What Data Analysts Can Learn from the Roman Empire

Subscribe to the Insights x Design Substack - https://insightsxdesign.substack.com/

In the realm of data analysis, we often look to cutting-edge technologies and modern methodologies to sharpen our skills. However, we can learn much by casting our gaze back through history – particularly to one of the most successful and enduring empires the world has ever known: the Roman Empire. At first glance, the connections might seem tenuous. But a closer look reveals striking parallels and valueable lessons that can enhance our approach to data in the 21st century.

1. The Importance of Standardization

The Roman road system, known as the viae Romanae, was a marvel of ancient engineering and planning. These roads were built to a consistent standard across the empire, with a uniform width, construction method, and even milestones to mark distances. This standardization allowed for efficient movement of troops, goods, and information across vast distances.

In the world of data analysis, standardization plays an equally crucial role. It manifests in various forms:

• Data collection methods. Just as Roman roads were built to a specific standard, data collection methods should follow consistent protocols. This ensures that data from different sources or time periods can be reliably compared.
• Naming conventions. Clear, consistent naming conventions for variables, datasets, and files are the equivalent of the Roman mile markers. They allow analysts to quickly navigate through vast amounts of data.
• Reporting formats. Standardized reporting formats, like the uniform construction of Roman roads, ensure that insights can be easily shared and understood across an organization.
• Data quality standards. Just as Romans had standards for the materials and techniques used in road construction, data analysts must establish and maintain standards for data quality and integrity.

The benefits of standardization in data analysis are numerous. It reduces errors caused by inconsistencies, speeds up analysis by eliminating the need to constantly "translate" between different formats or methodologies, and facilitates collaboration by ensuring everyone is "speaking the same language" when it comes to data.

However, like the Romans, who had to adapt their road-building techniques to different terrains, data analysts must also balance standardization with flexibility. The key is to create standards that are robust enough to ensure consistency, but flexible enough to accommodate the unique needs of different datasets or business contexts.

May we introduce Susan Walsh , an expert in all this.

2. The Power of Data Collection

The Roman census, conducted every five years, was a remarkable feat for its time, and it was more than just a head count. It was a wide-ranging gathering of demographic and economic data that included:

• Population counts
• Property ownership and valuations
• Livestock numbers
• Occupations of citizens
• Family structures

The parallels with modern data collection are striking.

• Broad in scope. Like the Roman census, effective modern data collection often needs to be wide-ranging, covering multiple aspects of an organization or system.
• Regular intervals. The five-year cycle of the Roman census mirrors the need for regular data collection in modern analysis. This allows for tracking changes over time and identifying trends.
• Purposeful collection. Romans didn't collect data for its own sake, but to inform specific decisions. Similarly, modern data collection should be driven by clear objectives and intended use cases.
• Data quality. Romans took the accuracy of their census seriously, with penalties for false declarations. This underscores the importance of data quality and validation in modern data collection.

The Roman census teaches us that good decisions are based on current, accurate data, and that the process of collecting this data, while sometimes arduous, is fundamental to informed analysis and strategy. It also highlights the importance of having a clear purpose for data collection and ensuring that the data collected aligns with the decisions that need to be made.

Modern data analysts can learn from the scale and ambition of the Roman census. While we have far more advanced tools at our disposal, the fundamental challenges of collecting comprehensive, accurate data across large, complex systems remain. By approaching data collection with the same level of seriousness and purpose as the Romans, we can ensure that our analyses are built on a solid foundation of high-quality, relevant data.

3. Long-term Thinking and Data Trends

The Romans built their empire to last, as evidenced by the durability of their architecture and the longevity of their influence. This long-term perspective is invaluable in data analysis. While it's easy to get caught up in day-to-day fluctuations, the ability to step back and identify long-term trends is crucial.

Ancient Roman architecture. Image from The Collector

Roman architecture serves as a powerful metaphor for long-term thinking in data analysis, in several ways.

• Durability. Roman structures like the Pantheon or the aqueducts have lasted for millennia. In data terms, this translates to creating robust data architectures and analytics frameworks that can stand the test of time.
• Scalability. Roman cities were designed to accommodate growth, with flexible layouts that could expand over time. Similarly, data systems should be designed with scalability in mind, able to handle increasing volumes and complexity of data.
• Functionality. Roman buildings were not just aesthetically pleasing, but highly functional. In the same way, data analysis should always serve a practical purpose, solving real problems or informing important decisions.
• Adaptability. Despite their durability, many Roman structures have been adapted for new uses over the centuries. This reflects the need for data systems and analyses to be flexible enough to evolve with changing business needs.

In practice, long-term thinking in data analysis involves a range of practices.

• Historical data preservation. Just as Romans preserved their history through monuments and writings, organizations need to prioritize the preservation and accessibility of historical data. This allows for analysis of long-term trends and patterns.
• Predictive analytics. Using historical data to forecast future trends is akin to how Romans used their understanding of history to plan for the future. Techniques like time series analysis and machine learning can help in identifying long-term patterns and making predictions.
• Sustainable data practices. Implementing data governance policies, data quality frameworks, and metadata management systems ensures that data remains usable and valuable in the long term.
• Strategic alignment. Ensuring that data initiatives align with long-term business strategies, rather than just solving immediate problems.
• Building for the future. Investing in flexible, scalable data infrastructure that can grow and adapt over time, much like the expandable design of Roman cities.

By adopting a long-term perspective, data analysts can create more value for their organizations. They can build systems and analyses that not only solve today's problems but continue to provide insights and value well into the future. This approach turns data analysis from a reactive task into a strategic asset, much like how Roman infrastructure formed the backbone of their empire for centuries.

