“Nothing endures but change.”

–Heraclitus

Everything changes. And so does data. 

But how do we keep track of these changes, and understand them? If the ancient Greek philosopher Heraclitus got anything right, it’s that nothing is permanent except change. 

Well, perhaps not anymore. Continuous intelligence may be mankind’s–and the tech world’s–greatest innovation in capturing and understanding change. And with more than half of major new businesses planning to implement CI for data analytics in 2022, brands need to seize the potential for success.

But what is continuous intelligence, and how does it work? Read on to learn about CI and its formidable presence in the data analytics world.

Not quite AI, not just an algorithm

The term itself is a nebulous, ambiguous phrase: continuous intelligence. Objectively speaking, CI could be used in many different contexts. 

However, the key difference with continuous intelligence in the data analytics world, is that it is, in fact, a noun. Continuous intelligence is a system–an engine–that unifies every data point of a company and produces real-time analytics. Simply put, CI is a living engine that reads all your data, old, current, and incoming–all at the same time. Systems and data points are incorporated into a real-time data pipeline. It then digests that data and creates insights and analytics brands can use–from product and customer experience to engineering and security analytics. 

CI isn’t exactly Artificial Intelligence(AI), but it carries certain components of AI. For example, it is constantly ingesting data and reading it–much like an Amazon Alexa or Apple Siri would learn your habits and preferences, like what shoes you like to purchase or music channels. However, CI doesn’t do anything you don’t ask (unlike Alexa’s eavesdropping glitches). 

Nor is CI just an algorithm–although it is comprised of algorithms and automated components. Instead of simply capturing your data, CI also reads data and allows you to run queries and analyses. 

Let’s break down what exactly a CI engine is.

What's under the hood

Under the hood of continuous intelligence, there typically are:

1. Aggregator: An aggregator collects real-time streaming data from many data sources. This could be a data aggregator or ETL engine, that compiles data. CI reads and updates data–well, constantly. 
2. Broker: A broker enables data in real-time available for use. A broker creates a pipeline for data compiled by the ETL engine to the analytics engine. 
3. Analytics engine: This is where data is correlated and data streams are merged together as data is analyzed. It applies logic to the aggregated dada and takes ambiguous data to make sense of it.
4. Interactive UI or output engine: An interactive UI that can enable the analytics engine to cross-measure analyses or automatically aggregate those analyses to make a final diagnosis of something.
   
   

Continuous intelligence in the real world

CI is nebulous, and understanding its components figuratively can be a challenge. Let’s use a real-world example to help visualize continuous intelligence:

Imagine a doctor is monitoring a patient’s health using a machine that measures blood pressure, heart rate, and brain waves. The machine is connected to the patient with sensors to monitor all of these metrics at once. In order to read the incoming data from the patient, the monitor is connected to computer software–one that can ingest those metrics, and then generate potential diagnoses. It can also create alerts when something happens. For example, if the patient’s blood pressure begins to suddenly drop or rise–the monitor would detect and alert that change. 

In this example, the aggregator would be the sensors connected to the patient. The broker would be the machine that organizes the data from the sensors and moves it over to an analytics/software engine. The analytics engine would then read for expected metrics, and the output engine would send alerts or aggregate all the different analyses with additional logic. That logic might say that a blood pressure of X, a heart rate of Y, and a presence of cortisol as Z in the patient’s blood indicate a possible upcoming heart attack. 

This whole system continually monitors the patient’s health data and continually provides information to the system and the person interpreting it. Thus, medical providers know how to act–quickly and in real-time–without having to read actual charts and numbers from each individual sensor to come to conclusions themselves.

How does this example relate to brands and their teams? Well, companies can leverage this technology for whatever data they want to collect, track, and analyze.

Better than BI. Why?

Many brands are familiar with business intelligence (BI) and big data–which have been heavily utilized over the last two decades. In 2020, 54% of businesses noted that cloud-based BI tools were an essential component to current and future goals. BI, big data, and CI are closely related but are each their own entity: 

• Business Intelligence: BI is the process of leveraging several software applications and tools to collect, aggregate, and analyze high volumes of both structured and unstructured data from various sources–including internal business data and external data. Sources BI tools pull from include email, files, images, and videos. BI tools are manually utilized to uncover insights, trends, and patterns by analyzing data. 

