Data 2020 Outlook Part II: Explainable AI and Multi-model Databases Posted on : Jan 03 - 2020

In Part II of our year-ahead outlook, we explore the sleeper issues that will drive data management and the mainstreaming of AI in analytics.

In the year ahead, we see the cloud, AI, and data management as the megaforces of the data and analytics agenda. And so, picking up where Big on Data bro Andrew Brust left off last week, we're looking at some of the underlying issues that are shaping adoption.

In the world of data and analytics, you can't start a conversation today without bringing in cloud and AI. Yesterday in Part I, we hit the cloud checkbox: we explored how the upcoming generation change in enterprise applications will in turn shift the context of how enterprises are going to be evaluating cloud deployment. Today we turn our attention to the core building block – what's happening in databases, and what we expect to become the sleeper issue this year in AI.

It's now Data, not Big Data

But first some context. Until now, we framed our annual outlooks as being about Big Data because until recently, it was considered exceptional. The definition of Big Data was introduced by Doug Laney, today a principal with Caserta, back when he was with analyst firm Meta Group in 2001. Big Data was novel because processing it was beyond the existing data warehousing technologies and BI analytic tools of the day.

Today, Big Data is just Data because necessity has become the mother of invention. As we'll note below, the database universe has expanded well beyond the core relational model to encompass a wide spectrum of data platforms and types. So, we're now just calling it data, and changing the name of our annual outlook. Of course, we're not the first to make that observation, as Gartner took Big Data off the hype cycle back in 2015.

Now let's get back to our regularly scheduled program.

Getting AI out of the black box

Among the industry observations reported by Andrew last week was the perception that AI has become mainstream in analytics. In fact, analytics is the tip of the iceberg as consumers, machines, and organizations consume services that are powered by AI every day. But as consumption of the results of AI spreads across the services that power the economy, there has been growing concern over the ethics, biases, or other assumptions that can easily skew the algorithms and selection of data that powers AI.

Today, AI is hardly considered smart. While the data sets and models can be complex, the decisions lack human context. AI can make yes/no decisions, detect patterns, and provide predictive or prescriptive recommendations, but for the foreseeable future, unlike humans AI won't be able to learn something in one context and apply it to another. But even making simple decisions, like whether to grant a loan or make recommendations, AI can still cause damage. Former Wall Street quant Cathy O'Neill brought awareness of potential AI bias with her 2016 book Weapons of Math Destruction. View More