Last year saw a significant shift within the big data and analytics marketplace. The wild rush over the preceding two years to put analytics into the hands of everyday business users had resulted in a growing sense of mistrust in the underlying data and by extension any decisions made using that data.

In response, enterprises data and analytics technology providers began to prioritize pragmatism over speculative innovation. Instead of rolling out new data discovery and analysis features, vendors emphasised data integration, governance and management.

But this is about to change as advances in artificial intelligence (AI), and natural language processing (NLP) in particular, have set the stage for a wholesale reinvention of big data and analytics where a word is worth a thousand pictures.

The role of AI in the future of big data for 2019

AI certainly dominated the mainstream press throughout 2018. An influx in AI accelerated hardware, more affordable processing/storage options, and widely accessible development and modeling tools have made AI a mainstream option for enterprise buyers.

However, data and analytics vendors have so far only sparingly put AI to work underneath the covers in augmenting decisions or automating routines such as helping users pick the right data source or the most appropriate chart type.

Building on this foundation, as 2019 plays out, we expect vendors to bring this capacity to the forefront. Moving beyond opportunistic capabilities like suggested datasets, vendors will likely put AI to work, delivering actual business outcomes and even more importantly, closing the skills gap between everyday business users and data professionals.

Such capabilities may come in the form of pre-built data models and algorithms, business taxonomies, or even broader packages aimed at specific vertical or business use cases.

The use of NLP in particular served as a significant point of interest, driving a number of partnerships and acquisitions such as Tableau’s purchase of natural language generation (NLG) player, ClearGraph. To date, most efforts have taken aim at the basics, like translating natural questions into database queries or turning raw query results into meaningful, contextual narratives.

But there’s a lot of ground left to cover. We expect technology providers to focus on emulating (and in some case integrating with) digital assistants like Apple Siri, Google Assistant or Amazon Alexa.

The idea will be to create a conversational, voice driven user experience. Users will begin speaking to the software, asking questions in their native language. And the software will answer those questions using language that’s specific to the business at hand. If the user works in sales, the results will speak the appropriate language, using the lingo and even the definitions specific not just to sales but to the company itself.

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