Real time analytics: why are they important?

Real-time analytics, or Real Time Analytics, is becoming increasingly successful in companies as its potential is transformed into an operational advantage. Transformation that is possible thanks to the progress that in recent years has seen the growth of Big Data first and the tools dedicated to it later.

Today, thanks to both canonical algorithms and machine learning and other branches of artificial intelligence, it is possible to perform analysis and processing with a speed and efficiency that was unthinkable just a few years ago.

Merit also goes to the increasingly available and less expensive computing power, which enables its rapid deployment. A very interesting theory, but how does it translate, in practice, into benefits for companies? Let’s find out some of the main ones.

Today, thanks to both canonical algorithms and machine learning and other branches of artificial intelligence, it is possible to perform analysis and processing with a speed and efficiency that was unthinkable just a few years ago, thanks in part to the increasing availability of computing power at increasingly affordable costs, thus facilitating its deployment.

A very interesting theory, but how does it translate, in practice, into benefits for companies? Let’s find out some of the main benefits.

Putting value on business data

Today, numerous companies, often even in an ill-considered way, collect and accumulate data in real time. For example, in the manufacturing field , most plants and machinery equipped with remote management and control systems possess this capability. Companies, therefore, already have real-time data waiting to be processed to make increasingly informed and data-driven decisions.

This is where the real innovation comes in, namely Real Time Data Analysis, a branch of Big Data Analysis aimed at providing necessary information and processing in such a short timeframe that it is considered, for all intents and purposes, real time, with sufficient time lags to allow its use even in field decisions where timeliness is critical.

Now, following this line of reasoning, companies have both the data and the tools to turn it quickly into useful information-how can it be used?

Real Time Analytics at work: some examples

Good quality data and fast and efficient processing are two of the basic needs for companies that want to adopt the Data Driven approach, in which strategic decisions are based on firm information and not on assumptions or hunches. Real-time analytics make it possible to extend this paradigm to decisions that must be made quickly, in changing contexts. Let’s look at some cases.

Act promptly in case of failure or malfunction

As much as there are already disciplines such as predictive maintenance that can reduce the risk of failure or breakdown, even in the most well-maintained and efficient plant there are always margins of risk. However, every type of machinery or plant gives some signal before ultimate failure. Real time analytics allow these signals to be picked up before they become problematic (as opposed to traditional measurements based on critical thresholds) and to intervene in a timely manner, thus saving the machinery involved from further risk and damage.

Continuously improve products and services

The adoption of Real Time Analytics also demonstrates its value in continuous improvement practices, for example, regarding products and services. Through real-time analysis of feedback and usage data collected from customers, trends, preferences and opportunities for improvement can be detected. This enables rapid and informed design changes, whether aesthetic or functional, meeting consumer expectations ahead of the competition. In addition, integrating this strategy into a continuous improvement cycle fosters an environment of constant innovation, where products and services are improved based on real data.

Identify bottlenecks in processes early and improve them

The use of Real Time Analytics enables companies to constantly monitor workflows and workloads, compare them with the performance of machinery and plants to reveal inefficiencies and delays in a timely manner. Through the analysis of data collected by sensors and integrated systems, overload points or processing steps that slow down the entire production chain can be quickly identified. Once these bottlenecks are identified, action can be taken by modifying production planning.

Security and cybersecurity

Making quick decisions is a prerogative of security-related contexts, both in the physical world and in the cyber context. Fields that are united by a common principle: the only thing better than quick action is quick and optimal action. A result that can be easily achieved with real-time analytics. In addition, real-time analysis systems can act as a first-level filter, causing only truly significant alarms and alerts to reach operators. This advantage is particularly useful in the cybersecurity field, where hundreds or thousands of alerts can occur every day, often resulting from background noise or false positives.

Analysis of customer and user sentiment

Another, higher-level area where Real Time Analytics show their effectiveness is in sentiment analysis or sentiment analysis. Here, advanced machine learning and natural language processing algorithms are leveraged to monitor and analyze opinions and emotions expressed by users in real time, including on platforms such as Twitter, Facebook and Instagram. Through this analysis, companies can identify and respond in a timely manner to changes in public opinion, any critical issues related to current products or campaigns, and any communication crisis. The effectiveness of this approach lies in its ability to transform huge volumes of unstructured data into quantifiable and actionable insights. For example, a real-time sentiment analysis can enable a company to immediately change an advertising campaign that turns out to be poorly received.

Constant improvement with Real Time Analytics

We have mentioned, both in this article and other times on these pages, how today the choice of a Data Driven approach is vital to enable companies to retain their competitiveness. Real Time Analytics, embedded within a well-structured decision chain, allow this concept to be extended to more tactical and immediate decisions, which will also become data-driven. This is a considerable advantage, especially if we consider it in light of today’s market, in which the margins of error are getting smaller and smaller and it is necessary to always operate with maximum efficiency.

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