As the volume and complexity of data continue to grow, organizations will increasingly rely on Continuous Intelligence (CI) to make sense of it all. Continuous Intelligence (CI) will give these businesses a significant advantage in terms of efficiency, innovation, and customer satisfaction. According to Gartner, by 2025, vendors who can leverage Continuous Intelligence to harmonize action across organizational silos will expand their revenue three times faster than their peers. [1] Therefore, Continuous Intelligence (CI) is a powerful tool for analytics and a strategic enabler for organizational transformation. It enables businesses to transform any process with the most current, relevant data, guide and automate actions at the right moment, and accelerate business outcomes.
Without Continuous Intelligence (CI), organizations are forced to rely on outdated data and manual analysis, which can lead to delayed and reactive decision-making. This reactive approach can result in missed opportunities, poor resource allocation, and an inability to adapt to changing market conditions. Without real-time insights, organizations struggle to allocate resources effectively, which can cause them to invest in projects with low potential returns or fail to identify areas where resources are needed most. This can lead to wasted resources and missed opportunities. Furthermore, organizations miss out on crucial insights that can help them identify trends and patterns in data. This lack of awareness of emerging threats or opportunities puts the organization at a disadvantage. Additionally, organizations lack a deep understanding of their customers’ needs and preferences, which can lead to poor customer service, dissatisfaction, and churn. Moreover, organizations are more likely to experience operational risks, such as fraud, security breaches, and supply chain disruptions. Such risks can damage the organization’s reputation and lead to financial losses. Organizations also struggle to adapt to changing market conditions, technological advancements, and consumer behavior, leading to obsolescence and loss of market share. This can hinder their growth and competitiveness. In addition, organizations may miss out on opportunities to innovate and develop new products, services, or processes. The lack of a centralized platform for sharing knowledge and insights across different departments and teams can lead to silos of information and hinder collaboration. By struggling to measure their performance effectively, it can be difficult to identify areas for improvement and make data-driven decisions. This, in turn, prevents organizations from fostering a culture of data-driven decision-making, causing them to rely on intuition and guesswork rather than evidence-based decisions.
Founded in 2005, Change Machine builds financial security for low-income communities through people-powered technology. Over 8,000 practitioners have used Change Machine’s platform to amplify their impact, including putting USD 45 million in the pockets of their customers. Financial insecurity is a daunting reality that people with low incomes must navigate. Change Machine, a nonprofit tech organization, tackles these issues head-on. At the beginning of 2020, Change Machine developed a set of standards to evaluate fintech products for affordability, inclusivity and safety, as well as how each product aimed to build financial security. The first iteration of the recommendation engine, called Marketplace Relief, was launched to mitigate financial insecurity amidst the unfolding economic recession resulting from the Covid pandemic. Criteria were created to filter relevant, vetted products and services to meet customer needs. If the needs were to boost savings and improve credit, for instance, the recommendation engine would recommend savings and credit products and services. Although the system worked well, the approach was limited. The recommendation engine also didn’t consider whether customers accepted or rejected the recommended products and services ― an indication of the feature’s impact. It was clear that the recommendation engine could be improved using AI data analytics. Change Machine engaged the IBM® Data Science and AI Elite team. IBM worked under the IBM Data and AI for Social Impact program, an apprenticeship collaboration in which IBM helps nonprofits use data science and AI to further their mission. To develop the data and AI models, the IBM team chose IBM Cloud Pak® for Data as a Service, which would link all data in a centralized data function. Developers used the IBM Watson® Studio solution with its AutoAI feature to ease development. The API-based IBM Cognos® Dashboard Embedded solution would power scalable dashboards. All tools reside within the IBM Cloud Pak delivered from IBM Cloud®. AI analysis of Change Machine’s data now powers the recommendation engine in Salesforce. The solution is so innovative that it was nominated for VentureBeat’s AI Innovation Award in the AI for Good category. With the former recommendation engine, customers actively used just 60% of fintech products recommended by their coaches. With the new version, the figure has risen to 98% ― indicating that the recommendations are more relevant. Another benefit stems from the recommendation engine’s connection to dynamic data about customers and fintech offerings. As this body of data is updated, so are the engine’s recommendations.[2]
Continuous Intelligence (CI) provides organizations with a continuous stream of real-time insights from various data sources, enabling them to make informed decisions that are aligned with overall organizational goals. This data-driven approach eliminates siloed perspectives and ensures that decisions are based on a comprehensive understanding of the market, customer behavior, and internal operations. It empowers organizations to anticipate and respond to customer needs proactively. By analyzing customer data and feedback in real-time, organizations can identify emerging trends, personalize interactions, and address customer issues promptly. This leads to increased customer satisfaction, loyalty, and ultimately, revenue growth. It helps organizations identify and eliminate inefficiencies across departments and teams. By analyzing data on resource utilization, project progress, and cost allocation, organizations can optimize resource allocation, reduce waste, and improve productivity. This leads to cost savings and a more efficient use of resources, further contributing to revenue growth. It facilitates a culture of innovation by providing organizations with the insights needed to identify new opportunities and develop successful products and services. By analyzing market trends, customer feedback, and internal data, organizations can identify unmet needs, validate new ideas, and accelerate product development cycles. It enables organizations to adapt quickly to changing market conditions and customer demands by continuously monitoring key metrics and analyzing emerging trends, organizations can proactively adjust their strategies, pricing, and product offerings to stay ahead of the competition.
The growing adoption of streaming analytics across various industries suggests that market leaders are increasingly recognizing the potential of Continuous Intelligence (CI). Its ability to make use of situational awareness in streaming data gives businesses a new tool that has not been available with traditional approaches that only mined historical data. It empowers market leaders to achieve success by detecting fraud in finance, enhancing customer experience in retail, and improving predictive maintenance in manufacturing and beyond. By embracing continuous improvement to analyze data and make real-time decisions, market leaders can set themselves apart in fiercely competitive, saturated markets. Market leaders are safeguarding their data usage with privacy and usage policy enforcement across all data and enabling users of all skill levels to access trusted data with tailored interfaces. 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. Enterprises can benefit greatly from a frictionless flow of data and the application of continuous intelligence powered by artificial intelligence. By turning data into actionable insights, businesses can increase their workflow efficiency. The applications of continuous intelligence are vast and can be implemented across numerous industries.
