The concept of big data analytics has been around for several years. It has allowed businesses to examine large amounts of historic data in order to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. This process originally took place in a data centre, but now it is often conducted in the cloud.
What’s relatively new is the introduction of massive amounts of machine and sensor data being generated by the Internet of Things (IoT) at the edge of various networks. This has led to the development of edge-based processing facilities, e.g. intelligent gateways that allow data to be processed close to the source, whether that’s a vehicle motor, a mechanical door, or a network router.
Therefore, less data needs to be transmitted to the cloud – this would typically be exception data: measurements that are outside the limits determined by the application, says Bob Emmerson, independent M2M writer and analyst.
It can be useful to divide data analytics into long term analysis employed in a central environment and data analysis performed at a local point, where it is employed in order to generate real-time intelligence on operations in the nearby environment as they occur.
In addition to combining long-term and real-time data, analytics can also enable predictive analysis, which is used to determine patterns and predict future outcomes and trends. Predictive analysis does not tell you what will happen in the future. Rather, it uses statistical models and forecasting techniques to indicate what could happen with a certain probability.
All three models represent a logical development that addresses a generic issue: organisations lack insight into the critical aspects of their business —aspects that are getting increasingly complex in today’s highly competitive marketplace. Data analytics is a factor that will, to a large extent, determine the future growth rate across industries.
So far, so good
I’m a freelance writer, an industry observer: I’m not a data analytics expert and until recently I failed to realise that my focus on the IoT obscured the importance of data coming from traditional information systems such as ERP, CRM, and others. They are of course sources of very useful business intelligence in their own right. Therefore, it follows that comprehensive business analytics should accommodate data coming from both the field and from internal business environments.
Pentaho, which is part of the Hitachi Group, talks about the need for “a highly flexible platform for blending, orchestrating, and analysing data from virtually any source, effectively reaching across system, application and organisational boundaries.” Needless to say the company markets the requisite platform. It allows analysts to engineer, explore and visualise data sets, while managers and executives can leverage the reports and dashboards based on both traditional and emerging data sources in near real-time.
Is this the ultimate IoT prize? Time will tell, but it makes sound business sense.
Pentaho’s Data Integration solution will be demonstrated at CeBIT in Hanover on Hitachi High-Tech’s booth, Hall 13 / E77.
The author of this blog is the independent M2M writer and analyst, Bob Emmerson.
He can be contacted at: email@example.com
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