Catalog
method#Data#Analytics#Data Governance#Data Quality

Data Profiling

Data profiling is the process of analyzing datasets to compile statistics about data content and structure.

Data profiling enables understanding of data quality and integrity by examining relevant metrics.
Established
Medium

Classification

  • Medium
  • Business
  • Technical
  • Advanced

Technical context

Integration with ETL tools.Compatibility with BI software.Interfaces to databases.

Principles & goals

Data should be analyzed directly at the source.Regular profiling improves data quality.Transparency is essential.
Build
Enterprise, Domain

Use cases & scenarios

Compromises

  • Inaccurate data can lead to wrong decisions.
  • Lack of acceptance in the organization.
  • Technical difficulties in implementation.
  • Conduct data profiling regularly.
  • Use automated tools for data analysis.
  • Ensure compliance with data protection requirements.

I/O & resources

  • Raw data from various sources.
  • Database connections.
  • Data quality metrics.
  • Reports on data analysis.
  • Documentation of detected anomalies.
  • Recommendations for data enhancement.

Description

Data profiling enables understanding of data quality and integrity by examining relevant metrics. It enhances decision-making by providing insights into the data landscape and uncovering potential issues.

✔

  • Improved decision-making through high-quality data.
  • Early detection of potential issues.
  • Increased efficiency through automated processes.

✖

  • Can be costly and time-consuming.
  • Requires special skills and tools.
  • Not all data sources are compatible.

  • Data Quality Index

    An index used to assess the overall quality of datasets.

  • Number of Anomalies

    The total of detected anomalies during analysis.

  • Data Coverage

    The percentage of covered data relative to available data.

Case Study on Data Quality in Supply Chain

This case study illustrates how a company improved the quality of its supply chain data through data profiling.

Efficiency Improvement through Anomaly Detection

A company identified anomalies in sales data, thus minimizing revenue losses.

Integration into BI Tools

Data profiling enabled a company to utilize its BI tools more effectively.

1

Define objectives of data profiling.

2

Collect and prepare raw data.

3

Select profiling tools.

4

Conduct data analyses and document findings.

⚠️ Technical debt & bottlenecks

  • Outdated databases without support.
  • Poor data quality checks.
  • Lack of documentation.
Insufficient data sources.Lack of technical infrastructure.Weak training resources.
  • Ignore anomalies that are important for decisions.
  • Conduct data profiling only on new projects.
  • Use outdated technologies for profiling.
  • Insufficient data validation.
  • Trusting inaccurate data.
  • Lack of communication in the team.
Knowledge in data analysis.Experience with BI tools.Familiarity with data management principles.
Ensure data integrity.Ensure compliance with regulations.Availability of quality-assured data.
  • • Compliance with data protection regulations.
  • • Limited budget for software solutions.
  • • Availability of qualified personnel.