Richard L. Nolan: Managing the Crises in Data Processing

Source: “Managing the Crises in Data Processing” by Richard L. Nolan, Harvard Business Review, March-April 1979

Introduction

In the late 1970s, many companies were struggling with rapid growth in their data processing (DP) functions. This growth led to skyrocketing budgets and management challenges. Richard L. Nolan’s article addresses these issues by introducing a framework to help organizations understand and manage the evolution of their data processing activities. This summary aims to explain the main themes and important ideas in a simple and detailed way.

Key Themes

1. Exponential Growth and Budgetary Concerns
   • Rapid Increase in DP Budgets
     • Companies noticed that their data processing budgets were growing much faster than their overall company budgets.
     • For example, a company’s DP budget might have increased from $30 million in 1973 to over $70 million by 1979.
     • Executives were worried about this rapid growth and questioned whether they were getting enough value for the money spent.
   • Challenges for Executives
     • Many top managers did not fully understand why DP costs were increasing so quickly.
     • They struggled to see the direct benefits of these increased expenditures on data processing.
     • This led to frustration and a sense of crisis within organizations.
   • Quote from the Article
     • A vice president expressed his concern: “My budget has grown from $30 million in 1973 to over $70 million this year… providing suitable space and connections to our sprawling computer network.”
2. Evolutionary Stages of Data Processing GrowthNolan introduces a model that describes how data processing evolves within a company over time. Understanding these stages helps managers anticipate challenges and plan accordingly.
   • The Six Stages of Growth:
     1. Initiation
        • Characteristics:
          • Introduction of computers into the organization for basic tasks, such as payroll or billing.
          • Limited number of applications and minimal impact on overall operations.
        • Management Focus:
          • Experimenting with technology and exploring its potential uses.
     2. Contagion
        • Characteristics:
          • Rapid expansion of DP applications across different departments.
          • Enthusiasm leads to a proliferation of systems without much control or standardization.
        • Management Focus:
          • Meeting the growing demand from users and trying to keep up with requests.
     3. Control
        • Characteristics:
          • Recognition of the need for standardization and control over DP activities.
          • Implementation of policies to manage costs and system development.
        • Management Focus:
          • Establishing procedures, budgets, and control mechanisms to manage resources effectively.
     4. Integration
        • Characteristics:
          • Integration of separate systems into a cohesive whole.
          • Emphasis on data sharing, reducing redundancy, and improving efficiency.
        • Management Focus:
          • Developing integrated databases and centralized data management.
     5. Data Administration
        • Characteristics:
          • Formalization of data management practices.
          • Data is recognized as a valuable corporate resource that needs careful management.
        • Management Focus:
          • Implementing data governance policies and ensuring data quality and accessibility.
     6. Maturity
        • Characteristics:
          • Data processing is fully integrated into business strategy.
          • High efficiency in using data to support decision-making and gain competitive advantages.
        • Management Focus:
          • Continuous improvement and leveraging data for strategic benefits.
   • Importance of Recognizing the Current Stage
     • By identifying which stage a company is in, managers can better understand the challenges they face.
     • This awareness allows for more effective planning and management of DP resources.
3. Organizational Learning and Control
   • Adapting Organizational Structures
     • As data processing grows, companies need to adjust their organizational structures and management techniques.
     • This might involve creating new departments or redefining roles and responsibilities.
   • Balancing Flexibility and Control
     • Organizational Slack:
       • Refers to the amount of flexibility and freedom within the DP environment.
       • Too much slack can lead to inefficiency and lack of coordination.
       • Too much control can stifle innovation and responsiveness.
     • Finding the Right Balance:
       • Each stage of growth requires a different balance between control and flexibility.
       • Managers need to adjust their approach as the organization evolves.
   • Importance of Learning
     • Organizations must learn from each stage to prepare for the next.
     • Continuous learning helps prevent crises and promotes smoother transitions.
4. Shift in Management Emphasis
   • From Technical Focus to Strategic Focus
     • In the early stages, the emphasis is on the technical aspects of implementing data processing systems.
     • As DP matures, the focus shifts to strategic management of data resources.
   • Leveraging Data for Business Objectives
     • Managers need to understand how to use data processing to support decision-making and achieve business goals.
     • This involves aligning DP activities with the company’s overall strategy.
   • Restructuring the DP Organization
     • The transition to a strategic focus may require restructuring the DP department.
     • This could include new leadership roles, different reporting structures, and closer collaboration with other departments.
   • Quote from the Article
     • “This transition involves not only new management techniques but also establishing restructuring the DP organization.”
5. The Emergence of Data Resource Management
   • Data as a Valuable Resource
     • Recognizing that data is not just a byproduct of operations but a critical asset that can drive business success.
     • Proper management of data can lead to better decision-making and competitive advantages.
   • Developing a Data Resource Management Strategy
     • Companies should create a strategy for collecting, storing, managing, and using data effectively.
     • This strategy should be aligned with the company’s overall business goals.
   • Aligning Data Management with Business Strategy
     • Data resource management should support the company’s mission and objectives.
     • Ensures that data initiatives contribute to business success.

