Artificial Intelligence and machine learning are the future of every industry, especially data and analytics. In Growing Up with AI, we help you keep up with all the ways this pioneering technology is changing the world.

Use AI to tackle huge datasets

Let’s talk about AI and machine learning (ML). Does your company actually have the tools and processes to use these innovations? Or do you just say that you have them? “AI-washing” is a disturbing trend among tech companies today, where they (correctly) ascertain that AI will be the deciding factor in the next generation of technologies, but don’t actually have the AI horsepower to back up their product, service, or claims. They slap a term like “AI-powered” or “AI-enhanced” to their offering and hope it’s enough to keep customers engaged.

Redefining Big Data Analytics with AI

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“AI-washing” is a disturbing trend among tech companies today, where they slap AI language onto their product or service, but don’t actually have the AI horsepower to back up their claims.

However, half-measures just won’t cut it when it comes to handling huge datasets. Data is growing at a phenomenal rate and that’s not going to stop anytime soon. Many companies are already sitting on datasets so massive that it’s not possible for humans to deal with them unaided.

AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. This is both a daunting challenge and an inspiring opportunity, since effective use of AI and ML can cut through about 80% of data preparation (the annoying, routine stuff), leaving humans to handle the remaining 20%, the actual modeling and optimization.

Overcoming the obstacles between you and revenue

Once your data is prepared for analysis, the next question is: how else can AI help you? There’s a belief held by many in the AI and analytics worlds that the Holy Grail of data strategy is the ability to turn every question the business could ask (strategic, tactical, and operational) into requirements that an AI system can understand. Then the AI would go into the data, find the respective answers, and serve them up in easy-to-digest language that even a nontechnical team member can handle.

This simple goal of “business questions to ML” would massively alter the business world, allowing users of any technical skill level to effortlessly draw deep insights out of their data. However, this sweeping paradigm shift will require the work of countless AI experts to tackle challenges that the industry simply hasn’t figured out yet.

There just aren’t enough AI and data science practitioners to go around to tackle this lofty goal. A recent Gartner report estimates that “by 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.” Apply that metric to any other business-critical function. Can you imagine if 50% of companies didn’t have coders or sales reps or any of the other vital roles that keep the lights on? It’d be considered a crisis and an emergency. That’s the state of AI. Implementation of AI elements is expected to almost triple in the US while the overall supply of AI experts will stay significantly lower than demand.

Speaking of the demand, there’s another hurdle to AI success in the short-term. Business teams are looking for very specific tactical insights from their AI utilities (profitability, sustainability, brand, customers, etc.) compared to the Data Science/AI teams, which are more interested in strategic improvements including Natural Language Processing, computer vision, optical character recognition, and neural nets.

Further complicating matters, research and development groups focus on bread-and-butter issues like scalability, security, reliability, and performance. Communication between these disparate teams, each with their own goals and desires, means that often novel AI solutions and applications are created and abandoned because stakeholders elsewhere in the company don’t understand the value they produce.

Get money out of your data

So where does this leave companies that want to survive and thrive in the coming AI apocalypse? 

Most existing business models are somewhere in the “diminishing returns” region of the economic value curve (investing more resources while revenue stays flat). More and more funds are poured into R&D with no additional associated revenue coming in. The right data and analytics platform can help you bridge the gap between your current AI and analytics paradigm and where you want your company to be in the future. 

Your platform should be able to effortlessly connect to numerous large datasets: cloud-native data warehouses and data lakes with millions or billions of rows. It should also be able to handle live datasets that constantly update and allow you to mash them up with cached data. If the system also allows for in-database preparation and materialized views that can be optimized for faster queries and lay the foundation for advanced analytics with Python and R, even better. All these utilities put powerful tools into the hands of data engineers to perform more complex analysis and use ML on huge datasets.

Whatever you want to do with AI and ML in the long term, the right analytics platform will be key to building it. It’s also a better way to monetize your data in the short term. It’s a win-win that more and more companies will rush to embrace as they undergo digital transformations and cloud migrations. It’s the future.

Redefining Big Data Analytics with AI

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Inna Tokarev-Sela, Sisense’s Head of AI, has over 15 years’ experience in the tech industry. She spent the last decade at SAP, driving innovations in cloud architecture, in-memory products, and Machine Learning video analytics. A frequent speaker at industry events like IBC, NAB, WonderlandAI, and Media Festival, Inna holds a BS in Physics and Computer Science, an MBA, and an MS in Information Systems, having written her thesis on Neural Networks.

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