What are Augmented Analytics?

Augmented Analytics is the integration of AI-enabled cognitive services (natural language processing, knowledge engines) to provide a new way to analyze, process, crunch, and derive insights from data.

NLP and Augmented Analytics

The biggest impact augmented analytics has on end users is making sense of unstructured data. We’re collecting a ton of data on a daily basis, but if we aren’t rigorous in how we collect and store it from the get-go, we can easily accumulate a ton of data all over the place. Natural language processing makes sense of that unstructured data, making it organized, queryable, and searchable.

An example of unstructured data would be social media data in relation to a brand. That brand wants to make sense of what people are saying about them, and how they feel about them. NLP can both categorize social media mentions by topic, and analyze things like sentiment to better understand how people feel about those topics. This gives end users a new way to understand all the unstructured data out there.

Business Intelligence and Augmented Analytics

Augmented analytics provides the functionality to analyze structured and unstructured data at scale. It discovers trends, patterns, and answers in massive sets of data. Which means BI companies can offer a whole new “intelligent analytics” offering, where their platform can automatically run these sorts of computations at scale, or allow teams to create their own. Top BI vendors are realizing that only providing functionality for teams to manually query data isn’t good enough. We’re creating insane amounts of data – but data is only as good as how you use it.

Data Literacy and Augmented Analytics

Augmented analytics brings machine intelligence and NLP to data crunching, and is data literacy on steroids. It’s data literacy far surpassing anything a human can handle. It’s how an augmented analytics service can take a giant database of unstructured data, sort it, and make sense of it. If you have 1000 blog posts, to manually process them, categorize them by topic/length/author/etc, match that to performance (views/signups/shares), consistently, that’s impossible for my capabilities of data literacy. For NLP, no problem.

Benefits and Challenges of Augmented Analytics

Benefits include tapping into new sets of data, especially unstructured, and being able to derive valuable insights from it. It can also find new ways to crunch data to derive insights from massive sets of data, and because it’s powered by cognitive services, it can do this at massive scale that a manual process never would be able to. Lastly, augmented data discovery works seamlessly with realtime data, and can process up-to-the-millisecond data on the fly, so insights are derived immediately.

Challenges include high costs and effective training and utilizing of the tools. Processing data at scale can easily run up massive costs. Cognitive services like Watson and Amazon Comprehend are incredibly powerful and easy to integrate through their APIs, but charging by each execution of the service can easily run up a giant monthly bill. It’s important to not run the computation repeatedly on every piece of data, but only on the databases of unstructured data you need to. For training and utilizing the tools, the services are incredibly intelligent, but at the end of the day, it is an AI. You’re responsible for implementing the service and making sure it’s looking at the correct data sets.

One big limitation is expertise in designing and implementing augmented analytics models and algorithms. Though the NLP system does the heavy lifting when it comes to processing and analysis, your model plays a massive role in how relevant the augmented analytics are. As a result, you’ll need a certain level of expertise to build these models – people with a good understanding of both NLP/machine learning services, and data science. Over time, BI vendors with augmented analytics features will make this easier to an everyday user, but for now, getting these services up and running still requires expertise.

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