• Big data: Big data operates similarly. Big data refers to large data sets that are too large for excel sheets or basic database and data handling architectures. Like BI, big data includes the process of storing, processing, and visualizing data within these large data sets–which are often stored in data warehouses or data lakes. 

However, CI platforms go beyond BI and big data–in regards to both capabilities, scalability, and efficiency–and are an ongoing system that ingests and reads data. Meaning, CI eliminates the need to break down and sort data manually and run specific queries. Instead, it automates that process for users and computes insights on its own, without requiring users to interpret, interact, and manipulate the data. CI pulls data automatically from all sources, across all platforms in real-time. 

In addition, CI beats out BI and big data for the following reasons:

• Machine learning and AI: BI doesn’t use machine learning or AI, whereas CI leverages certain aspects of both. In other words, much of the legwork in BI must be completed by data scientists or engineers. BI users have to manually guide BI tools through the needed workflows, select data, and make the data compatible with their system.

• Tedious manual effort: BI and Big data require human interpretation, interaction, and manipulation of the data or visualizations to actually get any value from the tool or the data they're analyzing with either approach. BI requires manual work from data scientists to upload, extract, and compile data into each tool. CI removes that component and ingests data indefinitely. With CI it's a constant hands-off pulse of insights geared towards a very targeted outcome. 

• Speed: BI wasn’t ideated with the intention of reading and analyzing data quickly or in real-time. CI was built specifically for real-time data analytics and faster insights.
  
  
• Human bias: CI takes an algorithmic approach to answer a single question. An algorithm might have multiple potential outcomes, but each will only indicate them if the data specifically supports it. Whereas, human interpretation with BI and big data may lead to inaccurate or missed insights.

Benefits of continuous intelligence across the board

Continuous intelligence, when compared to legacy tools like BI and big data, seems like a natural progression of better analytics tools--and it is. But, CI isn't better just because it's new technology. There are also a number of critical benefits CI provides brands.

• Constantly streaming data: CI engines continuously ingest data as it becomes available to its system. This eliminates the need for users to manually upload, process, or clean data each time they want to collect and analyze it. Instead, CI provides a steady, ongoing flow of information and data that is read, stored, and analyzed. 

• Fast, real-time insights: Think about our heart/blood pressure monitor example mentioned earlier. If a sensor ingests a patient’s heart and blood pressure, that data can then be immediately relayed to an engine that stores and computes those metrics. Because of the way CI is configured, it has the capacity to produce real-time insights. This means teams can access data immediately, run queries, and get answers right away. 

• Democratizes and unifies data: So much of a brand’s data comes from siloed sources, whether it’s from campaigns across various channels or using CDPs and CRMs. Regardless, brands need a unified view of all their data. CI does exactly that. It pulls data from all sources and produces insights that every team can understand.

• Can be customized: Streaming sources can be customized and changed based on a brand’s needs. Whether they choose to focus on a few data sources or many, brands can easily change them. With CI, this can be done on the fly or in a more targeted, slower process that incorporates large data sources over time. Brands can also swap out or swap in new or better data and they improve their CI system.

• Automated data import: In addition to digesting and reading data, CI platforms also automated data imports. As mentioned above, automated data import can be customized to select a brand’s preferred streaming sources. Once CI platforms are connected and configured to whatever data warehouse or data sources desired, they continuously import data as it comes in–and reads it at the same time. 

• Comprehensive view and analysis of data: Collecting all your data is one challenge, but accomplishing a complete analysis of that data is another. Because CI can unify and collect data across siloed channels, it, in turn, provides a holistic view of the data. This gives teams across companies the ability to view insights and analysis under one single pane of glass.

Continuous intelligence is for everyone and anyone

Data analysis has traditionally been the domain of data scientists, analysts, and engineers. However, continuous intelligence is disrupting that norm--and giving everyone across a company the ability to dive into data independently. 

