Making Complexity Usable
Insight in business and technology is not predictive in nature. Rather, insight represents the process of attempting to connect patterns to possible outcomes. Organizations such as Harvard Business Review and MIT Sloan Management Review continually frame insight as applied knowledge (or "the understanding of something"). The goal of insight is not to pursue innovation for the sake of pursuing innovation, but to assess how changes in technology affect strategy, structure, and accountability within an organization.
This section addresses this type of thinking. This section explores the concepts of digital strategy, organizational change, automation ethics, and data governance as related topics. Each of these areas of focus represents a broader inquiry into how organizations can evolve without losing cohesiveness. Strong insight does not make complexity disappear; instead, strong insight makes complexity usable.
Digital Strategy as a Discipline vs. a Plan
Digital strategy is often viewed as a plan that is developed and implemented as part of a defined timeline. As a result, many organizations view digital strategy as a one-time activity that is completed when the plan is developed. Digital strategy operates as a discipline that continues to develop in conjunction with the development of the organization.
According to McKinsey & Company, digital strategy is the process of aligning technology investment with business objectives, operating models, and capabilities. Aligning technology investment with business objectives, operating models, and capabilities is a process that requires constant adjustment and refinement as opposed to developing a new plan periodically.
In order for an organization to be successful in leveraging digital technologies, organizations cannot implement discrete projects in isolation. Instead, organizations must tie technology investments to core objectives. For example, a customer data platform may be aligned to service design. Similarly, organizations may need to establish ties between automated systems and workforce planning. Additionally, organizations must tie analytics projects to specific decision-making authority within the organization.
Additionally, digital strategy also involves the ability to exercise restraint. While organizations should be willing to adopt emerging technologies, organizations must first assess the relevance of the emerging technology prior to determining whether the emerging technology should be adopted. Insightful organizations ask themselves whether a given emerging technology will improve the clarity, speed, and/or reliability of processes and/or operations. If an emerging technology will not improve the clarity, speed, and/or reliability of processes and/or operations, then the emerging technology should remain discretionary.
Common Characteristics of Effective Digital Strategy
A sustainable model includes the following:
- A limited number of objectives that are set by leaders and used to evaluate whether specific technology purchases align with their goals (and therefore are worth spending money on).
- The various departments in the organization make joint technology purchasing decisions; the various departments do not operate independently when making decisions about technology.
- The success of an investment is evaluated based upon measurable outcomes (such as cycle time, level of service, or resilience) rather than solely upon how much technology was purchased or utilized.
- The strategic direction of the organization is continually evaluated for changes in the market, regulatory environment, or consumer behavior.
Sustainable models reduce fragmentation and create a common vocabulary regarding evaluation and measurement of performance.
Organizational Design and Change Limitations
Technology can expose weaknesses in organizational design. Existing approval procedures typically cannot keep pace with the speed of new technologies. Information may become available to groups of people who previously had to rely on hierarchical reporting.
The process of organizational change is not simply a reaction to technology, but is actually necessary for technology to be successful. According to research by Boston Consulting Group, organizations that are resistant to change are more likely to experience failure related to their digital initiative than organizations experiencing technical problems.
Change requires an examination of organizational structure, the incentive system and communication channels. While formal organization structures (charts) are important, it is informal behavior and decision-making that ultimately determine what happens.
Organizations that are effective at managing change include engaging employees in the change process early in the process, explaining to them why the organization is undergoing change and how their role in the organization may change, providing training to employees prior to the onset of change, and establishing feedback mechanisms that allow employees to identify potential problems before frustration builds.
Characteristics of Organizations That Experience Successful Change
The characteristics listed below represent an understanding that change is a social issue before it is a technical one.
- Change is framed as an adaptation of how work is done in the organization, rather than a top-down directive for change.
- Managers have the ability to take the organizational strategy and apply it to the local context of their team.
- Employees are able to provide feedback on the change process before feelings of frustration build.
- Employees view learning as a part of their job, not something additional that they have to do.
Automation and Its Ethical Boundaries
The current state of automation technology has evolved past simple repetitive tasks. Today's machine learning systems are being utilized to make important business decisions including; hiring, lending, pricing, and employee performance evaluations. Although this evolution in technology is improving the speed and efficiency of decision-making processes; it is also creating new ethical dilemmas.
Organizations will no longer be able to treat automation technologies as simply neutral technologies since the biases and decisions built into those technologies are reflective of the human assumptions and values that were used when designing them. The World Economic Forum has provided reports that support the notion that automation technologies have the potential to continue to perpetuate and expand upon existing biases unless they are properly designed and monitored.
Transparency is a key element of developing ethical automation technologies. In order for stakeholders to develop trust in the technology, they need to know exactly how the technology makes decisions and which data is being used to make those decisions.
