Organizational Intelligence and Knowledge Analytics

This chapter introduces the concept of an intelligent organization in the context of the twenty-first-century knowledge economy. An intelligent organization is one in which individuals behave intelligently, work is grounded in intelligent methods and choices, and rich stocks of intelligence in the form of knowledge capital to support intelligence work and choices. Intelligence is defined as both a thing and attribute and behavior and way of working. The chapter also highlights examples of intelligent behaviors and or organizational pathologies. The chapter also highlights the importance of becoming aware of intelligent and unintelligent choices.

This chapter reviews traditional intelligence work, primarily how intelligence was perceived and conducted in the industrial economy. The review includes economic sectors with dedicated intelligence functions such as military, law enforcement, and national security. The review also includes secondary intelligence work in all other economic sectors. Looking across all these examples, the authors present a traditional life cycle model of intelligence work and highlight this traditional view of intelligence’s tactical and reactive approach. The chapter details the historical evolution and common intelligence elements in military, business, law enforcement, judicial forensics, national security, market, financial, medical, digital, and computer forensics.

In this chapter, the authors build upon the value and the gaps of the traditional model to propose a more strategic and comprehensive framework for designing and conducting intelligence work. The future of intelligence work in the knowledge economy requires a new approach. The new framework includes four primary intelligence capabilities, including design, analysis, automation and operationalize, and accelerate. The framework applies to any organization operating in any economic sector.

This chapter fills a significant gap in the intelligence literature and the knowledge sciences literature by aligning definitions and characterizations of knowledge capital as an essential intelligence source. The chapter also explains how knowledge capital might be leveraged in each of the four capabilities. The chapter presents a well-researched characterization of knowledge capital drawing from international scholars and practitioners’ work. The value of human capital, structural capital, and relational capital in intelligence work is explored.

This chapter focuses on design capability. The authors draw from the work of design models to define design for intelligence work. Design is presented as both a way of thinking and a way of working. This chapter breaks the design capability down to several critical activities, including environmental scanning, problem detection, discovery, problem decomposition and recomposition, brainstorming, critical thinking, problem definition, factor identification, hypothesis development, model building, and source identification blueprinting.

This chapter provides a detailed review of the Analysis capability. The authors draw from several disciplines to provide a definition and characterization of analysis suited to intelligence work. This chapter distinguishes clearly between analysis as a capability and analytics as a tool or a method. The distinction is essential for seeing and leveraging all of the activities involved in analysis, including Blueprint Interpretation, Analytical Method Selection, Model Construction, Source Collection, Source Organization, Source Curation & Cleaning, Testing, Interpretation, Results Assessment, and Model Documentation & Formalization. The chapter highlights the importance of analyzing the whole landscape, seeing all factors, and thinking strategically. This chapter also includes business stories and scenarios from the real world.

This chapter focuses on automating and operationalizing the intelligence models and solutions developed through design and analysis. The authors draw from project management, planning, and implementation practices to identify the activities involved in this capability. Automating is defined more broadly than simply adding technology to a solution. Instead, it includes all aspects of operationalizing a solution for business use, including establishing a business model, building an operating model, operationalizing and deploying the solution in business operations, monitoring the solution, reviewing feedback and anomaly detection, and finally training for sustainability. This capability focuses on deriving the maximum business value from a solution and taking steps to ensure that there is an excellent long-term business return on investment. This chapter also includes practical business stories.

This chapter focuses on accelerating the adoption and promotion of the solution across the organization. The authors define acceleration as achieving the maximum effective and appropriate use of the solution across the organization. This capability is strategic – it ensures that any new intelligence solution or activity is part of its business product line. The initial focus is on internal business products, but organizations are also encouraged to see new solutions as potential external products. To ensure the product is strategically leveraged, business managers, designers, and analysts must work with leaders to socialize solutions and encourage their adoption. It also means being open to adapting and calibrating the solution over time. This chapter also includes practical business stories.

This chapter explains how organizations can build intelligence capabilities into their everyday working environments. The definition of capacity building builds upon the organizational management and the strategic workforce development literatures. This chapter also derives essential guidance from another series focused on critical capabilities and capacity building. The authors highlight the role of a strong intelligence culture and learning in building intelligence capacity. Capacity building is achieved through short- and long-term efforts. This chapter also highlights the importance of balancing capacity building across everyday business operations and specialized intelligence functions.

This chapter focuses on the types of roles, responsibilities, and competencies essential to organizational intelligence. The authors draw upon earlier series authors’ important work (Drucker, 2012; Garcia-Perez et al., 2019; Lafayette, Curtis, Bedford, & Iyer, 2019; Reinhardt, Schmidt, Sloep, & Drachsler, 2011) to define competencies. The authors define four categories of intelligence competencies, including those suited to strategic roles, those that support specialized intelligence work, those that support embedded intelligence roles, and universal competencies that apply to everyone.

This chapter lays out a strategic approach that an organization might follow to grow its intelligence capacity and competencies. The authors identify the elements of a strategy, explain how the strategy might be translated to a plan, and finally aligned with specific methods. This chapter intends to help an organization shift from the traditional tactical and reactive approach to doing intelligence to a more forward-looking, proactive, and strategic approach. This chapter also calls out specific factors to address in drafting an organizational intelligence strategy.

This chapter focuses on common business challenges where intelligent choices and behaviors may lead to new and different outcomes. The business stories represent a wide range of economic sectors, types of organizations, and challenges. Each story highlights the role the framework plays in deriving and realizing an intelligent solution.

This appendix contains a template for a project plan to guide an organization by thinking about intelligent ways of thinking and working. The appendix explains how the plan is organized. Suggestions for how to use the project plan are also provided.