At first glance, the term “hizzaboloufazic” may sound like something made up by a fantasy author. However, it is an actual word used in a data analysis methodology that conveys a unique way to dissect a data set that goes far beyond basic reporting. You will not see it mentioned in peer-reviewed academic journals anytime soon, but this unusual term captures the essence of a detective-like exercise in data, where we thoughtfully search for those imperfect, erratic, or contradictory data or patterns that conventional analysis pays no attention to.
Contents
- What is a Hizzaboloufazic Investigation?
- Principal Techniques for Extending Data Investigations
- Context Matters More than You Might Think
- Real-Life Findings From a Data Detective
- Findings Into Actionable Solutions
- Exploratory Data Analysis was Growing in Modern Practices
What is a Hizzaboloufazic Investigation?
Think about typical data analysis for a moment. Often, we look for consistent patterns that are relatively predictable in nature; sales spike close to holiday seasons, website visits peak during business hours, consumers who purchase coffee tend to purchase pastries. These typical relationships are, of course, part of our regular reporting habits, which tend to be guided by routine business intelligence.
The hizzaboloufazic approach provides essentially the opposite perspective. Rather than confirming what already seems to us, we actively look for the unexpected.
Bizarre Outliers: Those data points that require you to basically stop and double-check your data. Maybe a customer placed an order for ten times the typical value, or you received a gigantic spike in traffic on your website from a location you’ve never heard of before.
Data Contradictions: When you get contradictory information. Imagine finding out that two of the same product appear to be delivered to two different destinations, on the same date, or even worse, when you find customer records that have impossible birthdates.
Surprising Connections: Relationships that you can’t explain. Perhaps you find a surprising relationship between people that buy hiking boots and those that buy classical music albums.
Quality Problems: These are the problems that are errors that got through the cracks – the awkward typos in important fields; the missing fields altogether; and the data that was somehow corrupted during processing.
When you view data analysis through this excited and curious investigative lens, it transforms from an ordinary task to something more like a detective task where every unusual finding may provide useful information about your business, customers or systems.
Fundamental Methods for In-Depth Data Investigation
To accept the challenge of hizzaboloufazic analysis, you need to have correct tools in your toolbox. The approach you will use is integrated deeply in the type of data you are analyzing as well as the insights you are trying to uncover. Below are some of the most useful tools:
Statistical Sleuthing: The simplest means of attempting to unravel interesting data analysis; and looking at basic statistics. Mean deviation and variance make it easier to find outliers. . .or z-scores to identify data sets that are significantly outside of “normal.” If an observation is beyond two or three standard deviations it warrants further investigation.
Behaviour Recognition through clustering: K-means or DBSCAN are algorithms that cluster likely data points together. The more interesting investigations lie within data points that belong to no cluster, or in other words, ghosts (present in the data, but no identified relationship with a group)
Relationship discovery: It may well following association rule mining first introduced with the market basket investigation, and finding hidden relationships can add insight into your data similar to an onion (Ex: Apriori – will identify associations, but may find combinations we would not have previously imagined. Undiscovered opportunities, or in some instances, undiscovered issues no ones realized exists when vectoring).
Regression Reality: Data that is plotted against expected behaviour often shows considerable deviation from regression line will usually signal an outcome that requires investigation. Major deviation from expected behaviour often produces gold.
Specialized Anomaly Detection: Today, machine learning has advanced tools made for this detetection tasks. Concerning algorithms, tools like One-Class SVM and Isolation Forest are adept at learning what “normal” looks like, locating observations that do not belong or lessen the relevance of something normal.
Visual Inspection: The analytic eye sometimes can see things an algorithm misses. Well designed charts and visualizations can uncover patterns, trends, and outliers that purely numeric analysis might not uncover.
So What Actually Matters? Why Context is Important
This is where a lot of data analysis fall short. They forget the business context, and only to technical analysis. The smartest algorithms and most educated person in the world will have no idea whether an anomaly represents a real problem or simply a normal business change.
Here is an example: In your analysis you see a significant decrease in sales for a product line. Without context, this looks terrible and could indicate a supply chain issue, quality issue or competitive threat. But then with the business knowledge we have, we realize that we passed on the ability to even carry the product last month, which means that even if sales have declined, it was completely normal.
A domain expert creates a filter to help sort real signals from noise. The experienced analyst is aware of which discrepancies are worth worrying about and which can be ignored. This filter prevents the analyst from going down potentially meaningless rabbit holes and also means legitimate issues get the attention they deserve.