4. The Art of Visualization

Roman architecture and art were not just aesthetic choices; they were powerful tools for communicating ideas and showcasing the empire's strength. The grand arches, intricate mosaics, and towering columns all told a story. This ancient approach to visual communication offers valuable lessons for modern data visualization.

Trajan’s column. Image from National Geographic. Consider these Key aspects of Roman visual communication and their parallels in data visualization.

• Monumentality. Roman structures like the Colosseum were designed to awe and impress. In data visualization, this translates to creating impactful, memorable visuals that effectively convey the importance of key insights.
• Narrative in art. Roman historical reliefs, like those on Trajan's Column, told complex stories through visual sequences. Similarly, effective data storytelling often involves creating a narrative flow through a series of visualizations.
• Symbolism. Romans used symbols to convey complex ideas simply. In data visualization, choosing the right chart types and visual elements can quickly communicate the nature of the data and key takeaways.
• Accessibility. Public art and architecture in Rome were meant to be understood by a wide audience. Likewise, good data visualizations should be clear and interpretable to both experts and non-experts.
• Detail and overview. Roman art often combined intricate details with grand overviews. In data viz, this principle is reflected in the ability to provide both high-level summaries and detailed drill-downs.

Applying these lessons more directly to modern data visualization:

1. Know your audience. Romans tailored their visual communication to different audiences – from common citizens to the elite. Data analysts must similarly adapt their visualizations to their audience's level of expertise and specific needs.
2. Choose the right medium. Romans used different media (sculptures, mosaics, paintings) for different messages. In data visualization, choosing the right type of chart or graph is crucial for effectively conveying your message.
3. Use color strategically. Roman art made strategic use of color for emphasis and symbolism. In data viz, thoughtful use of color can guide attention, group related data, and highlight key insights.
4. Balance aesthetics and functionality. Roman architecture was both beautiful and functional. Good data visualizations should be visually appealing while clearly conveying information.
5. Incorporate interactivity. While Romans couldn't make their art interactive, modern data visualization tools allow for user interaction. This can provide a more engaging and personalized experience, allowing users to explore data in depth.
6. Provide context. Roman art often included inscriptions or was placed in specific locations to provide context. In data visualization, providing necessary context through titles, labels, and annotations is crucial for understanding.
7. Iterate and refine. Romans refined their artistic and architectural techniques over centuries. Data analysts should similarly iterate on their visualizations, seeking feedback and continuously improving.
8. Combine cultiple Views. roman friezes often told a story through multiple scenes. In complex data analysis, using multiple linked visualizations (like in a dashboard) can provide a more comprehensive understanding of the data.
9. Use hierarchy and layout. The layout of Roman cities and the design of their buildings guided people's attention and movement. In data visualization, thoughtful use of layout, size, and positioning can guide the viewer's eye to the most important information.

By mastering the art of data visualization, analysts can transform complex datasets into clear, compelling visual stories. Like the Romans who used their art and architecture to communicate the power and values of their empire, skilled data analysts use visualizations to convey the insights and value hidden within their data. The ability to present data in a visually appealing and easily understandable format is often what transforms a good analysis into an influential one, driving real change and decision-making in organizations.

5. (Not) Learning from Failures

The complex interplay of factors that led to Rome's downfall offers a rich tapestry of lessons for those working in the data field today.

A bronze statue of Caesar. Image from Live Science. One of the primary factors in Rome's decline was overextension. As the empire grew, it became increasingly difficult to manage and defend its vast territories. This expansionist approach eventually stretched resources too thin, leading to vulnerabilities that were exploited by external threats. For data analysts, this serves as a stark reminder of the dangers of scope creep and overambition in data projects. It's crucial to maintain a balance between ambition and feasibility in data initiatives. Trying to analyze too much data, or drawing conclusions from insufficient data, can lead to unreliable results and misguided decisions. Just as Rome couldn't effectively govern all its conquered territories, analysts must be wary of taking on more data or broader scopes than they can effectively manage and analyze.

Internal corruption was another significant factor in Rome's decline. As the empire grew more complex, opportunities for corruption and mismanagement increased. This erosion of integrity from within weakened the empire's foundations. In the realm of data analysis, this underscores the critical importance of maintaining ethical standards and data integrity. Manipulating data to fit predetermined narratives, cherry-picking results, or allowing personal biases to influence analysis can all be forms of "corruption" in data work. Just as corruption undermined Rome's strength, these practices can erode the credibility and effectiveness of data analysis within an organization.

Economic troubles, including inflation and the debasement of currency, played a significant role in Rome's decline. The empire's financial mismanagement offers lessons in the importance of economic literacy and sound financial analysis for data professionals. Understanding the broader economic context of data, particularly in business settings, is crucial.

Perhaps one of the most significant factors in Rome's fall was its failure, in the end, to adapt to changing circumstances. As new threats emerged and the world around them changed, many Roman institutions remained rigid and resistant to change. This inflexibility ultimately contributed to the empire's downfall. For data analysts, this serves as a powerful reminder of the need for adaptability and continuous learning. The field of data analysis is rapidly evolving, with new technologies, methodologies, and best practices emerging constantly. Analysts who fail to stay current, or who become too wedded to particular tools or approaches, risk becoming as obsolete as Rome's outdated military tactics in the face of new threats.

***

To sum up, while the tools and techniques of data analysis are modern developments, many of the underlying principles have stood the test of time. By looking to the successes (and failures) of the Roman Empire, we can gain valuable insights into how to collect, manage, analyze, and communicate data more effectively. As we build the data infrastructures and analytical methodologies of the future, we would do well to remember the lessons of the past. After all, if our data strategies can demonstrate even a fraction of the longevity and impact of Roman innovations, we will have achieved something truly remarkable in the world of analytic

Subscribe to the Insights x Design Substack - https://insightsxdesign.substack.com/