With the advent of digital transformation and the increasingly rapid growth of technology, many companies have been adapting to all these changes quickly.
The need for data analysis and the use of Big Data to drive better business decisions has always existed, but today there is a major challenge in this area, and that is to ensure that data analysis is rapid and that it is continually and constantly updated, whether the data is large and diverse, and whether it is structured or unstructured. This is where the concept of Continuous Intelligence (CI) comes in, designed to analyse all types of data in real time.
It could be said that the digital transformation has been gradually bringing companies closer to what it means to operate in real time. But it was not until the arrival of the COVID-19 pandemic that this transformation accelerated to the point of no return.
In this new era, successful businesses will be those that operate using a software-driven model. These companies will recognise the power of transforming huge volumes of data into knowledge in real time, which will drive them to success. The ability to do this in real time, continuously and across multiple functional disciplines, lies at the heart of Continuous Intelligence.
What is Continuous Intelligence?
Continuous Intelligence is a tool in which the analysis, in real time, of all types of data is integrated into commercial operations to determine actions that respond to commercial moments and events. It is a fluid solution driven by Artificial Intelligence that allows a company to take advantage of data in a continuous and detailed way from all possible sources.
CI combines data and analysis with transactional business processes and other real-time interactions, leveraging technologies such as augmented analytics, optimization, flow processing, and machine learning, allowing for reduced human intervention throughout the process.
In addition, this tool enables organisations to more quickly deliver reliable digital applications and services, protect themselves against security threats and consistently optimise their business processes in real time. On the other hand, this also allows employees to obtain development, IT and security equipment with all the data and information needed to address the technological and collaborative challenges required by their companies.
The rise of Continuous Intelligence
Organisations have been searching for real-time intelligence for a long time, and until now the systems were quite limited in their ability to do so. But with the advent of the cloud, advances in transmission software and the exponential growth of data being collected thanks to the Internet of Things (IoT), these systems can be implemented without problems on a much larger scale.
In addition, the COVID-19 pandemic has led to a business discontinuity in which the digital transformation has gone from evolution to explosion overnight, doubling, tripling and quadrupling infrastructure and application workloads in the cloud.
This has given rise to the need to collect, index and analyse all data in real time, and as a consequence, to the rise of Continuous Intelligence (CI) and its widespread use. In fact, it is estimated that by 2022, more than 50% of the major commercial systems to be created will incorporate CI to make use of real-time data to improve decision-making.
In addition, several surveys have revealed that 88% of senior executives said their company will benefit from Continuous Intelligence. 74% believe that Continuous Intelligence will help drive business speed and agility. 76% indicated that they are likely to use this technology in the next 12 months. And 62% believe that Continuous Intelligence is a new approach that many companies will need to adopt as they make more use of their software to generate revenue.
Benefits of IC
Continuous Intelligence allows companies to access all information more quickly, regardless of whether the sources from which this data is extracted are complex, large or varied. And all this with the aim of covering the needs of users who increasingly want everything to be instantaneous.
In addition to this, there are other benefits that make this technology must be implemented in many companies:
- Continuous Intelligence improves the quality and accuracy of a wide variety of operational decisions because it incorporates more types of reliable data into the algorithms used to calculate decisions.
- Systems can process large volumes of data quickly, protecting people from possible overload. They can apply optimization rules and logic to evaluate many more options than a person might consider in the time available.
- Backed by technologies such as Artificial Intelligence (AI), Machine-Learning (ML) and Augmented Analytics, Continuous Intelligence can also minimise or even eliminate human intervention throughout the process. The IT team can save themselves a flood of notifications they receive every day from monitoring tools.
- It is a seamless process that automatically extracts data from various sources and allows companies to make use of it when needed.
- CI relies heavily on the availability of real-time data, which in itself is a challenge for most organisations. Businesses that collect large amounts of data often lack a system that can leverage the data for useful information.
- CI also helps with cyber security and fraud detection. A potential security threat can go unnoticed by a professional for a number of reasons. In contrast, an AI- and ML-driven IC system can generate an appropriate proactive response to the threat and act on it based on multiple data points without disrupting business operations.
- It is a tool that is available 24 hours a day, 7 days a week, and throughout the year, with no exceptions.
- CI can recognise data patterns, as identifying and recognising them is part of the LFA. CI covers the knowledge gained and the study of statistical information. It helps to classify data and apply identifiers, develop new algorithms and create test data.
Trends that drive continuous intelligence
In addition to the benefits of this technology, to understand how Continuous Intelligence has emerged as a key approach at this particular time, one must examine the context and trends that underpin the concept.
The need for Continuous Intelligence is the result of the convergence of a variety of trends that have been widely observed in the industry for many years.
The acceleration of migration to the cloud
More and more applications are being hosted in the cloud. Currently, 75% of applications are hosted through cloud technologies. And this percentage will only continue to grow.
As businesses move to the cloud, data management challenges increase exponentially: applications must scale to handle larger workloads, security issues must be identified and resolved in real time, and application functionality must evolve to anticipate and address changing customer requirements.