Important Facts and Ideas

1. Applications Portfolio
   • Analyzing Current Applications
     • Companies should regularly assess the applications they are using.
     • Determine if these applications are meeting current needs and contributing to strategic goals.
   • Aligning Applications with Strategy
     • Develop new applications that support long-term objectives.
     • Avoid investing in systems that do not add significant value.
2. Benchmarks for Each Stage
   • Evaluating Progress
     • Nolan provides benchmarks that help companies measure their progress in each stage.
     • These benchmarks can include metrics like budget allocation, number of applications, and user engagement.
   • Identifying Areas for Improvement
     • By comparing their performance to these benchmarks, companies can identify weaknesses.
     • Allows for targeted improvements to address specific issues.
3. Transition Points
   • Critical Junctures
     • Moving from one stage to the next can be challenging and may lead to crises if not managed properly.
     • Transitions require careful planning and change management.
   • Avoiding Crises
     • Understanding the characteristics of each stage helps anticipate potential problems.
     • Proactive management can prevent crises and ensure smoother transitions.

Guidelines for Action

1. Recognize the Growth of Data Processing
   • Acceptance of Inevitable Growth
     • Acknowledge that data processing will continue to grow due to technological advancements and increasing data needs.
     • Denial or resistance can lead to bigger problems down the line.
   • Proactive Planning
     • Develop strategies to manage growth effectively.
     • Anticipate future needs and invest in infrastructure and personnel accordingly.
2. Introduce Database Management Systems
   • Improving Data Integration and Efficiency
     • Transitioning to database management systems (DBMS) helps consolidate data.
     • Reduces data redundancy and inconsistencies.
   • Supporting Advanced Applications
     • DBMS allows for more sophisticated applications, such as decision support systems and analytics.
     • Enhances the company’s ability to leverage data for strategic purposes.
3. Plan and Control Data Resources
   • Establish Clear Policies and Procedures
     • Implement guidelines for data usage, security, and maintenance.
     • Define roles and responsibilities for data management.
   • Align DP Activities with Business Strategy
     • Ensure that data processing initiatives support overall business objectives.
     • Involve business leaders in planning and prioritizing DP projects.
4. Recognize and Embrace Emerging Information Technologies
   • Staying Informed
     • Keep up with new technologies that could benefit the organization.
     • Encourage a culture of innovation and continuous improvement.
   • Leveraging Technology for Competitive Advantage
     • Use emerging technologies to improve products, services, and operational efficiency.
     • Stay ahead of competitors by adopting beneficial innovations.

Conclusion

Richard L. Nolan’s article offers valuable insights into managing the rapid growth of data processing within organizations. By understanding the six stages of DP growth and the associated challenges, companies can better navigate the complexities of data management.

Key Takeaways:

• Understanding Growth Stages Helps with Planning
  • Recognizing which stage your company is in allows you to anticipate challenges and plan effectively.
• Balancing Flexibility and Control is Essential
  • Adjust management approaches to suit each stage, ensuring innovation and efficiency.
• Data is a Strategic Asset
  • Treat data as a valuable resource that can drive business success.
• Proactive Management Prevents Crises
  • Anticipate changes and adapt strategies accordingly to avoid potential problems.

Relevance Today:

Although Nolan’s article was written in 1979, the core principles are still applicable. Businesses today continue to face rapid technological changes and data management challenges. By applying these timeless insights, organizations can better manage their data processing functions and leverage data to achieve their strategic objectives.

Final Thoughts

Understanding and managing the growth of data processing is crucial for any organization aiming to succeed in today’s data-driven world. By following the guidelines provided by Richard L. Nolan, companies can turn potential crises into opportunities for growth and innovation. Remember, effective data management isn’t just about technology—it’s about aligning people, processes, and strategies to harness the full power of information.