In the information age, data powers decisions. But what if the underlying information itself proves unreliable? As complexity grows, bad data infiltrates systems stealthily.
Left unchecked, inaccuracies propagate through downstream processes silently sabotaging planning.
By recognizing key data flaws and rectifying them diligently, organizations safeguard integrity to derive insights confidently.
Here we detail frequent data quality problems and associated detection strategies to help your business intelligence consulting stay corruption-free.
Common Information Corruption Triggers
Many assume data collection mechanisms minimize distortion automatically. However, multiple unwary actions actively introduce errors:
- Faulty data entry: Typos or incorrect formatting when uploading information manually. For example, entering January sales figures under June.
- Measurement failures: Incorrectly calibrated equipment producing inflated readings. Think of a messed up weighing scale overstating package weights.
- Integration errors: Data syncs can malfunction merging datasets imperfectly by dropping fields or duplicates.
- Processing interference: ETL bugs or encoding schemes gone wrong mathematically skew derivations.
Staying cognizant of potential corruption points allows smarter validation planning.
Technique #1: Statistical Analysis
Numerical metrics lend themselves to aggregate validity checks even without inspecting individual records.
Statistical techniques like:
- Distribution analysis
- Average value fluctuations
- Standard deviation shifts
Quickly identify outlier changes indicative of systemic data errors post-integration or acquisition.
For example, a sudden drop in average order value requires inspection – did the integration code change value calculation logic incorrectly?
Technique #2: Spot Check Sampling
Pull mini cross-sections to manually review instead of entire datasets.audit.
Two sampling methods help catch common flaws:
- Vertical top-to-bottom review of all fields across a few records. Reveals incomplete captures or missing variables.
- Horizontal-specific field review across time. Highlights outliers and invalid enumeration values.
Sampling saves time while still catching a majority of corruption signatures.
Technique #3: Logic Tests
Apply if-then style scenario testing to detect logical impossibilities:
- IF order status = delivered BUT delivery date blank
- IF buyer age = 72 BUT account holder DoB = 2015
- IF the order total > account balance THEN raise the exception
Even simple conditional checks validate integrity. Codify these into scripts for automated continuous verification.
Technique #4: Master Data Reconciliation
Centralize certified master listings of record counts, products, and employees. Reconcile totals daily from systems and highlight divergences:
| System | Customers | Discrepancy |
| CRM | 5,042 | None |
| Support Desk | 5,044 | 2 extra |
| Marketing DB | 5,201 | 159 extra |
Master record integrity prevents fragmentation across unmanaged duplicates.
Technique #5: Manual Inspection
When automated validation proves impractical, old-fashioned eyeball verification fills gaps.
Physically trace samples from origin through processing to catch steps dropping data or transforming incorrectly.
While manual examination takes time, nothing matches human visual pattern recognition for noticing subtle systemic anomalies hidden within information flows.
By diversifying techniques, organizations mitigate reliance on single-mode failure catching.
Reduce bad data risk through multi-validation adoption – our consultants stand ready to advise integration for resilient analytics.
Please share any personal anecdotes on flaws uncovered or monitoring methods welcomed below!