The importance of real-time analysis
As companies increasingly struggle with the digital transformation, what has emerged is a widespread appreciation of the importance of real-time data analysis and a recognition that the insights from these analyses have strategic value for the entire organization. In fact, many advanced cloud businesses are embracing real-time analytics as a key to success.
The rise of DevSecOps
The focus on modern application development means that companies must continually focus on delivering high-performance, scalable and always-on digital services. These services require modern customised architectures: applications with new standards, new technologies and microservices running on cloud platforms.
The need to manage these modern architectures effectively has led to the practice of DevSecOps which incorporates security as the main concern and throughout the software delivery life cycle.
The ephemeral nature of today’s applications has made it clear that traditional solutions for monitoring, troubleshooting and security management fall short, and an alternative approach is required, and the only way to manage this lifecycle is through a Continuous Intelligence platform in the cloud.
The consolidation of the IoT
The use of Continuous Intelligence (CI) in conjunction with Internet of Things (IoT) devices will significantly change the way companies use IoT analytics by allowing for more advanced, near real-time analysis. This will take IoT data analysis to higher levels than traditional operations and have a greater impact on strategic planning and change in the organization.
The adoption of CI for IoT has major implications for manufacturers, supply chain operators, transport, maintenance and repair organisations and more.
The increased importance of 5G
As the number of deployed IoT devices increases, companies need a way to access the data generated by these devices. The 5G will greatly increase the amount of data that can be sent at once and the speed at which it can be sent. Fundamentally, it will enable the use of the Internet of Things and intelligent sensors for conventional applications.
Many analysts point out the importance of 5G for making use of this IoT data in Continuous Intelligence applications. This is because 5G services offer higher data transmission speeds (over 1 gigabit per second) and low latency (up to a few milliseconds) compared to services available today. These capabilities are essential in some IC applications where decisions based on real-time analysed data must be made in milliseconds or minutes. Pumping more data at faster speeds and reducing latency will help improve the performance of an IC application.
Continuous Intelligence in the different industries
Many organisations in different industries are integrating Continuous Intelligence into multiple aspects of their operations. The obvious challenge in such industries is gaining access to customised data whose use is governed by privacy regulations. In such applications, special processes are often required to gain access to that data, and additional measures must be taken to ensure that the data remains protected during use.
Organisations that are able to address this issue can apply predictive analytics and Artificial Intelligence to the data to provide real-time information that can be quickly acted upon. Examples of IC in industries include the following:
Financial sector
As the volume of global financial transactions continues to grow, the detection of fraud, money laundering and insider trading becomes more challenging. The areas where CI’s application is having the greatest impact are monitoring transactions and managing penalty alerts, reviewing activity and payment fraud patterns, and conducting investigations into suspicious activity and entities.
In these areas, CI is implemented in a variety of ways, including automated systems that use Artificial Intelligence to detect suspicious transactions. CI is also used in RPA solutions that mimic the actions of human users to perform repetitive, high-volume tasks, freeing employees to focus on higher-value tasks.
Healthcare
An example of CI’s application in health care is to help organisations make real-time decisions about what is best for a patient. The focus in such applications is on providing value-based care that emphasises outcomes. Using predictive analytics, organisations provide real-time, personalised guidance to patients based on the individual’s health and history.
Retail sector
Many retail IC use cases combine transmission data, such as clickstreams, with detailed customer purchase histories and preferences to provide personalised offers, as offers are more effective when delivered in real time.
The aim of CI is to better understand the needs of customers, to provide them with a better experience and better service when they interact with the company in some way, whether by entering the physical shop, visiting your website or calling customer service.
Perhaps the most interesting aspect of using CI to improve the customer experience is that it allows for innovations that were previously not possible.
Industry Sector
A common application of IC in the Industrial Sector is to move from reactive to proactive maintenance using predictive analysis to help identify potential equipment failures and thus reduce downtime. Another application is to use CI to optimise spare parts inventory.
With CI, for example, an industrial plant could, in essence, establish a just-in-time supply chain approach where parts are ordered based on the lifetime probability derived from predictive analysis of IoT data, with the sole aim of reducing downtime and improving operational efficiency.
Conclusions
What makes Continuous Intelligence (CI) such a compelling value proposition today is that its effectiveness is not affected by the complexity of the data. Given its high-speed capabilities to analyse large volumes of data from any number of sources, CI can greatly enrich an organisation’s analytical environment.
CI treats data in a way that the human mind simply cannot process. It automatically creates data stories that become an integral part of the analytical process. This helps to guide decision making.
Furthermore, given the growth of the Internet of Things (IoT) and how 5G technology collects data even faster, the increase in connected devices is creating a data-rich environment for CI systems to benefit. Organisations can now interpret data in near-real time, becoming more agile in the process.
In a short time, Continuous Intelligence applications are likely to replace a significant portion of the manual data analysis that is done today. Because Continuous Intelligence provides organisations in all industry sectors with a more effective way of performing data analysis.
Those companies best equipped to rise to the challenge will be those that handle real-time data, and successful real-time business operations will be those that adopt Continuous Intelligence as the core of the strategy to accelerate decision making, ensure competitive advantage and deliver great customer experiences for sustained success.
