IoT data and analytics: a new landscape emerges

15 August 2016
Report type: Strategy Report
Author(s): Emil Berthelsen
Keywords: M2M, IoT, data, analytics, ACID, Big Data, Fast Data, Hadoop, MPP, NoSQL, NewSQL, predictive and prescriptive analytics, artificial intelligence, machine learning, open source, Subnets of Things, Domains of Influence, Event Horizons, first receiver
Companies: Cloudera, Splunk, Sight Machine, GeoSpock, Exasol, SAP HANA, IBM Bluemix, AWS, Microsoft Azure, Oracle, SAS, Infobright, MongoDB, Cassandra, Couchbase,, Skytree, Sight Machine, Glassbeam, MapR, Tableau, D3.js, ChartBlocks, Google Charts, VoltDB
Number of Pages: 47

The growth of the Internet of Things is intimately linked with the data produced by the vast array of diverse connected devices. Data, data management and analytics in the age of the Internet of Things pose fundamentally new challenges to the capabilities of existing data management and processing tools, technologies, and approaches. This report examines those challenges and provides Machina Research's views on how they can best be addressed.

In section four, we will be exploring how the scale, speed, structure, and context of the data have become key underlying factors in this challenge but also how other aspects such as changing and dynamic requirements in data querying and use add to the complexity.

In section five, we look at how these challenges impact data management and processing tools, technologies, and approaches. For the purposes of this report, Machina Research will analyze the impact in terms of the 5S Big Data model as well has exploring enabling technologies into the five functional areas of data ingestion, data storage, data processing (analytics), data augmentation and aggregation and data visualization. Each will address the changes in turn. Also in section five, we identify some of the players in the different technology spaces.

In section six, the report explores the impact these tectonic plate shifts have had on enterprises in for example having to augment existing and expensive legacy RDBMS systems with either NoSQL or NewSQL options, and having to shift focus from analytics as an afterthought to a step carried out as an integral part of the business process (combination of applications and analytics).

The future of IoT data and analytics will be a very interesting one as it in many ways returns the field to the capabilities of artificial intelligence and how machines learn, and also brings about a discussion as to the role of humans in these automated decision-making frameworks. Section seven will discuss some of the perspectives and others including data ownership and governance, and provide some guiding thoughts.

Section eight shares Machina Research’s key conclusions and recommendations.

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