How Adaptive Intelligence Makes Analytics Smarter

While the sources, and consequently the volume of big data continue to multiply due to the emergence of technologies like IoT, the need for making better use of the data that is gathered is also becoming more apparent. To fulfill this need, adaptive intelligence, which adds to the capabilities of traditional analytics can enable businesses to further automate their business intelligence tools.

Considering their increasing adoption and propagation across nearly every industry, the eventual confluence and the growing interdependence of big data and AI is hardly surprising. While big data has been around for a while now, applications of the technology, until recently, failed to make the impact they were expected to upon their emergence. In fact, it was estimated that 85% of big data initiatives had failed to deliver their expected results. While the reasons for the underwhelming impact of big data initiatives are numerous, not having the right tools and people to clean, analyze, and proactively seek and solve problems has been a major part of the problem. In recent years, the use of AI to clean -- and solve problems using -- the enormous body of data collected is making applications of data analytics more effective. However, when it comes to making decisions, there is still a large dependence on humans. Among the numerous ways that AI is enabling the effective utilization of big data by minimizing the role of humans, one way is through the recently emerged concept of adaptive intelligence.

Adaptive intelligence is a subset of artificial intelligence that goes beyond just converting inputs into insights to enable action. It helps in delivering the most contextually relevant output whenever required, by training. While there is little doubt regarding the superiority of analytics and artificial intelligence in term of computational capability, there is still a lot left to be desired in terms of making artificial intelligence actually “intelligent”. Thus adaptive intelligence, while eliminating all the limitations that come with human-driven decision-making, incorporate certain cardinal elements that can only be achieved by virtue of human involvement. Read on to know how critical business analytics is to the modern-day enterprise, and how adaptive intelligence takes it to the next level.

Business Analytics: Solving Problems with Data

Business analytics is the process of using the data gathered by a business pertaining to its market, its people, and processes, also known as business intelligence to improve decision-making and business processes. The use of business analytics to drive problem-solving and growth has become the standard across different industries, from agriculture and healthcare to entertainment and tourism. Businesses are increasingly becoming reliant on data analysis to drive both their operational and strategic decisions.

A manufacturing supply chain, for instance, generates terabytes-worth of data or business intelligence through its operations. The data includes external information such as that regarding the product demand, the supply of materials, regulations, competition, customers, and economic policies among others. It also includes internally generated data such as that related to the manufacturing activities and equipment, product design and development, inventory, vendors, and employees. Both the internally and externally generated data, if leveraged appropriately, can enable the manufacturer to make decisions that can improve their offerings, make their processes more efficient, maintain and grow their customer base, respond to changes quickly and effectively, and maximize profits.

For example, the data generated by the manufacturer’s main production facility can point to a flaw in process planning that leads to significant avoidable expenses. They can use the data to make better process planning decisions and maximize process efficiency. Business analytics is also becoming a staple in the service sector, where every industry, such as entertainment, finance, and healthcare is using analytics in innovative ways. Service sector organizations use business analytics to improve customer experience, boost customer satisfaction levels and ensure the retention and growth of their customer base.

Adaptive Intelligence: Making Analytics Smarter

While the heavy data processing and computational capabilities offered by analytics tools have been a major contributor to the effectiveness of business analytics, it also depends on:

• high data quality achieved through data cleansing, normalization, elimination of biases, and better data gathering practices, along with
• the decision-making that is done by humans based on data analysis.

The existing tools used for business analytics offer high-speed, high-volume data processing and computational abilities to turn data into usable insight which human employees, owners, and other decision-makers draw upon to carry out business activities. Thus, the effective use of business analytics requires a mix of machine and human capabilities. Adaptive intelligence combines these capabilities to offer businesses the ability to make the use of business analytics smarter and easier to use.

Adaptive intelligence not only analyzes large volumes of data to deliver valuable insights whenever requested by organizational personnel but also adapts the information based on specific situations. It ensures that the right information, in the right form, is delivered to the right people at the right time.

Adaptive intelligence, just like business analytics, helps businesses make better decisions. But, it makes the process of decision-making faster, more adapted to individual cases, and with minimal effort from the decision-maker. While business analytics focuses on data analysis and delivery of insights, adaptive intelligence adds the focus on context and relevance. This makes every instance of data analysis and decision-making faster and simpler for the stakeholders which culminates in the enterprise benefiting massively in terms of productivity, effective responsiveness, and, consequently, financial profitability.

While business analytics gives actionable insights when requested, adaptive intelligence proactively and autonomously provides insights when needed. It makes the process of determining the actions to be taken easier by simplifying the decision-making process. And like other AI systems, adaptive intelligence learns to perform analysis, make decisions, and prompt actions in better ways.

End-to-End Automation: Applying Adaptive Intelligence

The concept of adaptive intelligence is already gaining traction among business and tech leaders as an upgrade on business analytics. It can be used by businesses to make the process of sharing information across the enterprise easier. Thus regardless of where information is needed in an organization, be it in the accounting department or the customer service vertical, adaptive intelligence can deliver the requisite insights proactively by sensing the context. The penetration of adaptive intelligence will only increase with time as AI becomes smarter and technologies like the Internet of Things (IoT) become widely within organizations and across industries. Using IoT’s sensory and actuating end-points, adaptive intelligence can become more responsive through greater accuracy in data analysis and greater control over outcomes.

Industries like healthcare can greatly benefit from adaptive intelligence as information sharing, accurate data analysis, and prompt response to situations as they emerge are vital to its operations. Adaptive intelligence-based health technology is already being developed and tested to the benefit of all stakeholders in the healthcare industry. Eventually, adaptive intelligence may completely eclipse and replace business analytics applications in all industries. It would be hardly surprising, given its obvious benefits over traditional analytics. As the modern-day enterprise transitions further towards end-to-end integration, the adoption of adaptive intelligence will definitely prove to be a key catalyst in achieving that.