Via LinkedIn : It is now common knowledge that the growth of the Internet of Things (IoT) and the data it brings can offer valuable business insights for the vast majority of industries. However, data analytics and the IoT are continuing to develop at an accelerated pace and, from this point onwards, businesses should be looking at deriving value from all data.
By Andy Leaver, VP International, Hortonworks.
It’s not just the IoT that will provide insights, but rather the Internet of Anything (IoAT) – that is to say, getting value from data extends beyond devices and machines and encapsulates all information produced by sensors, server logs and geo location. In order for this data to be effectively scrutinised, businesses must rely on a dedicated infrastructure able to derive value and tangible benefits.
The IoAT creates a new paradigm that requires fresh thinking and new data management systems, and these solutions are beginning to mature and permeate enterprises. This article originally posted on Data Centre Solutions April edition will provide real life examples of how Hadoop can help businesses gain significant competitive advantage, as well as more accurate and profitable business insights.
Data Crunching Becomes Mission Critical The range of industries experiencing the transformative possibilities of connected data platforms based on technologies like Apache Hadoop and Apache Nifi are vast and include manufacturing, finance, telecommunications, retail, governmental bodies and advertising among many others. Using these platforms to analyse IoT data collected from machines and sensors enables organisations to unlock business benefits across a variety of areas. This could mean anticipating when certain manufacturing repairs will be needed, or when the next cell tower upgrades will be needed.
Furthermore, Hadoop enable connections to be made between a variety of seemingly disparate types of data, providing companies with a previously unattainable level of competitive advantage and transformative innovation. For instance, if it has been an unusually cold or snowy winter, a trucking company may need repairs to their fleet sooner than normal. Additionally, Healthcare organisations have been able to look at different patient statistics to find indicators of disease and medication side-effects that were previously unknown.
A prominent example of a company using Hadoop to aggregate disparate data sets to bring new business insights is the fast-growing mobile marketing company, Billy Mobile. The company offers consumer behaviour insights based on user profiles, behaviours, trends and feedback. To do this, Billy Mobile’s technology analyses tens of millions of records a day to determine the right offer for the right user using semantic analysis and machine learning. Through this it is able to optimise its customer’s ad campaigns and allow them to better understand where to route visitors and offer tailored content and promotions.
Data in Motion and Real-Time Analytics To get maximum benefit from the large and dynamic data stream generated by IoAT. Systems must be able to ingest and process the information within a useful window of time – before its value is lost. With connected data platform’s and integration of Apache Nifi, Storm and Spark, Hadoop offers real-time data processing capabilities and subsequently can give continuous operational visibility and control of data in motion, enabling near real-time decision-making.
One industry in which real-time data analytics is beginning to become a vital aspect of operations is the energy sector. There are various examples, both of traditional energy providers and pioneering new technology companies, that are utilising Hadoop to garner information on the energy grid to optimise its usage. The British energy demand company Open Energi is one of these. Open Energi’s unique demand response technology aggregates energy consumption from across its customers’ sites to provide a solution that instead of adjusting supply up or down to meet demand, adjusts demand up or down to meet supply effectively creating a virtual power plant at a fraction of the traditional costs.
This requires crunching vast quantities of data from a wide range of disparate datasets to build a more accurate picture of energy usage in real time. As the Hadoop platform can accommodate 1 trillion files through the use of an enterprise-class storage processing layer, and data snapshots enabling users to access device information from a specific timeframe after the fact, it is a mission critical element of Open Energi’s engagement with IoAT.
Big Data Means Big Business So, data flows now increasingly originate not within the datacenter but from devices, from sensors and servers on an oil rig in the ocean to a satellite in space, and the value of this monumental body of data is progressively being recognised. Correspondingly enterprises are demanding systems that can effectively handle data in motion and data at rest to deliver profitable business insights.
All of this means that, as a result of the IoAT, adoption of Hadoop has now gone mainstream and big data means big business. Thanks to the increasing prominence of the IoAT, born-on-the-web companies are no longer the sole champions of Hadoop data platforms. Instead the big-beast bricks and mortar enterprises are also adopting and talking about the possibilities the open-source platform provides for mission-critical workloads in far greater numbers.