What is the difference between Operational Intelligence (OI) and Business Intelligence (BI)?

Understanding the differences between operational intelligence (OI) and business intelligence (BI) is crucial to contextualizing and taking action on the information and insights provided by your analytics toolset. While both operational and business intelligence are used to drive action and inform decision making, there are key differences that distinguish these two areas of analysis.

Business intelligence maintains a relatively narrow focus with an emphasis on finding efficiencies that optimize revenue or profitability. BI typically means taking a snapshot of data over a defined period of time in the past and reviewing it to understand how the organization might achieve better success in the future.

In contrast, operational intelligence focuses on systems, rather than profits. OI uses real-time data collection and analysis to reveal trends or problems that could affect the operation of IT systems and to help front-line workers make the best decisions about how to address those problems.

The differences between operational intelligence and business intelligence can be summarized as follows:

Business intelligence focuses on finding efficiencies that increase or protect profits, while operational intelligence focuses on maintaining the health of IT systems.

Business intelligence leverages more historical data, while operational intelligence relies on real-time data collection and analysis. Operational intelligence has been described as immediate business intelligence gained from ongoing operational functions, a definition that speaks to the real-time nature of data collection and focuses on the operational functions that characterize operational intelligence in an enterprise environment. While business intelligence typically runs within a specific data silo, operational intelligence helps organizations break down data silos to uncover trends and patterns of activity within complex and disparate systems.

From colorizing old photos to becoming more efficient with Deep Learning

The technology key in improving the present future

The ability to synthesize sensory data, while preserving the desired statistical properties, is currently proving to be a great success in different industries.

Many examples are based on this concept and apply it to various industries using technology. One of the most prominent is the case of DeOldify, an artificial intelligence program that translates black and white images into color, or Nvidia, with its proposal to create realistic images of fake landscapes or non-existent people’s faces, from semantic sketches.

GANs: The most interesting idea in Machine Learning of the last decade

These systems are based on a very specific neural network architecture, called Generative Adversarial Network (GAN), an artificial intelligence algorithm based on the fact that synthesized data must maintain both statistical properties and be indistinguishable from real data, a process similar to a Touring test for data.

Such concepts have their origins in the past, where the comparison was made through simple visual inspection. Nowadays, classification models, called discriminators, are used to distinguish between synthesized data and real data. In a more intuitive way, this network can be understood as two competing networks: the first one is in charge of generating candidates, synthesized data, while the second one evaluates whether the data is real or synthesized. The goal of training is to increase the error rate of the discriminating network, i.e. to deceive the discriminator.

Monitoring, deep learning and its business benefits

The solutions described above are adapted to various sectors such as “Smart”, “Industry 4.0” and “Energy” among others. Real-time asset monitoring software is starting to use these technological advances to solve common problems, such as, for example, connection failures. It often happens that some sensors sending data are partially disconnected, which could have been avoided if a generator-discriminator model had entered the game, replacing the missing data with synthetic data. From here we could consider the possibility of replacing some sensors completely with synthetic parts, which would ensure the highest possible quality and reduce the hardware infrastructure required for effective monitoring.

Currently, Spanish companies such as CIC Consulting Informático, with its asset monitoring product IDboxRT Inteligencia Operacional, consider Deep Learning as a tool to make their customers’ lives easier.

Example of predictive visualization of the value that each variable will have in the next 15-minute period.
Example of predictive visualization of the value that each variable will have in the next 15-minute period.

The series of measures in the field of Deep Learning in monitoring, developed by CIC Consulting Informático, leads to significant positive results at several levels. First of all, it provides favorable economic conditions, allowing savings in the operation and maintenance of specific equipment, while avoiding serious losses of information. For this reason, there are advantages associated with energy efficiency, such as the reduction in energy consumption resulting from the reduction in the number of physical components.

Back to the future with deep learning

Deep Learning is expected to have a revolutionary effect on the way companies operate in the near future, making them more efficient in terms of consumption and profitability, optimizing all their processes and achieving tangible results on a global scale.

Industrial IoT: at the service of ideas

This is nothing new. We are not talking about a breakthrough technology that will appear with force in 2020. The IoT is something that has been on the minds of its precursors for many years, since the 1990s, simply waiting for communications and systems integration techniques to support their ideas.

And it is precisely this last word that is the key to understanding the reasons why its adoption in the industrial field has not been as rapid as predicted by the major global consulting firms. The truth is that we are not talking about a technology in itself, whose mere application is capable of solving a problem, but about a concept of application of several technologies in the service of a basic premise: an idea.

During the first years of the IoT boom, there have been situations in which medium-sized and large companies did not take steps, but real leaps towards the “application” of IoT in industrial environments. The problem is that these leaps were simply based on investing heavily in IoT devices, whose data turned out to be anecdotal or ancillary.

