By Apsara Rodriguez
March 2020
Investing in machine learning and AI is becoming increasingly necessary to stay competitive in the modern marketplace. To keep up with consumer demand, it is now vital for businesses to manage the flow of data. This can be achieved by staying up to date with data analysis strategies. Recent advancements in technology have led consumers to expect a high degree of responsiveness from companies (Mulligan, 2018). The demand for “everything now” is only reasonably achieved by sifting through data and analyzing the information in real-time. Lacking quick decision-making processes, a business will struggle to attain the growth desired in a marketplace with an increasing digital presence. One analytical strategy which is capable of incorporating changes in data quickly, safely and seamlessly is known as continuous intelligence.
What is Continuous Intelligence?
According to Gartner, a leading research and advisory company, “Continuous intelligence is a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events”. In other words, CI is a way for systems to know what’s happening in real time.
Figure 1 provides a graphical representation of how continuous intelligence supports the decision-making process. The left side of the figure demonstrates the gathering of data, both real-time and historical, which is to be later transformed into a message that is meaningful and actionable. This is where continuous intelligence plays a role. Continuous intelligence is an artificial intelligence-based system used to interpret data, find trends, and discover new insights in real-time data.
In essence, CI enhances the human decision-making process by bypassing the need to wait for the formation of a historical database to create new insights. The OODA (Observe, Orient, Decide, Act) loop seen on the right of Figure 1, illustrates a strategy for human decision making in which information is first reviewed (Observe), then analyzed (Orient), used to determine the best course of action (Decide), and finally the information is used for execution (Act). This
Figure 1. Representation of the role of continuous intelligence in decision making.
strategy, originally developed for military operations, encourages quick critical thinking in response to issues that may arise or are already presenting as trends in the data. We can see how CI would fit well in this type of system as it provides an up-to-date analysis of information collected no matter the volume, complexity, or the diversity of the data. This allows businesses quick access to data so that they can act on it promptly. Speed is more necessary now than in the past as the world, in general, has become more connected to data resulting in the generation of endless amounts of information. As such, it is best to rely on a system which can continuously automate the process of data analytics so that data-driven business decisions can become more fluid.
Adoption of Continuous Intelligence
Continuous intelligence should be adopted by companies looking to apply real-time data analytics to systematically recognize patterns as well as provide responses to irregularities in a timely manner (Ransbotham, 2019). Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time data to improve decisions. Those companies that can verify and analyze data in real-time and detect the valuable patterns to improve their performance will thrive compared to the companies who do not adopt these analytics. Currently, top performing organizations are building and using continuous intelligence by focusing on the right data, refining relevant information, recognizing the patterns, and acting upon them immediately (Kelly, 2019).
An example of continuous intelligence in current use are mobile navigation applications which continuously updates a person’s position as well as the expected time of arrival to a specified based on traffic data, travel distance, and average speed. In this setting, CI is gathering both historical and real-time data to provide users with meaningful data to act on. For example, if a traffic jam on a selected route is noted by the application, the program will prompt the individual to take a detour using an alternate route for a faster arrival time. This is just one example of how CI can be utilized, however, there is exists countless more opportunities to implement this kind of functionality into a broader set of applications to meet consumers’ expectations (Mowery).
Another example of the application of CI can be seen in the healthcare field. In healthcare settings, real-time data analytics can help hospitals to provide more proactive care to the patients. For example, if a patient’s most recent lab work reveals an allergy to a dye agent used during an MRI, a CI-based program can instantly inform the doctor to change the instructions for the MRI exam (Mowery). As in the case of the navigation example, here, CI provides on-the-spot information that can be used by the physician to make an informed decision. In this case, CI can directly affect a patient outcome based on the data and the analysis it has performed.
As has been demonstrated, CI is applicable in almost any settings. For this reason, the demand for continuous intelligence applications is soaring in the industries such as financial services, e-commerce industries, insurance companies, public sector, telecommunications, transportation, utility and retail industries (Miller, 2019). Netflix, one of the largest and most popular media streaming companies, has also CI for real-time data analytics to provide users with a personalized streaming experience based on their viewing behavior.
