Principles of a Hybrid Intelligence Framework for Augmented Analytics

Principles of a Hybrid Intelligence Framework for Augmented Analytics

Alexander P. Ryjov
DOI: 10.4018/978-1-7998-9016-4.ch003
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Analytics is a key success factor for any business in the competitive and fast-changing world. Using good analytics, people, business, social, and government organizations become capable of making good decisions. With augmented analytics, human expertise actually becomes more crucial than ever. This chapter aims to introduce the hybrid intelligence approach by focusing on its unique analytical capabilities. The state-of-the-art in hybrid intelligence, symbiosis and cooperative interaction between human intelligence and artificial intelligence in solving a wide range of practical tasks, and one of the hybrid intelligence frameworks, a human-centered approach for evaluation and monitoring of complex processes, have been considered in this chapter. The chapter could be interesting for analysts and researchers who desire to do analytics with more intelligence.
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Analytics has become ubiquitous in our day-to-day life. Why do people spend time and effort on analytics? People want to make good decisions based on sound analytics. One of the latest data and analytics trends that has gained considerable traction these days is Augmented Analytics. Gartner coined the term in 2017: “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment” (Gartner, 2017).

Big Data, Machine Learning, Artificial Intelligence, and other technologies allow improved analytics for making more good decisions, it is true. However, the complexity and volume of data every business accumulates is a common challenge for those that need to make decisions. Data is a continuous and constantly growing asset, and it can be particularly challenging for decision-makers.

With Augmented Analytics, human expertise becomes more crucial than ever. The following comment good enough describes the situation “Analytics is at a critical inflection point. Businesspeople are awash with data yet struggle to determine what’s most important and the best actions to take. Augmented analytics addresses this growing challenge, using AI and machine learning techniques to augment human intelligence to present to businesspeople the insights most important to them, and drive data-driven decisions”. (ThoughtSpot, 2021).

The author agrees with the vision “Ultimately, Augmented Analytics will strip out the dull, robotic processes involved in BI and empower employees to focus on being human” (MHR, 2021).

This chapter focuses on when a person (analyst or decision-makers) is at the center of the analytics framework and collaborates with computer analytics tools. This situation is typical for many organizations and is not so well-studied, like analytics based on Big Data, Machine Learning, Artificial Intelligence.

An example of such a situation could be project management. There is usually a measurable project goal and tools for collecting various metrics describing the current situation with resources, time, and scope. Based on such metrics, the project's current status is analyzed, and measures are developed to achieve the project goal in the best way. Within the framework of the proposed approach, these processes (evaluation of the project's current status and the choice of measures for optimal achievement of the goal or expected status) are solved in an automated way. Additional examples are discussed in the HYBRID INTELLIGENCE FRAMEWORK FOR AUGMENTED ANALYTICS section.

Key Terms in this Chapter

Fuzzy Sets: It is a set of elements that have no strict boundaries. Examples of such sets are sets of “young people”, “expensive cars”, “successful companies”, etc.

Augmented Intelligence: Human-computer systems allow us to combine human intelligence strengths (for example, intuition) and computer’s computational power. Augmented intelligence enhances and scales human expertise; AI systems attempt to replicate human intelligence.

Fuzzy Logic: Fuzzy Logic is a form of mathematical logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

Hierarchical Systems: It is a special type of systems where elements (objects, names, values, categories, etc.) are represented as being “above”, “below”, or “at the same level as” one another. A hierarchy can link elements either directly or indirectly, and either vertically or diagonally.

Artificial Intelligence (AI): AI is the simulation of human intelligence processes by computer systems. Particular applications of AI include text and speech recognition, machine vision, learning by examples, etc.

Evaluation and Monitoring: It is a process that helps improve performance and achieve results for particular processes. Its goal is to improve current and future management of outputs, outcomes, and impact. It establishes links between the past, present, and future actions.

Measurement: Measurement is the assignment of a value (number, symbol, etc.) to a characteristic of an object or event, which can be compared with other objects or events.

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