Continuous Intelligence

As part of the assignment, I would like to write something on the chosen topic, Continuous Intelligence. Continuous intelligence plays a major role in most digital business transformation projects. It is a growing part of enterprise analytics and BI strategies.

Definition

Continuous intelligence is a design pattern in which real-time analytics are integrated into a business operation, processing current and historical data to prescribe actions in response to business moments and other events. It provides decision automation or decision support. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business relationship management (BRM), and machine learning (ML). The definition extracts from the Gartner Research.

What can you do with Continuous Intelligence?

Continuous intelligence enables companies to deliver better outcomes from a broad range of operational decisions since it involves more relevant, real-time data in decision-making algorithms. Individuals can make sense of extreme volumes of data in milliseconds, evaluating more alternatives in greater detail than humanly possible without access to real-time data and processing.

Gartner estimates that, within 3 years, more than 50% of all business initiatives require continuous intelligence, leveraging streaming data to enhance real-time decision-making.

Combining all these forms of artificial intelligence (AI) with continuous intelligence drawing from geospatial, real-time, and historical analytics can further enhance business ability to know where assets and people are at all times and help predict what might occur next.

Adding rules engines and programmatic logic to AI, location data enables organizations to automate many decisions that previously required human insights. From predictive maintenance based on actual driving conditions to decide the best next action to take with customers to improve loyalty, leading companies are decreasing costs and improving revenues to become more successful.

What are The Challenges?

What makes continuous intelligence difficult is feeding a business’s analytics systems with high volumes of real-time streaming data in a way that is robust, secure, and yet highly consumable. The ability to combine “always-on,” streaming data ingestion and integration with real-time complex event processing, enrichment with rules and optimization logic, and streaming analytics is key to enabling Continuous Intelligence.

Many data analytics organizations lack experience with Continuous Intelligence, or unsure how to start their Continuous Intelligence journey to keep up with growing business demand.

Continuous Intelligence requires the building of new capabilities, skills and technologies. The challenge for data and analytics leaders is to understand how these differ from existing practice.

Why Use Continuous Intelligence in DevOps/DataOps

If you are considering DevOps as a strategy to adopt continuous innovation, your data strategy has to evolve, too. Traditional BI has too many silos and too much human intervention to support your move to an agile system.

Up to this point, I would like to add that in my current project, some of my team members, who are in the agile system, try to implement the ETL (Extract, Transform, Load) processes by following agile methodology. Sometimes ago, I went to the agile workshop and I have forgotten some of the concepts. It is a good time to read them up again.

According to Open Data Science’s article entitled “Why Use Continuous Intelligence in DevOps/DataOps,” it wrote that businesses look out for continuous innovation. Those who do not may put out shoddy products. Your data strategy, therefore, has to be seamless, frictionless, and automated.

Artificial Intelligence

The article adds, “Artificial Intelligence is capable of continually combing data, looking for patterns as data updates. Continuous intelligence allows you to analyze this data accurately and in real-time. The other piece could be letting go of data wrangling. Until you have deployed Continuous Intelligence, data wrangling remains a huge and functional part of your data management plan.”

Gartner identifies six defining features of CI.

  1. Fast: Real-time insight keeps up with the pace of change in the modern age.
  2. Smart: The platform is capable of processing the type of data you get, not the type you wish you had.
  3. Automated: Human intervention is rife with mistakes and wastes your team’s time.
  4. Continuous: Real-time analytics requires a system that works around the clock.
  5. Embedded: It’s integral to your current system.
  6. Results-focused: It should go without saying, but data means nothing without insight. Your program should deliver those insights. Don’t forget the results in the search for more data.

Once you let go of batch processing and silos, moving towards an agile framework is a reality with CI.

Open Data Science

Your team has access to these insights to direct new inquiries and drive brainstorming, pivot during sprints, and reach a frictionless state in which data flows in and insights become the next iteration of a product or a new product altogether. “

With this information, I have a vision; I wish to move into Continuous Intelligence and bring this agile methodology into my project.

References:
https://www.striim.com/blog/2019/05/gartner-identifies-continuous-intelligence-as-top-10-trend-for-2019/
https://www.rtinsights.com/what-can-you-do-with-continuous-intelligence/
https://medium.com/@ODSC/why-use-continuous-intelligence-in-devops-dataops-b6bc0a448b7a