new analysis methods, let’s look at a typical data warehouse.

The old and behemoth equipment that keep enterprises tied to the past are often numerous: from applications to storage systems to the system administrators who keep the status quo going because it’s not broken so why spend money to fix it. For many organizations, the data warehouse is one of the factors responsible for maintaining the old guard.

On the other hand, many enterprises di move to modernize their data warehouses to keep up with analytics innovations. They looked to Hadoop, the range of new extract, transform, and load (ETL) tools that were flooding the market, and various other “silo-breaking” technologies. Overall, the data warehouse revisions were just enough to let organizations keep one foot planted firmly in the past, and the other tentatively in the future.

The problem is that the next generation of Big Data analysis and intelligence—the AI and machine learning revolution in the enterprise—will require another rethink of the data warehouse. The good news is that the potential ROI is far easier to trace than it was in the early days of Big Data. Organizations that took significant steps to shore up their data warehouses and other legacy infrastructures to meet the demands of the Big Data era will be better positioned to tackle the AI challenges in the years ahead.

But if you’re stuck with a data warehouse that is more 2010 than 2017, you may have to go back and build a more modern infrastructure before you can consider an AI initiative. And there’s no better time to consider upgrading because AI will transform the way you do business.


source image: https://www.pexels.com/photo/white-laptop-computer-on-white-desk-1329068/

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