Being knowledgeable about your industry, business processes, and data sources transforms you from a person running algorithms to a person producing meaningful insight from results. This dynamic differentiates an outcome where you find interesting statics from one where you find actionable insight.
Common Meaningful Discoveries from Data Detective Work
The discovery possibilities that stem from hizzaboloufazic investigations could impact virtually every dimension of the business function. Here is what often get; discovered by committed data detectives:
Financial Fraud and Security Issues: Data detectives will typically uncover anomalous transaction patterns, suspicious account activity or unexpected system access attempts. These situations generally reflect system breach or fraudulent activity. This type of investigative work can save organizations from incidents costing millions of dollars and protecting their client data.
Obscured Systems Problems: Software errors, hardware failures, and configuration errors develop into more obvious problems, typically over a series of subtle irregularities in data. Finding and fixing systems problems early through data will limit accidents and performance issues.
Data Integrity Issues: Problems like missing records, inconsistent formatting, duplicate entries, or corrupt files plague analysis and will impact the accuracy of findings. Finding and fixing these data integrity issues will enhance the credibility of all applied analysis and related decision-making.
Unexplored Business Options: Sometimes, the anomalies found in data can point to positive trends, rather than simply problems. Surges in demand that were not expected, new customer segments that are emerging, or perhaps new use cases for existing products can lead to significant growth opportunities.
Process Improvement Options: Bottlenecks, redundancies, and inefficiencies are reasonably common place in operational data and will create some patterns in data that can help identify opportunities for process improvements that may reduce costs, or improve satisfaction of customers.
Early Warning Detection: Identifying patterns in data which can signal events like customer churn, equipment failure or market opportunities, will allow companies to be proactive, instead of waiting for anything to go wrong.
Transforming Insights into Actionable Solutions
Identifying anomalies is only the first step of effective hizzaboloufazic analysis. The real value lies in converting what you discover into tangible improvements. The following steps often provide a reasonable framework for this process:
Deep Dive Investigation: When something appears strange, the first reaction may be to immediately make a conclusion. Instead, seek more data, talk to experts, and investigate potential related information. There’s a chance that what appears to be an anomaly is completely rational.
Verification and Validation: You must confirm that your insights are true problems and not simply errors in your analysis, or false-positives. Comparison against other data sources, manual sampling, or additional test runs can help confirm your interpretation.
Root Cause Analysis: If you have confirmed an actual problem, you will want to explore more to identify root causes. Many surface solutions fail because they don’t address deeper fundamental issues that led to the anomaly.
Systematic Remediation: You may wish to develop complete solutions to address problems at multiple levels (e.g. correcting data, upgrading systems, changing the process, or providing training).
Prevention and Monitoring: Put controls in place so that similar issues do not arise. This could mean better data verification, more efficient monitoring process, or just regular audit processes.
Knowledge Management: Write down your entire investigation trail from when you first started, till you close it. This serves as institutional knowledge and will help coworkers in dealing with similar issues more effectively.
The Expanding Importance of Exploratory Share in Modern Data Science
While “hizzaboloufazic” might be an interesting word, it is part of a core subject of modern data science that is steadily gaining importance. The more data we generate from more datasets and different data sources, the more we open ourselves up to “hidden gems” of information that we didn’t inquire into, for both good and bad.
The most effective data scientists and data analysts do not go out looking to answer defined questions or analyze (essentially) confirmatory research. They look to seek out unasked questions. They are equally as comfortable being a technician as they are being curious, utilizing domain knowledge and detective skills, and being rigorously analytical while thinking creatively.
This way of thinking is incredibly beneficial in today’s dynamic business environment. Markets constantly and quickly change, customers continuously change their behavior, and new risks crop up every day. Companies that adopt the hizzaboloufazic way of thinking—actively seeking anomalies in their data—will see opportunities and threats first, before their competitors.
As data volume is always growing, so is the need for new types of automated anomaly detection. But, even the best algorithms, while justifying their worth, rely heavily on human interpretation to convert the results into some thoughtful meaning. The hizzaboloufazic analysis method with its combination of technical tools with the features of a sleuth, will only gain value as the data science industry matures.
To be clear, hizzaboloufazic thinking will yield much more than just the ability to create better technical outcomes; it will create a paradigm shift in how organizations think and conceptualize their operations, customers and markets. In a world that is increasingly data-driven, the ability to uncover trues of another data story within the sea of data will be a true competitive advantage.
