Continuous Intelligence
What Is Continuous Intelligence?
Continuous intelligence is the use of tools and processes that integrate real-time analytics into business operations, processing current and historical data and recommending specific actions to be taken in response to business questions, difficulties, and opportunities.
Continuous intelligence leverages different modern machine-driven approaches and technologies such as augmented analytics, business rule management, optimization, event stream processing, and machine learning. In other words, continuous intelligence is all about deriving continuous business value from data. It allows users to quickly get to all of their data and accelerate the analysis they need, regardless of data sources or volumes, as well as letting the machine-driven technologies continuously query analysis rather than doing it once.
How Does Continuous Intelligence Differ From Traditional BI?
The primary difference between continuous intelligence and traditional business intelligence is that while traditional BI relies on people to orchestrate each step of the data analytics process, from data access to dashboard creation, continuous intelligence is an AI-based, machine-driven analytics approach that replaces human biases in each step of the data analytics process with a smart and automated AI.
Another difference is that traditional BI tools from the past have been created to be technically complex and typically require extensive IT staff to maintain and manage. Continuous intelligence platforms don’t have such limitations and, in contrast, bring the power of data into the hands of users of all levels.
Benefits of Continuous Intelligence
In a constantly changing and evolving business environment and analytics space, a continuous intelligence-based approach allows businesses to stay on top of the market, react promptly to changing conditions, and make more intelligent decisions based on insights rather than gut feelings.
Some of the key benefits of continuous intelligence include:
- Automate and guide actions at the moment of impact – augmented analytics is at the heart of continuous intelligence, and along with AI and ML algorithms, it guides people on what action to take to fully automate processes that trigger the downstream actions without any human intervention.
- Predict more accurately – the combination of current and historical data, algorithms, and machine learning allows you to run assumptions against the data available to validate and forecast multiple outcomes. By employing continuous intelligence throughout the organization, users are enabled to find insights on their own for more accurate predictions and anticipate future situations.
- Real-time impact analysis – the real-time contextualized data of continuous intelligence provides a greater understanding of implications at various points of different business processes, allowing for adjustments to operations that can result in significantly improved productivity that impacts the data analysis process in real-time.
- Fill in the gap between real-time and historical data – data, in general, is proof of your activities and actions. Analytics helps us understand those better and, as a result, bridges the gap between what was it, what is it now, and what would it be. Continuous intelligence machines look at real-time and historical data to develop better business solutions for the future.
- Improved data analysis – AI and ML technologies behind the continuous intelligence approach can effectively analyze complex data so the users can make the most informed decisions based on deep and more accurate analysis. That gives users a more complete view of the situation and helps avoid confirmation bias in the end. It also provides contextual suggestions that reveal insights users never thought they needed.
- Recognize data patterns – covering the acquired knowledge and study of statistical information, the machine learning algorithm is able to identify patterns in data and recognize them. It also helps classify data, apply identifiers, develop new algorithms, and create testing data.
The substantial and wide-reaching benefits of continuous intelligence for businesses of all industries and sizes make it an easy decision and obvious choice for organizations to embrace a digital transformation in order to elevate their business and stay ahead of their competitors.
Continuous Intelligence Use Cases & Examples
Continuous intelligence is not industry-specific, and it can be applied to a number of industries. Here are some examples:
Continuous Intelligence in Healthcare
Continuous intelligence in healthcare helps healthcare organizations, doctors’ groups, and hospitals make real-time, value-based decisions about what’s best for an individual patient based on a 360-degree view of the patient’s health and history. Using predictive analytics in healthcare also helps identify at-risk patients, prevent the development of chronic disease, identify cohorts exposed to a possible disease outbreak, forecast equipment maintenance needs before they arise, and much, much more.
One of the most important use cases of predictive analytics and continuous intelligence in healthcare is also the help of AI technologies to prevent human errors that could be fatal.
Continuous Intelligence in Finance
As the volume of global financial transactions continues to grow, detecting fraud, money laundering, and insider trading becomes more challenging. Continuous intelligence in the finance sector finds application in these key areas and more by including automated systems that use AI to spot suspicious transactions in progress and prevent any financial frauds before they occur.
Continuous Intelligence in Retail
With the help of continuous intelligence in the retail industry, retailers can get the most revenue out of their customers. One example of how CI is used in the retail industry is by combining streaming data with detailed customer purchasing history and preferences to deliver personalized and, most importantly, effective offers at the right time. Retail marketing also benefits from analytics as it assists marketers to plan future campaigns targeting their audiences more accurately.
Continuous Intelligence in Manufacturing
Unlocking continuous intelligence in manufacturing to harness data to understand and optimize machine use and maintenance can set manufacturing organizations apart from their competition. When real-time and historical data are continuously combined for real-time analysis to yield rapid, accurate, machine-driven decision-making, manufacturers can easily increase operational efficiency, forecast market demand, reduce equipment maintenance expenses, and lower costs by delivering insights into energy consumption, as an example. Analytics solutions also allow businesses to identify risks and help predict future risks by spotting trends and patterns throughout the supply chain. Advanced supply chain analytics can also help identify such risks at the onset, so your teams can act at the right moment and reduce the risk impact on the business.
How to Choose a Continuous Intelligence Platform?
Continuous intelligence platforms currently exist as standalone applications or as part of embedded analytics solution offerings. Both types of continuous intelligence platforms have been built to improve traditional BI tools and approaches. However, they all range in capabilities and functionalities that can benefit your business decisions and outcomes.
It should go without saying that the continuous intelligence tool you decide to partner with should provide the very foundation for continuous data analytics that is the AI-based, machine-driven way to continuously interpret data, discover patterns and learn what’s of value in the data.
Here is what else you should look for in a continuous intelligence platform:
Modern application architecture – choosing a modern, cloud-native application architecture will support the integration of visualizations and charts and will allow deeper analysis of your data, flexibility, and scalability. Modern continuous intelligence platforms support a full stack of integrated analytics functions and will integrate with your own apps seamlessly.
Cloud-based BI adoption – one of the biggest trends in the embedded analytics space, cloud-based BI makes it possible to run distributed application workloads across multiple cloud infrastructures.
Data-driven intelligence – the ability to extract real-time actionable insights and business value from all your different sources of data.
Collaborative BI – the combination of tools and features that allow real-time, contextual discussions, easy sharing of reports and dashboards with both internal and external team members and stakeholders, and comments and discussion abilities that support collaborative thinking and speed decision-making.
Continuous security – the capability to prevent security threats and data breaches across an ever-expanding digital surface.
Connectivity – if a given continuous intelligence platform cannot connect to all of your data, then there’s no point in it at all. Make sure that the vendor you choose offers seamless integration with all the data sources that store your data.
APIs – the continuous intelligence platform that you decide to invest in should also include an application programming interface (API) to handle dashboard creation and rendering, deep linking, custom UI for data source acquisition, and a modern API with multi-channel distribution capabilities.
Pricing – many vendors in the BI space use shady pricing tactics to maximize their profits. You don’t want to partner with a vendor that will charge you unpredictable costs like usage and users. We recommend choosing a continuous intelligence solution that has a 100% transparent pricing model that won’t escalate as your app sales increase.