From the experience accumulated by our IDboxRT team, we subscribe to the maxim “What can’t be measured can’t be improved”. But the hype that IIoT is experiencing should not drag us into an unjustified eagerness to collect useless data, the counterpart to this maxim is that we should only and exclusively measure those parameters that allow us to achieve the expected ROI.

On this solid basis, a tide of device manufacturers, models, protocols, etc. appears before the leaders of these initiatives. In this sense, it is difficult to predict which of them will dominate the market in the medium term, so it is essential to have an open IoT platform that allows communication with a variety of devices in a simple way, as this greatly facilitates the choice of the right device for each use, without fear that certain data will remain isolated as the technology evolves.

At the level of communication protocols, we find a variety of lightweight protocols that allow communication with remote devices powered by batteries, whose duration can reach several years depending on the protocol used and the refresh rate. From the well-known MQTT, through CoAP, to other less recognizable protocols such as BACnet, we will find a multitude of protocols implemented by different devices, which may create doubts in those who have data processing platforms with low flexibility.

It is precisely this open nature what makes the Operational Intelligence tool that we developed at CIC Consulting Informático de Cantabria, IDboxRT, appear as one of the references in Gartner’s 2018 Competitve Landscape: IoT Platform Vendors. Without being able to be considered solely an IoT platform, the ability to ingest data from any device, regardless of the protocol, makes IDbox one of the safest bets in this regard, since whatever direction the industry takes, our customers will always be able to integrate their data, combining different protocols.

The possibility of combining data from our “IoT park” with process data directly collected from PLCs, SCADAs, databases or even third party WebServices allows IDboxRT customers to contextualize the information, implement mathematical models, combining all this data, and analyze the results to improve decision making in real time.

In short, from the IDboxRT team, we are sure that the implementation of IoT initiatives in the industrial field will undoubtedly bring substantial improvements both in terms of control and process optimization, as long as we focus on the value that each piece of data can bring to the heart of any initiative: an idea.

BIG DATA and OPERATIONAL INTELLIGENCE: a connection for life

The data generated by the machine makes Big Data analysis really interesting. Among other things, they allow you to improve the user experience, increase IT stability, detect security threats and even analyze customer behavior. But for that, the information must first be found and reviewed.

What is operational intelligence (OI)?

OI can be defined as a form of real-time business analysis that offers actionable visibility and an insight and management of all business operations.

The data produced by real-time operational intelligence enables operators to understand the performance of distributed infrastructure, make predictions, improve efficiency, and even prevent disasters. This gives a greater ability to make the right operational decisions and engage important stakeholders. Importantly, IO software learns from past actions through artificial intelligence (specifically, machine learning) and can therefore improve its own decision-making processes.

Operational intelligence, along with machine-generated data, provides the ability to understand exactly what is happening in individual systems in the IT infrastructure, in real time.

Therefore, modern Big Data platforms for operational intelligence are capable of processing more than 100 terabytes of machine data every day, which then serves as a prerequisite for making informed decisions in different business processes. Product managers can bring applications and services to market faster, managers improve the availability and performance of internal IT solutions, while sales teams have the ability to tailor services and products to customer behavior. The opportunities for business process optimization are endless.

How operational intelligence works

Operational intelligence plays directly with IT itself: A large amount of information is produced there from different IT segments to solve exact problems such as performance. This information generally includes web infrastructure logs, network data, application diagnostics, or cloud service information. Shortly after consolidating this data, it is possible to conduct a cause investigation and react to specific incidents, failures, and other problems. Monitoring mechanisms and alarms allow you to monitor your entire IT infrastructure, including applications, by identifying specific conditions, trends, and complex patterns. With this real-time report of an organization’s “machine rooms”, IT administrators will be able to develop a service-oriented view of their IT environment. This enables on-the-fly reports and data visualizations that provide an overview of events from different perspectives. This includes information about how applications, servers, or devices are connected to mission-critical IT services.

The main characteristics of an Operational Intelligence Software

  • Monitoring of all business processes in real time.
  • Detection of all kinds of situations throughout an operational process in real time.
  • Big Data control and analysis. Our Operational Intelligence Software continuously monitors and analyzes a variety of high-speed, high-volume Big Data sources.
  • Analysis of different situations, trends, the root of the problems.
  • Different users can view the data dashboards in real time.

Conclusion

Applications, sensors, servers and clients constantly generate data. This machine information can be used for user transactions, customer behavior, sensor activity, machine behavior, security threats, fraudulent activity, and other measures. For meaningful analysis and subsequent decision making, special operational intelligence platforms are suitable. They allow you to discover the value of hidden information by collecting, indexing, searching, analyzing and viewing the vast amount of information. It offers companies real-time information about the increasingly digitized business world that can be used for decision-making and corporate governance.