Product Management

CI gives product teams the ability to keep their finger on the pulse of product performance, and new feature rollouts. It’s critical for product teams to get the most up-to-date insights to amplify what’s working and quickly resolve what isn’t. CI is continuous–meaning, it works around the clock to continuously ingest and read data. For example, product teams can get a granular, real-time view of how a customer or prospect is navigating and interacting with a new product launch. This means teams track the performance of a new product or feature as time passes–without having to rely on analysts or outdated, time-consuming ETL processes that can lead to missed opportunities and fresh insights.

Marketing

Marketing teams can also benefit from CI in regard to improving campaign performance and tracking advertising success. It’s key for marketers to get a full snapshot of a customer’s interaction with their brand–whether it’s finding the most successful channel to reach users or finding better strategies to generate more leads. With CI, teams can better understand customer journeys, points of success, and areas of friction. Marketers can gain insight into how well content, ads, and segmentation is working.

CI gives marketers a more clear understanding of what customers want and resonates with. In today’s world, customer experience and personalization drive a brand’s success. So, marketers can tap into that with real-time analytics. According to McKinsey, 44% of businesses consider CI a top marketing and sales priority, more so well than trending topics like digitization and omnichannel.

Analysts and data scientists

When it comes to exploring data and time-series events, analysts and data scientists need the most efficient and comprehensive platform available, like CI. CI platforms, like Scuba, provide analysts and data scientists with a system that automatically updates and continues to digest data as it comes in. This enables analysts to capture and explore time-series data, run queries, split events, and conduct experiments with the most accurate data. Instead of relying on analytics tools that may not have the capacity to update data daily, analysts and data scientists can provide their brands with data that is relevant, fresh, and constantly evolving.

Engineers

Data security and management are critical for business success, and CI benefit software engineers. Companies need to ensure they remain compliant with regulations, like GDPR, to protect against data loss and breaches. With CI, engineers can build and manage their data structures with data reliability and speed. Engineers can also leverage CI in the following ways:

• Monitor internal and external activity as it happens--and work in tandem with analysts to format data in a way that is easily used.

• Build scalable, unified data infrastructure that is supported by fast, accurate, up-to-date data.
• Review event logs across a network in real-time to monitor any suspicious activity.
• With real-time analytics platforms like Scuba, engineers can analyze new behavioral patterns, without ETL, pre-aggregated data, or the need for company-wide technical expertise.

Service (CX teams)

Customer service teams leverage CI to provide better customer experiences, gain deeper insights into a customer’s journey. Reviewing, monitoring, and analyzing data with CI gives customer experience teams the ability to do the following:

• Increase customer lifetime value.
• Provide an elevated customer experience in comparison to competitors who do not leverage CI.
• Identifying friction points in a customer’s journey, to then address and reduce those problems.
• Reduce churn and improve retention rates.

Leadership

CI is key to success and profit for any brand--and can strongly benefit company leaders. Driving revenue and accomplishing business goals are top of mind for executives who are facing an ever-growing bar of success.

Executives can rely on CI to provide fast, accurate, and comprehensive analysis reports, and make informed decisions on those findings. CI platforms like Scuba digest and unify data in a single platform and provides leaders with real-time visibility across different data siloes. Business leaders can use CI to accomplish the following:

• Make agile, strategic business decisions. 
• Reduce costs and increase profits in an efficient manner.
• Improve and create products based on customer journey experiences. 
• Provide accurate, unified reports to stakeholders and board members.

Harness the power of CI with Scuba

Continuous intelligence is revolutionizing the way businesses approach customer experience, product analysis, and security. With insights that are fresh and quick to see, CI gives businesses and team members across an organization the power to make informed decisions. But, not every CI platform is created equally. 

Scuba’s continuous intelligence analytics platform is easy to use and provides brands with comprehensive, fast insights with privacy built-in mind. Whether your brand wants to track customer experience and journeys or product performance, Scuba has the ability to do so. Scuba digests data from multiple sources and can store both structured and unstructured data, meaning brands don’t need to deal with ETL or tedious data work. Instead, users across a company can dynamically visualize customer journey maps, and analyze new patterns as they emerge with Scuba–whenever they want.

Want to make better informed, agile business decisions and improve your customer experience at the same time? Explore Scuba today.

Watch this video where CloudBees merges customer data from multiple data sources, and then starts to perform deep analysis with Scuba in under an hour.