When you think of Japan, what comes to mind? Fabled Mount Fuji towering over Honshu? The neon-lit streets of Tokyo? The tranquil temples of Kyoto? Like everyone else, you probably picture Japan as consisting of its four largest islands (Honshu, Hokkaido, Kyushu, and Shikoku) and perhaps a few sprinkles of various smaller ones scattered in the surrounding waters.
But here's something that will totally surprise you: Japan recently discovered it has more than double the number of islands it ever knew. Yes, you read that right. For nearly four decades, officially, Japan recognized 6,852 islands in its territory. But in 2023, using high-tech digital mapping technology, the Geospatial Information Authority of Japan reported the actual figure to be a staggering 14,125 islands.
This discovery wasn’t the result of volcanic activity creating new landmasses or the sea level rising high enough to reveal hidden atolls. These islands were there all along; we just couldn’t see them properly with the paper maps and rudimentary measuring tools of the 1980s.
This finding resonates with me as a data management expert. How often do organizations think they need to manage their data only to discover there's much more going on under the hood?
Just as Japan’s paper maps led to small islands being incorrectly grouped together, many organizations unknowingly consolidate or overlook valuable data assets, missing the bigger picture. And here’s the kicker: Gartner estimates that poor data quality costs organizations an average of $12.9 million per year. That’s not just a rounding error; it’s more like a financial hemorrhage caused by unseen data islands.
So, let’s explore four essential tenets of data management that this fascinating discovery illuminates, each introduced with a rhyming keyword to make them stick.
Just as Japan’s recent survey employed advanced digital mapping to reveal previously uncounted islands, organizations need a comprehensive strategy to identify and catalog their data assets.
Key Insight: Planning your data management approach goes beyond knowing what data you have to encompass implementing the right tools to discover what you might be missing.
If Japanese cartographers had continued relying on paper maps, they would still be working with half the picture. Similarly, organizations that are using outdated data management software are likely missing out on meaningful insights hidden in their data landscape. In a report by Forrester Research, 72% of businesses have not moved beyond manual methods of locating data, resulting in inefficiencies and blind spots.
Breadth: Identifying All Data Sources Across the Organization
Achieving true breadth in data discovery means casting a wide net across every corner of your organization. Beyond the obvious databases and spreadsheets, uncovering shadow IT systems, departmental file shares, cloud storage buckets, and informal data exchanges between teams is essential. An integrated approach means not ignoring silos of information because they could contain valuable business intelligence. Think of it as archaeological excavation: while artifacts at the surface yield preliminary findings, the best insights will be found in the yet-to-be-excavated layers beneath.
Depth: Understanding Relationships Between Data Elements
Depth transforms raw data into meaningful intelligence by revealing how different data points interact. It's the difference between knowing customer names and understanding how purchase histories connect to support tickets, which then influence product development cycles. This dimensional thinking helps prevent the "flat data" syndrome where information exists in isolation, like unconnected islands in an archipelago. When you map these relationships, you create navigational charts that turn data from static facts into dynamic business narratives.
Height: Recognizing the Business Value of Each Data Asset
Height gives you the strategic vantage point to assess which data deserves your limited resources. Some datasets are like mountain peaks (i.e., immediately visible and valuable) while others resemble rolling foothills with potential yet to be developed. This dimension requires asking tough questions: Does this data directly impact revenue? Could it mitigate risks or reveal untapped markets? Evaluating data through this lens enables you to create a prioritization framework that aligns with organizational goals, ensuring your data investments deliver maximum ROI.
"You can’t manage what you can’t measure—and you can’t measure what you can’t see."
A major hospital system discovered 3.2 million unindexed patient records during a digital transformation initiative. By implementing automated scanning tools, they:
✦ Reduced diagnostic errors by 22%
✦ Improved treatment outcomes by correlating previously siloed data
✦ Saved $4.5 million annually in redundant testing costs
Lesson: Modern data discovery isn’t a luxury—it’s the foundation of effective data management.
The revelation that only 400 of Japan’s 14,125 islands are inhabited tells us something profound about data storage and utilization.
Key Insight: Every uninhabited island holds potential value—whether for conservation, research, or future development. Similarly, every piece of data in your organization has potential worth, even if it’s not actively being used.
Accessibility: Data Must Be Findable and Retrievable
True accessibility goes beyond permission settings to involve creating intuitive pathways to data. Imagine a library where books are perfectly cataloged but shelved in random order. Similarly, data needs intelligent organization systems with clear metadata, searchable tags, and user-friendly interfaces. The goal is to minimize the "data scavenger hunt" phenomenon where employees waste hours searching for information that should be at their fingertips. When done right, accessibility turns data from a buried treasure into a well-organized toolkit.
Contextualization: Understanding Data's Meaning and Relationships
Context is the difference between seeing "42" as a random number versus understanding it as the answer to life, the universe, and everything. Effective contextualization weaves explanatory threads around raw data: Where did it come from? What conditions affected its collection? How does it relate to other datasets? This process creates what I call "data provenance"—a lineage that helps users interpret information correctly, preventing the all-too-common scenario where numbers get misinterpreted because their origin story was lost.
Activation: Turning Insights into Action
Activation is where data proves its worth by crossing the chasm from analysis to impact. It's the operationalization of insights through automated workflows, decision triggers, and integrated business processes. Think of it as the difference between a weather forecast (insight) and actually carrying an umbrella (action). The best activation systems create feedback loops where actions generate new data, which then refines future decisions—creating a virtuous cycle of continuous improvement.
