Is it 2020 already?


mdm-trends-2013-2015

A recent post on Information Week highlights 10 data and analytics trends that Gartner analyst, Rita Sallam, predicts will expand over the next 3 to 5 years.

These trends fit into three main themes – intelligence, structures and scale – bringing together the potential of big data and machine learning.

Augmented Analytics

Sallam predicts that machine learning and artificial intelligence techniques will increasingly be applied to augment analytics – reducing the need for specialist skills in delivering advanced insights.

Augmented Data Management

Machine learning will increasingly be used to augmant and simplify complex data management tasks such as schema recognition, manage regulatory requirements and manage service levels. Sallam predicts that manual data management tasks will be reduced by as much as 45% through the use of machine learning in these areas.

Natural language processing

Users will increasingly be able to query data sets using natural language questions, rather than having to build queries in programming languages such as SQL.

Graph

Graph technology enables data exploration by revealing relationships between logical concepts in a way similar to how most people think.

The application of graph technology will simplify data preparation and help to link diverse data – enabling more complex and adaptive data science.

Commercial AI will dominate the market

Gartner’s findings are that most enterprises are struggling to scale open source AI and ML pilots into production. By 2022, Gartner believes that 75% of AI solutions will be built with commercial, rather than open source products. Many organisations may augment open source stacks with commercial tools

Data fabric

Where the goals used to be to have all reporting data in a single data warehouse, current trends are for data to become more distributed.

This means that companies will have to invest in custom made data-fabric designs to enable a logical data warehouse across these multiple sources.

Explainable AI

The lack of trust in AI is a serious inhibitor to AI adoption for many corporates.

Gartner predicts that AI behaviour, forensic, privacy and customer trust specialists will be employed by 75% of corporates to reduce brand and reputational risk.

Blockchain

Blockchain is used in data and analytics to ensure that changes to data can be tracked – for example to track things like fake news.

Continuous intelligence

Continuous intelligence is about enabling smarter decisions through real-time data and advanced analytics.  It incorporates situation awareness and prescribes the action to take. It is intelligent, automated, and outcome-focused, according to Sallam.

Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Persistent memory servers

Last, but not least, persistent memory servers will begin to change the way that in-memory analytics take place. These servers enable larger memory, affordable performance, and less complex availability and some database vendors are rewriting their systems to support them

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