Organizing Data-driven Companies

This chapter examines why and how digitalization is pushing organizations to adopt team-based structures, greater transparency, and agile work cultures. I draw attention to a shift in focus from efficient routines towards greater adaptability, and elaborate the paradoxical effect that digital data has in both eliminating and generating coordination needs within corporations. The chapter introduces six basic approaches to organizing and discusses their relative advantages and disadvantages in leveraging digital technologies. I elaborate how focus on agility has redefined the basis of control in organizations, called into question the prevalent ‘culture of secrecy’ in corporations, and eroded traditional sources of authority. The chapter concludes by discussing how modularity has reshaped the network of relationships around corporations and increased the strategic importance of digital ecosystems and platforms.

The Data Imperative

This chapter examines digitalization as a set of new normative ideals for managing and organizing businesses, enabled by new technologies. The data imperative consists of two mutually reinforcing goals: the pursuit of omniscience—the aspiration of management to capture the world relevant to the company through digital data; and the pursuit of omnipotence—an aspiration of managers to control and optimize activities in real-time and around the world through software. The data imperative model captures a self-reinforcing cycle of four sequential steps: (1) the creation and capture of data, (2) the combination and analysis of data, (3) the redesign of business processes around smart algorithms, and (4) the ability to control the world through digital information flows. The logical end-point of the data imperative is a ‘programmable world’, a conception of society saturated with Internet-connected hardware that is able to capture processes in real time and control them in order to optimize desired outcomes.

Digital Transformation

The introductory chapter to the book The Data Imperative examines how technological advances together with a new managerial mindset are driving digital transformation. While early business information systems were often self-contained and designed to solve specific problems, contemporary systems are highly interconnected and integrated. Corporations can use data flows to coordinate diverse processes and activities across organizational and geographic boundaries. The chapter explains how digital transformation involves a systematic shift from predominant reliance on human knowledge and skills to digital data flows and smart algorithms. Artificial intelligence techniques, such as generative adversarial networks and advanced natural language processing, and 5G wireless technologies create new opportunities to replace human routines with algorithmic processing. Data will continue to break down organizational silos, enable deeper collaboration across company boundaries, and speed up the development of new services.

The Promises and Pitfalls of the Data Imperative

This chapter charts the future developments of data-driven businesses and digitalization. The chapter first elaborates a vision of the artificially intelligent organizations that utilize AI technologies, such as natural language processing, to capture the skills and knowledge of their employees. This will allow corporations to automatically orchestrate expert tasks to increase productivity and, hopefully, meaningfulness of knowledge work. Looking at the potential risks of ongoing digitalization, the chapter examines ‘optimization traps’, a set of organizational myopias that may arise from increasing reliance on algorithmic processing and smart automation. The chapter concludes by examining the new skills and attitudes managers and professionals are likely to need to remain relevant in the digital workplace, arguing that current use of data to create ‘insights’ may soon be outdated.

Artificial Intelligence and Human Expertise

This chapter examines in detail the structure and functioning of work routines that involve collaboration between human experts and advanced algorithms. Despite the intuitive complementarity of human intelligence and AI, it is often challenging to combine the two effectively. Building on case company examples, such as Finnair and Stich Fix, the chapter introduces a typology of different human–algorithm hybrids and a framework for understanding challenges and pitfalls in in human–AI interactions. I conclude by suggesting four idealized approaches to solving these issues and helping companies take full advantage of digital technologies.

Complexity and Modularity

Modularity is the single most important concept related to software and digitalization, and one that every manager and many experts should understand. This chapter elaborates modularity as a central response to complexity that relates to the design, production, and operation of technological systems. Digital technologies increase modularity by facilitating the creation of clear interfaces between sub-systems and processes. This, in turn, allows complex systems to be split into autonomous parts that can be developed and installed independently. Modularity allows companies to scale up their activities more quickly and cheaply, while also facilitating innovation both within modules, by eliminating interdependencies that hamper development efforts, and at the architectural level, through the creative recombination of pre-existing modules.

Algorithmic Management of Humans

This chapter examines software-based control of employees and work tasks. To leverage digital data and optimize the productivity of human workers, companies have piloted algorithmic management systems that survey, incentivize, instruct, and sanction human workers, commonly through their smartphones. We have entered an era in which a substantial portion of the workforce will no longer have a human supervisor or manager to whom to report, constituting the ‘Uberification’ of the labour market. While the first generation of algorithmic management was designed for the narrow optimization of the efficiency of customer-facing business processes, I suggest that the next generation of algorithmic management could—and should—attend more carefully to the employee experience. There are clear incentives to develop algorithmic management that make work more meaningful and motivating.

Strategic Priorities in the Digital Era

This chapter examines how digitalization influences companies’ pursuit of competitive advantage. The chapter makes the case that the creation and use of data has led companies across diverse industries to embrace three broad strategic priorities, using data for: (1) constant optimizing, (2) experimenting to diversify to new products and services, and (3) building interactive digital relationships with customers, suppliers, and partners. While these priorities are not unique to data-savvy corporations, they benefit directly from real-time data and smart automation. As digitalization often erodes firms’ ability to exploit unique resources or capabilities for sustained profits over time, they pursue greater agility and innovativeness. To accomplish this, companies invest in digital infrastructures that enable them to identify and react to environmental changes more rapidly.