Definition

What is continuous intelligence?

Continuous intelligence (CI) is a data analysis process that delivers real-time analytics and insights as a part of ongoing business operations.

CI technology takes current and historical data, analyzes it, and provides insights and actions an organization can take in response. Continuous intelligence also provides decision support and automated decision-making capabilities. This gives corporate executives, business managers, operational employees and automated systems deployed by an organization a real-time awareness of business scenarios as they happen.

The addition of continuous intelligence tools to computer systems lets organizations make better and faster decisions. That, in turn, lets them improve the business processes or functions supported by platforms that have incorporated CI.

Continuous intelligence goes beyond business intelligence (BI) and even embedded analytics, as CI not only provides insights to user queries but uses machine learning to continuously monitor and process data in the background to uncover unidentified trends. This helps organizations develop a more complete picture of their operations and gain even more value from their data. In addition to machine learning, continuous intelligence relies on augmented analytics, event stream processing, business rule management and decision management technologies.

How does continuous intelligence work?

To implement CI, organizations must have a high level of data and technology maturity, use software-driven products and services, and have modern applications, automated workflows and an advanced data management program.

A CI environment works by continuously ingesting structured, unstructured or semistructured data generated by various systems. The data can be both data at rest -- data housed in file hosting services, databases, data warehouses or other storage systems -- as well as data in motion, such as data moving across corporate networks or streaming data from external sources.

A continuous intelligence platform analyzes all the data using machine learning algorithms, artificial intelligence (AI) and other technologies and then does the following:

  • Delivers actionable insights to business users.
  • Recommends actions for users to take.
  • Automatically takes specific actions itself, if programmed to do so.

Moreover, the machine learning that powers continuous intelligence learns as it operates, allowing CI to become more fine-tuned over time. CI systems can also proactively push or deliver insights in response to user demands or other triggers.

Implementing continuous intelligence within an organization requires an IT environment and, more specifically, a data architecture capable of the following:

  • Supplying the required data through technologies such as application programming interfaces (APIs) and data pipelines.
  • Supporting data transformation and analysis.
  • Delivering and then acting on the generated insights.

These components are created using a combination of proprietary code and vendor-supplied software products.

Potential benefits of continuous intelligence

Despite years of investment in data infrastructure, analytics programs and increasingly AI, most organizations still struggle to use and optimize all the value from their data. The use of continuous intelligence, however, can help organizations boost their data use.

Like all successful data management and analytics investments, continuous intelligence within an organization can deliver other benefits. For example, CI deployments can help organizations do the following:

  • Significantly boost the speed at which an organization can act on decisions with ongoing real-time visibility into operations.
  • Respond more appropriately to business situations with real-time insights instead of historical reports or forecasts built on less-than-current data.
  • Automate responses to in-the-moment business situations.
  • Generate more accurate predictions.
  • Allow more accurate adjustments in operations in response to real-time insights.
  • Uncover hidden patterns, correlations, relationships, signals and trends within data that allow the organization to make better decisions or pursue new opportunities, i.e., new revenue models.

These advantages can lead to the following business benefits:

  • More efficient operations.
  • Improved user experiences for customers and employees.
  • Greater returns for the organization's investments in its data program.
  • A competitive advantage in the marketplace.
  • Better financial results through higher profits due to improved customer experience, as well as new business opportunities and cost savings from more efficient operations.

Continuous intelligence vs. conventional business intelligence

Although business intelligence still has value within the enterprise, such as for visualizing progress against key performance indicators and financial results, BI's static insights typically don't provide real-time information based on dynamic data that lets organizations accurately react and respond to current circumstances.

Continuous intelligence -- one of many technologies that help an organization make sense of its data -- builds upon predecessors such as BI, which has been widely used for many years. But while BI and CI have similar names, key differences exist.

To start, business intelligence relies on human orchestration and interrogation of data whereas continuous intelligence, with its machine-driven analytics, is automated. BI tools also typically required IT staff and other experts to extract, analyze and visualize data. CI democratizes the power of such analysis, with more users and even systems able to use the analytics within a continuous intelligence platform.

Perhaps most important is the difference in the data generated. Business intelligence dashboards deliver insights for predefined indicators and metrics, while continuous intelligence delves more deeply and broadly to find data patterns, trends and relationships -- even ones the organization didn't know to query. That capability makes continuous intelligence unique from BI and a particularly valuable addition to an organization's analytics program.

Chart comparing features of continuous intelligence and business intelligence.

Use cases and examples of continuous intelligence applications

Continuous intelligence can be used across functional areas within any organization, for multiple business use cases and to support various processes. The following are examples of where and how CI can be used in functional areas within the enterprise:

  • IT teams can use continuous intelligence to monitor systems and react efficiently to notifications, helping to reduce the overwhelming volume of alerts that can drain IT staff time.
  • Operations can use CI to generate information about performance, identify and innovate opportunities, and identify and address business risks, among other tasks.
  • The supply chain department can use CI to understand real-time needs based not only on historical data such as recent sales, but current situations such as inventory levels and items in transit.

The following are some examples of industry-specific continuous intelligence use cases:

  • The financial sector can use CI to improve its fraud detection capabilities by detecting suspicious activities in the moment.
  • Healthcare can use continuous intelligence to get a more comprehensive view of a patient and help clinicians make real-time decisions about treatment options that are specific to that patient at that point in time.
  • Retailers can use continuous intelligence to deliver personalized recommendations to customers.

Key features and capabilities in continuous intelligence platforms

Continuous intelligence platforms, by definition, must have AI-based machine learning capabilities to continuously ingest and analyze data. They typically also include the following features:

  • Cloud-native architecture.
  • Support for integration with data visualization tools.
  • APIs, connectors and other integration capabilities to access different data sources.
  • Data security features.

However, to successfully deploy continuous intelligence platforms, organizations must have a modern IT environment and a mature data management program that eliminates data silos and supports strong data governance and data quality processes.

This was last updated in November 2024

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