About Data Cleansing
In our previous blog post, we introduced some basic concepts of data or information quality assurance. Now, we will focus on one of its fundamental pillars: data cleansing.
Why is Data Cleansing Important?
Every organization collects and stores data as part of its operations. The types of data collected depend on the organization’s activities, but data collection is always necessary. Examples include:
- Personal or company identification data of customers,
- Contact details of business partners,
- Product or service attributes,
- Inventory records.
Some claim that “clean data does not exist”, but let’s examine why data cleanliness is crucial.
Clean vs. “Dirty” Data
Advantages of Clean Data
Working with clean data offers several benefits:
- It improves business performance and provides a competitive advantage over companies with poor-quality data.
- It enables accurate analysis and data-driven decision-making.
- It enhances operational efficiency, customer satisfaction, and regulatory compliance in highly regulated industries.
Risks of “Dirty” Data
Conversely, poor-quality data poses serious risks:
- Inaccurate decision-making due to incomplete or incorrect data can lead to financial losses.
- Customer satisfaction declines when incorrect data results in miscommunication, wrong orders, or poor service delivery.
- Business processes—such as ERP system implementation—can be hindered.
- Regulatory non-compliance may lead to legal penalties or fines.
Common Types of “Dirty” Data
- Missing data – Important information (e.g., phone number, email) is absent.
- Outdated data – Changes in personal details (e.g., job position, phone number) or company information (e.g., company name, tax number) are not updated.
- Inconsistent data – Formatting and structure variations (e.g., name order changes, phone number formats differ) make data use and analysis difficult.
- Erroneous data – Typing errors (e.g., wrong phone numbers, incorrect email addresses) create operational issues.
- Duplicates – Identical records appear multiple times in a database (we will cover these in detail in an upcoming post).
What is Data Quality?
Data accuracy and completeness are essential, but in reality, data is often incomplete or erroneous.
Data quality refers to how closely stored data matches the real-world entity it represents. The smaller the discrepancy, the higher the data quality.
Expectations for data quality are influenced by:
- The internal needs of the data-collecting organization (e.g., required data fields).
- External regulations (e.g., legal requirements for name structures, tax number formats).
Data Cleansing – The First Step Towards Improving Data Quality
When data does not meet expectations, data cleansing solutions can enhance quality. There are two primary methods:
- Algorithmic analysis – Mathematical algorithms infer the correct value (e.g., check-digit validation, consistency checks).
- Reference database matching – Data is compared against trusted sources (e.g., official name databases, phone area codes, correct city names).
In our next post, we will cover the practical aspects of data cleansing. For a brief description of our data quality assurance services, please click here.
Interested in improving your company’s data quality? Why not discuss it over a cup of coffee?
