How low-quality data can adversely affect data analysis

Outcome: Business leaders make flawed strategic decisions based on bad data, such as investing heavily in a market that the data falsely indicated was growing.

2. Biased Results and Skewed Models

Effect: Incomplete data, especially non-randomly missing data, introduces selection bias. For instance, if demographic data is only missing from low-income respondents, any analysis on income will be skewed toward higher-income groups.

Outcome: Predictive models (like machine learning algorithms) trained on biased data will perpetuate systemic errors or discrimination. For example, a loan approval model trained on historically biased data may unfairly reject applications from certain demographic groups.

3. Wasted Time and Resources (Increased Cost)

Effect: Analysts spend up to 80% of their time cleaning, verifying, and reconciling inconsistent or missing data (the "garbage in, garbage out" principle).

Outcome: This diversion of effort slows down the entire decision-making cycle, increases operational costs, and delays the realization of business value. If the data cannot be cleaned, the entire analysis project may be scrapped

Sample Answer

Adverse Effects of Low-Quality Data on Analysis

Low-quality data introduces noise, bias, and uncertainty into the analysis pipeline, making the resulting insights unreliable.

1. Inaccurate and Misleading Conclusions

Effect: Inaccurate data (typos, errors) and inconsistent data (different formats) will lead to incorrect calculations. For example, if product prices are inconsistent across sales regions, revenue calculations will be skewed, resulting in an inaccurate picture of profitability.