Transparency also needs to include accountability for the outcomes generated by automation technologies. Organizations cannot simply say "it was the technology" when there is a negative outcome.
Ignoring ethics while building automation technology does not limit an organization's ability to innovate. Rather, ignoring ethics in the development process limits an organization's ability to establish trust with its customers and partners.
Organizations that choose to ignore ethical considerations in their automation technologies will likely experience significant reputational and regulatory risks. On the other hand, organizations that consider ethics during the design phase of their automation technologies will likely experience fewer challenges establishing credibility.
Important Ethical Considerations for Automation Technologies
As previously stated, the ethical considerations outlined above are consistent with guidelines established by organizations such as the Organization for Economic Cooperation and Development (OECD) and the European Commission.
- Automated systems should be capable of being audited so that the decisions made by the system may be reviewed and justified.
- Bias testing should be performed regularly to determine whether or not any unintended biases exist.
- Human oversight should remain in all high-impact decisions.
- Clear governance structures should be developed to outline who is responsible for the outcomes generated by automation technologies.
Building Data Governance as a Strategic Competency
Many organizations collect and store large amounts of information, however, very few organizations effectively govern the data. Data is frequently referred to as an organizational asset, however, many organizations do not effectively own or manage their data. Information is frequently collected and stored without a definition of ownership, definitions of the data vary across different systems and security policies regarding the data are not always consistent.
When organizations effectively implement a data governance strategy, they are able to directly address these issues. As defined by Gartner, effective data governance provides authority and control over data management activities. Effective data governance outlines who is authorized to access data, how data may be used, and how data quality is maintained.
Effective data governance is not restrictive, rather it allows for confidence in using data. When leaders have confidence in the accuracy and consistency of their data, they will use it more consistently. Similarly, when employees clearly understand the data governance policies and procedures, they will spend less time resolving discrepancies and more time focusing on core business functions.
An effective data governance policy will strike a balance between providing the necessary flexibility to support experimentation and maintaining the discipline required to ensure compliance. An effective data governance policy will provide the necessary structure to support experimentation and encourage innovation while still maintaining the discipline required to ensure compliance.
Connecting Trends to Practical Outcomes
Business and technology trends are usually introduced as conceptual ideas. Artificial intelligence, remote work, platform ecosystems, etc. are presented at an abstract level. Insight takes these concepts and provides real-time connection to the actual decisions being made.
Remote work is more than just a policy choice. The way you manage employees’ performance will be impacted by remote work as well as onboarding new employees and collaboration norms. AI is more than one capability; it encompasses data readiness, model governance and workforce impact.
Insight looks at where trends intersect with constraints (regulatory environment, industry dynamics and organizational maturity) rather than looking at trends as independent concepts.
Insight also helps avoid superficial adoption and instead replaces urgency with relevance.
Insight as a Shared Practice
Insight does not belong to one person or department. Insight emerges through dialogue. When diverse perspectives are considered, blind spots become visible.
Organizations that foster thoughtful discussion tend to make decisions that are more resilient than those that do not. They value dissent. They test assumptions and they document reasoning.
To develop this practice takes psychological safety. Employees must feel comfortable expressing concerns and leaders must signal that questions are welcome.
Over time, insight becomes embedded in how organizations make decisions.
The Role of Research and Evidence
Strong insight is based on evidence. Longitudinal research, cross-industry analysis, case study analysis and other research methods can be used to create the context for your insight.
While evidence may assist leaders with making judgments, evidence alone will never completely replace leadership judgment. Leaders use the evidence-based findings, and apply them to their organizations' objectives, and limitations.
Value is created when leaders synthesize evidence-based findings into real-world experiences.
From Insight to Action
Unless an organization is able to translate its insight into action, there is no real value to having developed an insight. To do so, the organization must prioritize the development of actionable strategies that are derived from its findings.
Not all insights are immediately actionable. However, some will have a significant impact on future decisions made within the organization.
When insights are documented and referred back to over time, organizations benefit from their prior experiences and learnings. Organizations also begin to recognize patterns of success or failure across different initiatives.
Over time, this continuous recognition of lessons learned from previous initiatives develops a strong institutional memory.
Directional Clarity in an Age of Constant Change
Research-based insights into business and technology enable organizations to intentionally navigate increasingly complex environments. Insights into these areas create a cohesive view of how to strategically utilize digital, organizational change, automation ethics, and data governance.
Insights enable clear-thinking leaders to cut through rapidly changing and emotionally charged narratives surrounding trends in technology and business. Instead of simply reacting to the next trend, leaders who possess insights, think critically about trends and develop practical applications and outcomes.
As uncertainty increases, the ability to stabilize an organization's decision-making process through insight and clear thinking becomes critical. While an insight may not offer absolute certainty, it provides a basis for directional clarity in times of increasing uncertainty.