Japan’s islands, whether inhabited or not, fall under its territorial jurisdiction and require protection. Similarly, all your data assets, whether used or unused, need governance (Data Governance Institute, 2024).
Key Insight: Unsecured data is a liability waiting to happen.
Prevention: Proactive Security Measures
Prevention is about building digital fortresses before attackers arrive at your gates. It combines technical controls like encryption and access management with human factors like security training and phishing simulations. The most effective prevention strategies adopt a "zero trust" mindset, verifying every access request regardless of its origin. This layered defense approach ensures that even if one barrier fails, others stand ready—like concentric castle walls protecting the most valuable data treasures within.
Detection: Continuous Monitoring for Anomalies
Detection systems serve as your organization's nervous system, sensing subtle disturbances that could indicate trouble. Modern detection goes beyond simple rule-based alerts to incorporate behavioral analytics that learn normal patterns and flag deviations. It's the digital equivalent of a seasoned security guard who notices when something "just feels off." These systems must balance sensitivity to avoid alert fatigue while remaining vigilant enough to catch sophisticated, slow-burn attacks that traditional tools might miss.
Response: Rapid Containment Protocols
A well-crafted response plan turns chaos into controlled action during a security incident. It's not just about technical remediation—it's about clear communication channels, predefined decision rights, and practiced escalation paths. Think of it as a fire drill for data breaches: everyone should know their role, from IT teams isolating affected systems to PR teams managing external communications. The best response plans include "war game" scenarios that stress-test the organization's readiness for various attack vectors.
Lesson: In data security, what you don’t know can hurt you—badly.
Japan’s recount didn’t create new islands; it confirmed their existence. Likewise, data cleaning ensures accuracy by clarifying existing information rather than expanding it.
Key Insight: Bad data leads to bad decisions. There is a cost associated with dirty data; in fact, in a 2016 report, IBM estimated that poor data quality costs the U.S. economy $3.1 trillion annually. Imagine what that cost must be nine years later!
Standardization: Consistent Formats and Naming
Standardization is the grammar of your data language—it ensures everyone speaks the same dialect. This means enforcing conventions for date formats (MM/DD/YYYY vs. DD-MON-YYYY), measurement units (kg vs. pounds), and naming schemas (CustomerID vs. Cust_Number). Like a style guide for writers, these standards prevent the Tower of Babel effect where different departments interpret the same data differently. The goal is to make data instantly understandable to any authorized user, regardless of their department or technical background.
Deduplication: Eliminating Redundant Entries
Deduplication is the data equivalent of spring cleaning—removing clutter so you can find what you need. Beyond just identifying exact duplicates, sophisticated deduplication recognizes fuzzy matches (like "J. Smith" vs. "John Smith") and contextual duplicates (like the same product listed under different categories). This process requires both automated tools and human oversight to handle edge cases. The result is a leaner, more accurate dataset where analyses aren't skewed by ghost copies of the same information.
Validation: Ensuring Accuracy and Completeness
Validation acts as your data quality control checkpoint, catching errors before they pollute downstream processes. It verifies that required fields aren't empty, that numerical values fall within expected ranges, and that categorical data matches predefined options. Advanced validation incorporates business rules—for example, ensuring discount percentages don't exceed authorized limits. This ongoing quality assurance creates what I call the "clean data dividend"—the cumulative time and resources saved by preventing errors from propagating through your systems.
The story of Japan’s island discovery serves as a powerful metaphor for the hidden potential within your organization’s data landscape. Just as advanced technology revealed thousands of previously uncounted islands, modern data management tools and practices can uncover valuable insights and opportunities lurking beneath the surface of your data ocean.
Remember, what you think you know about your data might only be the tip of the iceberg – or in this case, the visible peaks of your data archipelago. Embracing these four tenets (Plan and Scan, Store and Explore, Secure and Ensure, and Clean and Lean) will enable you to start mapping your true data territory and unlock its full potential.
The journey to data excellence, like the mapping of Japan’s islands, requires the right tools, methodologies, and mindset. As you navigate your organization’s data waters, remember that there’s always more to discover. The question is: are you ready to embark on your own data discovery expedition?
Just as Japan’s new island count didn’t change the country’s physical reality but transformed our understanding of its geography, proper data management won’t change the data you have. It will revolutionize how you see it, use it, and value it. And in today’s data-driven world, that makes all the difference.
Dr. Joe Perez is a powerhouse in the IT and higher education worlds, with 40-plus years’ experience and a wealth of credentials to his name, having been featured on multiple Times Square billboards. As a former Business Intelligence Specialist at NC State University and currently a Senior Systems Specialist/Team Leader at the NC Department of Health & Human Services (and Chief Technology Officer at CogniMind), Perez has consistently stayed at the forefront of innovation and process improvement. With more than 18,000 LinkedIn followers and a worldwide reputation as an award-winning keynote speaker, data viz/analytics expert, talk show co-host, and Amazon best-selling author, Perez is a highly sought-after resource in his field. He speaks at dozens of conferences each year, reaching audiences in over 20 countries and has been inducted into several prestigious Thought Leader communities. When he’s not working, Dr. Joe shares his musical talents and gives back to his community through his involvement in his church’s Spanish and military ministries.
