Streaming analytics is a real-time big data flow analysis, a sequence of activated operations following the collection of data generated by predetermined and observed events.
Streaming analytics tools allow us to study in real-time a huge amount of data traffic generated by different and heterogeneous sources, i.e., Big Data. The ultimate goal is always to obtain useful information for any sector organization to identify opportunities and risks when making a decision. If we wanted to explain this term Streaming Analytics with very few words, we could say that it analyzes real-time big data streams.
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A data stream or “Datastream” is triggered following a specific event resulting from an action or series of actions. The data generated by continuous real-time queries can come from mobile devices, transactions on applications, interactions on websites, interactions on social media, machinery sensors, and in general from any device of the Internet of Things. In practice, any measurable activity can trigger an event and help create a flow of data that is worth analyzing.
Given the large amount of data that we could find ourselves observing, especially if we observe the IoT flows, the analysis takes place utilizing software programmed to keep track of the variations of trends and variations of these data through a tracking operation. After storage, they can process the collected elements and provide customized reports for the reference sector or the organization that requested them.
Generally, we could imagine streaming analytics as a sequence of operations, which is activated following the collection of data generated by predetermined and observed events. As the data flow is generated, the monitored data is extracted from the context, processed, and prepared to be first stored and then organized in the format requested by the user, which is usually also called the “presentation phase.” Finally, from the information obtained, the user takes a decision, that is, an action, which may consist of implementing a new strategy or a variation of the already existing one.
Both streaming analytics and traditional analytics have a common element of collecting data and then processing it, but between the two, there is a subtle and substantial difference. The analytics streaming works with data in real-time, while the other works on historical data previously collected and stored over time. Furthermore, while the first performs only simple operations on data flows, precisely due to the speed of generation, the second performs more in-depth and complex analyses, having more time at its disposal for the production and presentation of insights.
On the other hand, the result is common to both. It is to present the user with a usable result with updated information while also ensuring that the data status is updated.
The main utility of preferring streaming analytics to a traditional one lies in the fact that every second, thousands and thousand of bytes of data are generated, which, if not constantly processed, could cause a loss of information useful to an organization for its optimal present management, but above all future.
Just think that “taking all the sources of data generation together, there are 2.5 quintillion bytes of data created every day” and that “every minute, 456,000 tweets, 510,000 comments and 293,000 status updates on Facebook are published on Twitter . During this one minute, 156 million emails are sent,” as reported in the IBM special report “Streaming Analytics: how to realize its full potential.”
If used well, streaming analytics is a powerful tool. While it improves operational efficiency, it can reduce costs and quickly provide insights and actions to transform any information obtained into a new opportunity for a company.
As is well known, whatever the reference market, it is always arriving first to make a difference, just as in a production chain, the difference is the prevention of faults and the management of the same, constantly analyzing the data sent by the various sensors. These are just some of the main benefits we could list. Other examples of use are:
Keeping track in real-time of the routine operations of the company or of the cycle productive by monitoring the productions.
Furthermore, with this type of analysis, it is possible always to know what is happening daily and even at every moment, with updated company statistics. In general, it allows us to have an eye on all the main company information, manage our performance indexes with ever shorter intervals, and quickly carry out benchmarks to evaluate the effectiveness of a decision or a production process.
For accurate streaming analytics, it is necessary to resort to fast and reliable software and, above all, capable of processing large amounts of data. Another essential operation they perform is the storage in a database of the information obtained and their sharing and integration with other applications used by the company.
Among the leading software, we find those of large IT companies. Still, many others are available, such as IBM Streaming Analytics, Microsoft’s Azure Stream Analysis, Capterra’s Apama Streaming Analytics, SAS Analytics for IoT, TIBCO Software.
These are just some of the tools available to analysis experts to process the data collected by events as quickly as possible to have useful information in real-time, but also to verify insights on basic data such as: demographic, geographical, permanence display media, type of device used or evaluate the user experience.
Closely related to the use of this software, new professionals are increasingly required to set them up correctly, such as senior or big junior data streaming analytics.
A company, making use of streaming analytics, will be able to have the data at its disposal very clear, updated in real-time, highlighting hidden correlations also through comparisons between different statistics, which will be useful in the decision-making phase to increase turnover and at the same time, minimize the risks associated with the normal conduct of its business and the new strategy undertaken.
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