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The Rise of Continuous Intelligence

By Clairvoyant Perspectives, Thejesvini Sukumaran - August 27, 2021

This blog discusses Continuous Intelligence, how it is different from Big Data and Business Intelligence, and its use-cases.

Real-time analytics has a notable share in most business models, tasked to deliver personalized customer experiences to businesses that demand everything “right now”. Despite this trend, there are organizations even today, that rely upon historical databases instead of real-time data. Such businesses may find it difficult to reach a decision in this rapidly changing data-centric climate. Continuous intelligence (CI), which is all about frictionless cycle time to derive continuous and improved business value from all data, could be the strategy for such businesses.

“The modern consumer wants everything “right now”, and businesses need to ensure that their data is fluid enough to be leveraged at any time”, says Shantanu Mirajkar, co-founder and CTO at Clairvoyant, India. “Businesses looking to harness the digital revolution can make use of the real-time continuous intelligence to answer critical queries on the spot.”

How is Continuous Intelligence different from Big Data?

Big data involves data-wrangling which pulls the data from both internal and external sources and stores it in a single platform to be used for analytics and other purposes. The process of data wrangling, however, demands a lot of effort and resources to make the data usable. To better the process, businesses add a data wrangling module into the workflow that requires a data-literate workforce for its operation. This ultimately slows down the whole process and businesses end up having to invest time just to create value out of the obtained data.

Continuous intelligence, which is a step-up from big data, does not require that users add an extra step to the process to leverage its data processing abilities. It is a seamless process that automatically pulls data from various sources and allows businesses to make use of them whenever needed.

Business Intelligence or Continuous Intelligence- which should you opt for?

Continuous intelligence is a seamless AI-driven solution that allows your business to take advantage of continuous, insightful data from all possible sources. It digs out data patterns that help your business marry intelligent data with the constant intent to find new insights. To top it all, backed by Artificial Intelligence, Machine Learning, and the right training data, continuous intelligence can also minimize human bias throughout the process.

Unlike Continuous Intelligence, Business intelligence (BI) tools do not engage machine learning or AI. BI heavily relies upon skills and expects (data literate) people to guide the BI tool through each step of the workflow, right from data pulling and integration through interpretation. Also, unlike CI, BI was not ideated to fast-track the access of information/data.

Organizations looking to obtain actionable insights from their database on a regular basis can trust Continuous Intelligence to proactively support them.

How can CI help your business?

According to Gartner, by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. CI can help your business in more than one way-

Streamline your data to obtain continuous insights- CI is majorly driven by the real-time availability of data, which in itself is a challenge for most organizations. Businesses that collect vast amounts of data often lack a system that can leverage the data to arrive at actionable insights. They can streamline the data to the right place to apply AI or derive continuous intelligence out of it.

Increase your IT team’s efficiency- CI eliminates human intervention in many tasks from an IT standpoint. It can spare the IT team from a flood of notifications that they receive every day from monitoring tools. While many notifications are ignored, others can take over months to get resolved. With a massive volume of information being fed to such systems, a ML-driven automation system can start picking up patterns, bring down alert fatigue, speed up resolution times and further the overall quality of the workflow. As it begins to analyze more and more data, it can even start predicting issues before they occur and ensure timely resolution. Identifying the underutilized monitoring tools can ease the restriction to the flow of data and better utilize continuous intelligence for improved outcomes.

Strengthen your cyber security strategy- CI also finds its application in cyber security and fraud detection. A potential cybersecurity threat can go unnoticed by a professional for various reasons. In such cases, an AI and ML-driven CI system can throw out an appropriate proactive response to the threat and act on it based on multiple data points without disrupting the business operations. On other occasions, it can also send out an appropriate alert to the human analyst responsible for the function, thus saving a lot of their time and effort.

Use cases

Use case 1

Financial Sector

Problem statement- A prominent payment gateway company was looking to help merchants engage their customers by offering them on the spot offers thereby enhancing customer experience. They also wanted to employ analytics to aid cross-border compliance breaches, revenue trends, and product performance.

Clairvoyant’s solution- Clairvoyant offered a continuous intelligence platform that functions in the following sequence:

  • It collects payment transactions, historical and real-time financials and accumulates data in low latency stores
  • It combines this information with other customer-specific data like location sharing, social media, personality insights, etc. to create a unified data collection at near-real time
  • This data is consumed by downstream teams like Merchant Reporting to re-calibrate product positioning, customer segmentation, offers, and campaigns, etc.
  • It also helps the compliance team react to fraudulent activities in a shorter timespan

The implementation of continuous intelligence in this use-case resulted in quicker turnaround times for identifying shopping patterns.

Use case 2

Manufacturing Sector

Problem Statement- A leading company in the manufacturing sector wanted to improve its manufacturing quality and optimize its production costs. It was also looking to analyze all the incoming sensor data to predict near-future manufacturing issues.

Clairvoyant’s solution-

  • We architectured an end-to-end multi-node cluster to house all the ingested data sourced from the firm’s factory machines
  • We enabled ingestion of the production data and their efficient storage in Kudu with the help of Spark streaming process
  • We provided a robust open-source IoT architecture to aid with operational insights and highlight areas of improvement

Conclusion:

The above use-cases are proof that the application of continuous intelligence across industries are valuable. The union of continuous intelligence and artificial intelligence can deliver multiple benefits to organizations. Shantanu says, “Today’s data processing platforms like Spark combined with stream-processing platforms like Kafka can deliver a powerful and scalable foundation for supporting continuous intelligence in businesses. Using such technologies, the industry has solved multiple use-cases in the finance and compliance domains transforming streams of raw data into insights with minimal latency.”

CI marries real-time data and artificial intelligence to give rise to advanced analytics that can answer your business queries on the spot. Continuous data visibility, faster resolution times, continuous insights, and productivity are just a few key benefits.

Providing a frictionless flow to the data and applying artificial intelligence-powered continuous intelligence can help modern enterprises turn their data into actionable insights, thereby increasing the overall efficiency of their workflow. No matter the industry, the applications of continuous intelligence are countless.

Author
Clairvoyant Perspectives, Thejesvini Sukumaran

Tags: Machine Learning Artificial Intelligence Big Data Data Engineering