5 data quality processes to know before designing a DQM framework 

Most companies trying to become data-driven cite poor data quality as one of the top 5 challenges. Invesp published a report where they discovered that 54% of companies blame incomplete data and poor data quality as the biggest challenge to data-driven marketing.

Business leaders understand the importance of utilizing data across various functional departments. But data today is far more complex than it used to be 10 years ago. It is multi-variate, causing it to be measured in multiple dimensions; it also changes frequently and has exponentially increased in volume. Organizations are realizing that simple data quality management techniques are not enough. They need to implement an end-to-end data quality framework that fixes current errors as well as prevents future ones.

In this blog, we will look at the most common and crucial data quality processes. You can use this information to see what can possibly benefit your case.

Data quality processes versus data quality framework / lifecycle

Before we move on, it is important to mention that this blog highlights the individual, stand-alone processes that can be used to improve the quality of your data. The order in which they must be implemented is not discussed here. This is where a data quality framework may be useful.

A data quality framework tells you the exact number and order of processes you should perform on your data to improve its quality. For example, you may start your data quality lifecycle by profiling errors and finding possible cleansing opportunities. Similarly, you may end the lifecycle with data profiling as well, just to see if any errors remain. This is something that a data quality framework defines.

Here, we look at the individual processes that are used while implementing a data quality framework. The framework itself depends on the nature of your data, its current quality state, the technology used, and what you wish to achieve.

Data quality processes

1. Data profiling

Data profiling simply means understanding the current state of your data by uncovering hidden details about its structure and contents. A data profiling algorithm analyzes dataset columns and computes statistics for various dimensions, such as:

  • Completeness analysis: Percentage of missing or incomplete fields.
  • Uniqueness analysis: Percentage of non-duplicate or distinct values in a column.
  • Frequency analysis: Count of the most frequently occurring values in a column.
  • Character analysis: Count of values containing letters, numbers, or both, as well as punctuation, leading/trailing spaces, non-printable characters, etc.
  • Statistical analysis: Minimum, maximum, mean, median, and mode for numeric columns.
  • Pattern analysis: Count of values that follow the correct pattern and format.
  • General analysis: Count of values that follow the correct data type and fall between an acceptable range of character length.

A detailed data profile report that contains such information can do wonders for your data quality management initiative. It can be performed at multiple stages of your lifecycle to understand how the data quality processes are impacting the data.

To know more about data profiling, read this blog: What is data profiling: Scope, techniques, and challenges.

2. Data cleansing and standardization

Data cleansing and standardization is the process of eliminating incorrect and invalid information present in a dataset to achieve a consistent and usable view across all data sources.

Some common data cleansing and standardization activities include:

  • Remove and replace empty values, leading/trailing spaces, specific characters and numbers, punctuations, etc.
  • Parse aggregated or longer columns into smaller sub-components, such as Address field into Street Number, Street Name, City, etc.
  • Transform letter cases (upper to lower or lower to upper) to ensure consistent, standardized view.
  • Merge same or similar columns together to avoid duplicated columns.
  • Transform values of a column to follow the correct pattern and format.
  • Perform operations (flag, replace, delete) on the most repetitive words in a column to remove noise in bulk.

To know more about data cleansing and standardization, read this blog: The complete guide to data cleaning tools, solutions and best practices for enterprise level.

3. Data matching

Data matching (also known as record linkage and entity resolution) is the process of comparing two or more records and identifying whether they belong to the same entity.

A data matching process usually contains these steps:

  • Map columns from various data sources to match duplicates across datasets.
  • Select columns to match on; for advanced matching, you can select multiple columns, and prioritize them to increase the accuracy of match results.
  • Execute data match algorithms; if your dataset contains unique identifiers, you can perform exact matching that tells you exactly whether two records are a match or not. In absence of unique identifiers, you will need to perform fuzzy matching that computes the probability of two records being similar.
  • Analyze the match scores that tell the degree to which two or more records are duplicates.
  • Tune the match algorithms to minimize the number of false positives and negatives.

To know more about data matching, read these blogs:

4. Data deduplication

Data deduplication is the process of eliminating multiple records that belong to the same entity. It is one of the biggest challenges faced during data quality management. This process helps you to preserve the correct information and eliminate duplicate records.

The process of eliminating duplicates includes:

  • Analyze the duplicate groups to identify the golden record
  • Mark the rest of the records as its duplicates
  • Remove the duplicate records

To know more about data deduplication, read these blogs:

5. Data merge and survivorship

Data merge and survivorship is the process of building rules that merge duplicate records together through conditional selection and overwriting. This helps you to prevent data loss and retain maximum information from duplicates.

This process includes:

  • Define rules for master record selection based on a column that qualifies for a certain operation (for example, the master record is the one with the longest First Name).
  • Define rules for overwriting data from duplicate records to the master record (for example, overwrite the shortest ZIP Code from duplicates to the master record).
  • Execute the rules creates for conditional master record selection and overwriting.
  • Tune rule configuration to prevent loss of important information.

To know more about data merge and survivorship, read this blog: A quick guide to data merge purge.

Additional processes

In addition to the processes mentioned above, there are a couple more processes that are a necessary part of data quality management lifecycle.

1. Data integration

Data integration is the process of connecting and combining data from multiple disparate sources – including file formats, relational databases, cloud storage, and APIs – and merging them to attain clean and standardized data.

It is an important part of the data quality management framework since data from different sources must come together before it can be transformed to achieve a consistent, standardized view.

To know more about data integration, read this blog: Merging data from multiple sources – challenges and solutions.

2. Data export or load

Data export is the process of loading the cleaned, standardized, matched, deduplicated, and merged data back to the destination source. Just as data integration, data export/load is also an important part of the data quality management framework, since data must be loaded to a central source that is available for use by anyone who needs it.

You must consider some crucial factors before loading the data to a destination source. For example, does the source have older data that can possibly cause conflict during the loading process, as well as assessing the data model followed by the source and ensuring incoming data is designed accordingly.

To know more about data export/load, read this blog: Your complete guide to a successful data migration.


There you have it – a list of top data quality processes to know before you next DQM initiative. Depending on your current data quality state as well as the data view you wish to achieve, you can select the required processes, design them into a data quality management framework, and implement it into the data pipeline.

A data quality tool that facilitates these data quality processes can improve the performance and productivity of your data quality team exponentially. DataMatch Enterprise is one such tool that is capable of performing all data quality processes mentioned above in less than 12 minutes for 2 million records. If you’re interested to know more, you can download a trial of our software or book a demo with our experts.

Furthermore, I have linked some good reads for you below that will help you on your DQM journey.

In this blog, you will find:

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