Continuous Intelligence: Business Use Cases
Continuous Intelligence use cases manifest themselves as operational intelligence uses cases in which data analysis are applied to make business decisions in real time instead of relying on historical data. Some examples of continuous intelligence business use cases are Predictive Maintenance, IoT Analytic, Supply Chain, Fleet Management, Next Best Offer and Checking Transaction for Fraud.
Democratize Analytics and Data Science for Continuous Intelligence
The consolidation of AI and automation into analytics and data science is democratizing these technologies and giving more users faster access to deeper, actionable intelligence so that they can benefit from these technologies (Pisano, 2019). These innovations in analytics and data science empower business users, data analysts, and data scientists with advanced analytics capabilities to work collaboratively, and to provide continuous intelligence across the major use cases of every business from strategic to operational (Diaz, 2018). The democratization of data makes it easier people to gain a better understanding of the data to expedite decision-making and uncover brilliant opportunities for an organization (McKinsey, 2018). In order to understand data independently and make insights-driven decisions, organizations must make data available to their employees so that they can be empowered to provide their insights on business growth.
According to Gartner, the four ways to democratize analytics and data for continuous intelligence are as follows:
- Search-driven analytics: This is about the ease of use of natural language interfaces to speed up finding and delivering insights. Every human should benefit from data-driven decision making irrespective of their proficiency with analytics tools.
- AI-powered insights: This is about using AI, Machine learning to speed up data preparation, insight discovery, analysis, and delivery of insights.
- Embedded data science: This is about using data science with the user simplicity of analytics dashboards for high-value use cases.
- Streaming analytics & data science: This is about applying analytics and data science on streaming data at critical business moments.
Conclusion
There is still many areas where practical use of CI needs to be adopted. Businesses will find adopting continuous intelligence to be transformative for business operations through continuous creation of new insights which can provide a competitive advantage. Furthermore, consumers’ experience can be enhanced by making real-time data driven decisions. With real-time data analysis, organizations can promptly respond to patterns in the data and changes to market environment as well as predict future trends. With the help of CI, businesses will be able to facilitate continuous data capture and form relevant insights, providing the right information to the right people at the right time in an accurate way.
Works Cited
Diaz, Alejandro, et al. Why Data Culture Matters. McKinsey Quarterly, Sept. 2018, www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Why%20data%20culture%20matters/Why-data-culture-matters.ashx
“Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019.” Gartner, www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo
Herrell May, Kelly. “Why Continuous Intelligence Should Be a Key Priority for Business Leaders.” Transforming Data with Intelligence, TDWI, 29 May 2019, tdwi.org/articles/2019/05/29/biz-all-continuous-intelligence-key-priority-for-business-leaders.aspx.
Miller, Michael J. “Gartner: The Present and Future of Artificial Intelligence.” PCMAG, PCMag, 29 Oct. 2019, www.pcmag.com/news/gartner-the-present-and-future-of-artificial-intelligence.
Mowery, Katie. “The Rise of Continuous Intelligence and How It’s Changing Analytics and BI.” Clarity Insights, http://www.clarityinsights.com/blog/the-rise-of-continuous-intelligence-and-how-its-changing-analytics-and-bi
Mulligan, Sharmila. “What Is Continuous Intelligence?” Forbes, Forbes Magazine, 18 Oct. 2018, www.forbes.com/sites/forbestechcouncil/2018/10/18/what-is-continuous-intelligence/#6c735c317d25.
Pisano, Gary P. “The Hard Truth About Innovative Cultures.” Harvard Business Review, Feb. 2019, pp. 62–71.
Phillips, Alexa. “4 Ways to Democratize Analytics and Data Science for Continuous Intelligence.” The TIBCO Blog, TIBCO, 12 Nov. 2019, www.tibco.com/blog/2019/11/11/4-ways-to-democratize-analytics-and-data-science-for-continuous-intelligence/.
Ransbotham, Sam, et al. “Winning With AI.” MIT Sloan Management Review, 15 Oct. 2019, sloanreview.mit.edu/projects/winning-with-ai/
“The OODA Loop Explained”. Coolfire. https://www.coolfiresolutions.com/blog/ooda-loop-explained/
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