How to get the right AI analytics

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Companies of all sizes and in virtually every market are scrambling to increase their analytical capabilities with artificial intelligence (AI) in the hopes of gaining a competitive advantage in a difficult post-pandemic economy.

Much anecdotal evidence points to the ability of AI to improve analytics, but there seems to be less discussion of how it should be implemented in production environments, let alone how organizations should. consider it strategically for the long term.

Start with a plan

AI may be the latest iteration of digital technology, but like its predecessors, it’s not foolproof. More often than not, success depends on deployment and integration into existing environments, not on the technology itself. So, before rushing headlong into the AI ​​tsunami, business executives might want to take a moment to think about how they plan to use it and for what purpose.

According to Content Rules Founder and CEO Val Swisher, AI can be applied to analytics in three ways: as a descriptive, predictive, and prescriptive tool. Descriptive AI is used to describe something that has happened in the past, typically by grouping data into clusters to detect patterns and outliers. This allows companies to answer the question “What happened?” Predictive AI takes descriptive results and attempts to apply them in the future, again using massive data mining and storage. This answers the question “What could happen?” Prescriptive AI then takes all of that data and the resulting analytics to help guide the process toward the desired outcome, answering the question, “What should happen?” “

Depending on your goals, you’ll need to sprinkle your analyzes with different levels of these three flavors of AI. But how is that done and how do you adapt them to production levels quickly and efficiently without losing control?

In a recent article on eWeek, Ryan Grosso, US head of data science at SparkBeyond, offered a number of tips to help “bridge the gap between aspirations and analytical capabilities.” Topping the list is the need to develop internal analytical talents (like human talents) capable of handling the data science tasks required by AI. In addition, you will need to create hybrid teams with expertise in various fields to replace the often siled hierarchies that take root in complex organizations. The key here is to train data scientists and business leaders to speak a common language. Only then should you select and deploy the right AI-based analytics platform, preferably one that can be tailored to your needs rather than requiring changes to your processes. or your business model.

Reading is fundamental

But what exactly should AI do once it’s integrated into the analytics process? What specific functions should it fulfill? One of his key abilities, according to Mahipal Nehra of Decipher Zone, is to read text, a lot of text, and extract meaning from mostly unstructured data. This means that it can provide insight not only on the raw numbers coming from connected devices and monitoring solutions, but also on the equally valuable abundance of communication between employees, customers, partners and other parties. stakeholders. This, in turn, can lead to valuable insights into consumer experiences, brand recognition, and the organization’s overall reputation. And understanding text is part of the roadmap to full speech recognition, which opens up whole new possibilities in areas such as customer relations and self-help applications.

Even for AI, however, the harder it is to collect and analyze all of this data, the more expensive and error-prone the analytics platform will be. That’s why a key part of any AI strategy is getting your data house in order, explain Manveer Sahota and Chris D’Agostino of DataBricks. One way to do this is to combine existing data warehouses and lakes into a unified management system that takes advantage of the scale of the former and the flexibility of the latter. This enables the kind of fine-grained control and governance to maximize data availability for intelligent analytics tools without compromising privacy and security.

It’s also worth noting that deploying AI in analytics is not just a one-time effort. Software deployment and the data it accesses will be ever-changing, growing, and evolving at the speed of modern businesses. Usually the most valuable information gleaned from AI will force you to change what you do and how you do it, and that can be difficult, especially in large organizations. But after all the time, effort, and expense of setting up this smart analytics operation, it would be a shame to ignore what it has to say just to be outdone by a more savvy competitor in AI.

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