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Augmented analytics tools, NLP search, graph are trending

Augmented analytics, NLP and graph analytics are changing the data and analytics market, according to Gartner -- enabling users to more easily get at and share data.

Augmented analytics tools, natural language processing, or NLP, search, and graph analytics are the top trends of 2019.

Augmented analytics -- using machine learning and AI in BI tools to automate data preparation and help users discover and share insights -- has been trending over the last year or so, and that trend is accelerating, according to Rita Sallam, a Gartner analyst who co-presented a top 10 data and analytics technology trends session at a recent conference.

Gartner's 2019 data and analytics technology trends list reinforced the significance of trends. These technologies will dramatically affect "how we build and who builds data and analytics content, who has access to insights from advanced analytics, how people interact with data and analytics, and the types of analytics we can do at scale," Sallam said in an interview after the Data & Analytics Summit in Australia in February 2019.

"Augmented analytics continues to move up the hype cycle with earlier adopters realizing benefits from early deployments," Sallam said. "At the same time, more analytics, BI, data science and ML [machine learning] vendors are adding augmented analytics features to their platforms."

Gartner described augmented analytics tools as "the next wave of disruption" in the data and analytics market and advised data leaders to adopt the tools as the platform capabilities continue to mature. By 2020, augmented analytics will be a dominant driver of new purchases of BI and analytics systems, as well as data science and machine learning platforms and embedded analytics tools.

Of today's data and analytics trends, augmented analytics tools will have the biggest impact on enterprises in the long run, said Jim Hare, a Gartner analyst.

"As digital businesses become inundated with data, augmented analytics is crucial for presenting to operational users only what is important for them in the context to act upon at that moment," Hare said. "It also expands who has access to insights from analytics by delivering analytics anywhere and to everyone in the organization, and does so with less time, skill and interpretation bias than current manual approaches."

Meanwhile, Jen Underwood, senior director at machine learning software vendor DataRobot, called augmented analytics tools the most significant recent innovation for data and analytics professionals.

Augmented analytics is crucial for presenting to operational users only what is important for them in the context to act upon at that moment.
Jim HareAnalyst, Gartner

Underwood compared how the technology is transforming modern analytics to the way self-service BI transformed the analytics software industry over the last decade. The biggest improvement that augmented analytics, or "intelligent automation," as she called it, offers is that it enables business users and citizen data scientists to more easily practice analytics, Underwood said.

"Intelligent automation is enabling non-data scientists to use advanced analytics techniques to uncover opportunities from massive data sources, solve more complex problems and better optimize outcomes," Underwood said. "This next wave of analytics -- citizen data science -- is happening much faster than the prior market change."

Gartner predicts that, through 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists. Augmented analytics tools will significantly contribute to that upsurge.

data and analytics trends, analytics predictions, Gartner predictions, natural language processing, augmented analytics, graph analytics

NLP search features proliferate

NLP, often considered a subset of augmented analytics, is another major data and analytics trend. NLP is the ability of a computer program to understand informal human language as it is typed or spoken out loud.

The goal is to make analytics tools as easy as a search interface or a conversation with a virtual assistant. By next year, 50% of analytical queries will be generated by either search, NLP or voice -- or will be automatically generated by software, Gartner said.

The need to make analytics accessible to everyone in the organization is driving adoption of this trend. Gartner predicts that, by 2021, NLP and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users from the business side.

NLP search -- and even natural language generation -- applications have already emerged with early integrations from many BI and analytics vendors, according to Hare.

Some BI and analytics vendors are already integrating NLP search technology, including Tableau, which recently the NLP search feature Ask Data, and Qlik, which acquired AI chatbot CrunchBot to help users get data insights quicker and easier via text query.

The MicroStrategy 2019 BI platform has also partnered with Amazon Alexa and other voice-controlled applications to provide an NLP voice search feature. BI and analytics platform ThoughtSpot also beta-released the SearchIQ tool, which enables both text and voice searches.

Despite its growing adoption, Hare noted NLP technology is not yet ready for out-of-the-box integration.

"Today, [NLP capabilities] commonly require some services effort to set up, as the ontology for conversational analytics is different by industry and organization," Hare said. "That said, [Gartner] expects out-of-the-box and enterprise-ready instances to appear over the next two to five years."

Graph analytics

Graph analytics -- a set of analytic techniques that allows for the exploration and mapping of relationships between data sets like organizations, people and transactions -- is also a notable trend. Gartner predicts that the use of graph processing and graph database management systems will grow at a rate of 100% annually through 2022, continually enabling users to prepare data more quickly and apply more complex and adaptive data science.

However, the need for specialized skills has limited enterprises' adoption of graph analytics to date, according to Gartner. Sallam said skills needed for graph analytics -- beyond knowledge of graph databases -- include the Resource Description Framework, RDF query language SPARQL Protocol and emerging languages such as Apache TinkerPop or the recently open sourced Cypher.

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