Real-time data analytics is still a relatively new concept, but it is changing the logistics of countless businesses across the world. One poll showed that 60% of companies use real-time data analytics for better customer service. However, there are many other applications of real-time analytics.
Data analytics experts must familiarize themselves with the technical aspects of this new field of data science, so they can utilize its full potential. Decisionmakers must consider the applications of real-time analytics to get the most value out of it.
The principles of real-time analytics
Science Soft has a detailed overview of the technology behind real-time data analytics. The tutorial points out that data analytics tools like Citrix monitoring software have the capacity to both push and pull data from their servers. Real-time analytics applications must continually pull data and have algorithms that are capable of processing it quickly.
There are two ways that data can be pulled from the database in real-time. The ideal approach is to stream data because it will be displayed with virtually no time lag. Unfortunately, data streaming is not always feasible. Scalability is usually a significant limitation because there is a bottleneck as data volume increases. Therefore, streaming is typically only a practical solution for real-time analytics applications with a small number of users and data fields.
The majority of real-time analytics applications use processing engines that are instructed to pull new data upon arrival. They are built with algorithms that instruct the processing engine to extract new data in predetermined time intervals. The time intervals will vary depending on the application. Medical monitoring applications require very timely data because patient conditions could critically deteriorate in a matter of minutes. These applications might be programmed to pull data in intervals measured in microseconds. Precision is not as vital with financial applications, but they still need to be administered in a timely manner. These algorithms might be programmed to pull data every several minutes. Other applications with lower levels of urgency could be programmed to pull data every few hours. Science Soft points out that retailers often update their pricing information every hour because the consequences of a 30-minute lag in pricing data are usually inconsequential.
This helps illustrate the confusion many people have about real-time data. Real-time data is actually a surprisingly vague concept because it misleadingly implies that data is being updated instantly. In actuality, it is updated automatically much more quickly than other analytics applications would be, but the updates are certainly not handled instantly.
How real-time data analytics is solving perplexing challenges
Real-time data has helped solve many problems in various industries. Here is a synopsis of some of its most promising applications.
Manufacturing production controls
Some of the most impactful developments in big data have been in the manufacturing sector. Big data has led to the implementation of real-time production controls and analyses.
An IEEE conference in Seoul, South Korea covered this application in 2012. Presenters pointed out that there are numerous applications of real-time data in the manufacturing sector, especially with Bernoulli machines. New advances are still on the horizon, which will increase output and minimize product effects.
Improving financial arbitrage models
The financial management sector has been highly reliant on big data for years. Real-time analytics has opened new doors for financial planners and analysts.
One of the most important benefits is with arbitrage models for investors. Short term investors need to carefully monitor transactions to take it vantage of price fluctuations. Real-time analytics tools give them a huge edge over traders that use data with longer time lags. Network World points out that these models are merging machine learning and real-time analytics to execute trades more rapidly than ever.
IT security
Data breaches are a serious concern for organizations in virtually every sector. The most sophisticated hackers are able to execute their attacks in a matter of minutes. Security administrators have a little time to react.
As the time that it takes to execute a major cyberattack shortens, real-time analytics becomes increasingly important. A growing number of organizations are depending on real-time analytics to safeguard their data. They are going to be even more dependent on real-time data protection solutions as the GDPR and other regulations put more pressure on organizations to prevent cyberattacks.
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