What is Data Harmonization?

Data harmonization is similar to data integration, in that it involves bringing disparate data sources together into a single location. However, harmonization goes a step further by reorganizing data according to a single schema.

Harmonization allows all data to be used for business processes such as analytics and BI without any issues arising from variations in data types, file types, or naming conventions.

How Does Data Harmonization Work?

Data harmonization solutions can vary greatly, depending on factors such as:

• The volume and variety of data sources being used
• The structural differences between each data source
• The reliability of data from each source
• The speed at which updates are performed
• The goals of the relevant business process

In most cases, organizations use an ETL platform such as Integrate.io to create a fully harmonized data source in a central location, such as a data mart. The process for doing goes as follows:

1. Analysis: Business analysts look at available data sources and identify those that are most relevant to business process goals.
2. Mapping: A schema is created to define how data is collated, including processes for conversions, renaming, and dealing with empty or duplicate values.
3. Extraction: Data is exported from the target sources in a raw format.
4. Transformation: The defined schema is applied to the raw data, which can involve deletion, updating, and merging.
5. Loading: Data from multiple sources is loaded to the target destination.
6. QA: Analysts confirm that the harmonized data meets quality standards, with no data loss or inaccuracies. They also confirm that the harmonized data set meets business requirements. For example, if the goal was to create a comprehensive repository of customer information, then the harmonized data should contain an accurate and exhaustive record of each customer.
7. Deployment: Harmonized data is then made available to business users for processes such as analytics and business intelligence.

AI and machine learning can help to perform data harmonization when working with Big Data sets, as they can respond to changes in incoming data without being explicitly programmed to do so.

How is Data Harmonization Used in Enterprise?

Data harmonization can be used to solve any enterprise problem resulting from having disparate and inconsistent data sources. Common examples include:

• Analytics and Business Intelligence: Harmonized data offers a 360 view of the enterprise, which provides richer insights than those gathered from querying each data source individually.
• Customer relationship management: Customer data may exist on a number of different systems across the organization. Harmonization can place all of that data in a single location, with any irrelevant or inaccurate data removed.
• Logistics: Harmonized data can improve decisions related to the supply chain by giving the most accurate possible snapshot of the organization’s current state. This helps to improve the efficiency of just-in-time ordering and flag up issues as they arise.
• Auditing and compliance: Harmonized data can help to speed up processes related to auditing and compliance, such as internal financial controls, by reducing the time spent on collating and cleaning data.

Data harmonization is also a key step in strategies such as Master Data Management.