What Is Data Harmonization?

Data harmonization brings your data together in a way that offers a unified story.

Specifically, it’s the process of reconciling disparate data from various sources and formats, transforming it into a cohesive, standardized format. By addressing discrepancies/inaccuracies in data types, formats, structures, and semantics, and consolidating them, data harmonization ensures that all data across the organization can be promptly analyzed.

Hence, data harmonization’s fundamental purpose is to facilitate more effective data analysis, decision-making, and strategic planning — turning raw data into a trove.

Let’s unpack this further.
 

Data harmonization use cases/examples

Below are three specific data harmonization use cases in the context of educational facilities, supply chain management, and real estate analysis.

Use case 1: Educational outcomes

Educational institutions — online learning platforms, especially — collect vast amounts of data on student demographics, engagement, learning outcomes, and more. Problematically, such raw data often resides in silos, disjointedly stored across Learning Management Systems (LMS), in-house student information systems, and third-party apps.

Data harmonization collects the disparate raw data sets to create a holistic, actionable view of student learning experiences and outcomes. This unified data can then be used to facilitate personalized learning, identify at-risk students, and measure the educational programs’ effectiveness more generally.

Use case 2: Supply chain optimization

Consider a global manufacturing company with multiple factories, warehouses, and suppliers scattered across various countries — each operating with its own independent set of data formats, standards, and systems. Managing inventory levels, forecasting demand, and ensuring timely delivery of materials becomes an onerous — often unreliable — task if the associated datasets aren’t harmonized. Data discrepancies across chains can lead to inefficiencies, including overstocking, understocking, and delays in production, among others.

Data harmonization here involves standardizing data across the multinational supply chain, enabling a more seamless communication stream and data exchange between entities, thereby enhancing overall supply chain performance.

Use case 3: Real estate market analysis accuracy

The role of real estate companies and associated market analysts involves gathering a lot of information: property listings, sales transactions, zoning laws, demographic data, economic indicators, and so on — all from different regions and sources. This data, however, often comes in varied formats and standards, making it difficult to aggregate and analyze effectively.

Data harmonization here involves reorienting this disparate information to enable on-demand market analysis, trend forecasting, valuation accuracy, and visibility into other market-essential insights.
 

Data harmonization vs. data integration vs. data standardization

Data harmonization, integration, and standardization are all practices that fall under the umbrella of data management. They’re similar and partially overlap, although they each play their own distinct roles.

• Data integration refers to the process of combining data from different sources into a single warehouse. Data harmonization takes this one step further by hierarchically reorganizing data into a single schema. Put slightly differently, data harmonization goes beyond integration, not just merging data, but also reconciling discrepancies among datasets to facilitate more efficient analysis and reporting.
• Data standardization involves implementing formatting, structural, and/or rule conformity across your data, such as changing all temperature readings to Celsius, or ensuring all phone numbers follow the same international dialing format. Harmonization, differing slightly, entails adjusting your data to conform to a shared definition, interpretation, or benchmark, such as aligning all financial transactions to the Generally Accepted Accounting Principles (GAAP), for example.

Additionally, standardization ensures the consistent application of techniques and standard operating procedures, whereas harmonization offers a more adaptable and pragmatic, flexible — often more realistic — approach in collaborative environments, aiding in the reduction of data variability.

The method your business chooses to adopt largely depends on the objective. If the goal is to ensure uniformity and compliance across data types and sources, then standardization may suffice. If the goal is simply to evaluate your data’s integrity and integrate it into a single repository, then integration may be all you require. But, if the aim is to facilitate interoperability and prompt, meaningful analysis across diverse datasets, harmonization will likely prove more effective for you.
 

Benefits of harmonizing data

Data harmonization enhances the usability and value of data by converting fragmented and inaccurate collections into actionable insights, facilitating new analyses, and enabling optimized data visualization. This process paves the road to business intelligence, accelerates the discovery of actionable and relevant insights, and aids in the early detection of disruptions, among other business-critical advantages.

Regardless of whether an organization is a longstanding entity or a recent startup, it will accumulate vast amounts of data from a diverse range of sources — often plagued with errors and inconsistencies. Through the adoption of data harmonization tools, organizations can transform this data from raw information into a rich source of business intelligence, offering insights into customer behavior, market trends, competitive landscapes, and others.
 

Risks of not harmonizing data

Failing to harmonize data translates into lost opportunities.

In the context of our use cases, a multinational supply chain, through unharmonized data, could experience significant logistical challenges, including inefficiencies that lead to overhead cost increases, stock imbalances, and delays in product deliveries. Similarly, educational institutions may fail to fully understand or improve student performance and engagement, limiting the overall quality of their institution. For real estate companies and market analysts, the absence of harmonized data can result in unreliable market analysis and forecasting, potentially leading to poor investment strategies and financial losses.

More generally and across all business types, when data harmonization isn’t present, management makes themselves susceptible to making uninformed, costly decisions. This is because they lack the holistic picture of business measurables that harmonization offers, and therefore increase their likelihood of missed opportunities.
 

The data harmonization process

Before hopping into the following three key steps, ensure that you concisely define your objectives for harmonization. This will guide the alignment of disparate data sets to meet your specific needs, making sure that the subsequent steps are focused and relevant. And, no relevant party should be left out of this initial planning stage; engage all stakeholders to ensure their insights and requirements are considered.

Step 1: Acquire and map

Identify all relevant sources of data that need to be harmonized — both internal and external, and concerning the predetermined objective. This could include siloed internal databases and spreadsheets, cloud networks, and external data services, among others.

Then, create a single schema for the data to follow, mapping all the identified sources to this unified model. Once the data map is prepared, consider the rows, columns, data labels, and the types of information these elements hold, as well as how the data is interconnected.

Step 2: Transform

You can transform your data either via an Extract, Transform, Load (ETL) solution, or through data virtualization.

• ETL: To consolidate data into a unified dataset, three key processes are necessary: extracting, transforming, and loading. While extraction/mapping involves pulling data from its original sources, the transformation phase adjusts the data to ensure compatibility and readiness for analysis. This step may include cleansing, deduplication, normalization, and aggregation to improve data quality and value.
• Data virtualization: As a more cost-effective method, data virtualization establishes a unified layer that enables applications to access, retrieve, and manipulate data on demand. This approach consolidates information into a single virtual space, offering immediate access without the need for ETL processes.

Step 3: Test and deploy

Run a thorough set of tests to validate the accuracy, integrity, and usability of the harmonized data. This involves checking for consistency and completeness, and ensuring that the data meets the defined objectives and requirements. Furthermore, utilize both automated testing tools and manual reviews to uncover any issues or discrepancies that may have been overlooked during the transformation phase.

Then, deploy. Ensure that the harmonized data is made accessible to all relevant stakeholders. Note, also, that the data harmonization process doesn’t end at deployment; rather, ongoing monitoring and maintenance are required for your organization to best adapt to new data sources, evolving business needs, and emerging technologies.
 

Data harmonization FAQs

Who uses harmonized data in a business?

Harmonized data isn’t confined to one team.

As data harmonization facilitates a unified view of information across an organization, it’s used by multiple departments, most commonly including marketing, sales, finance, human resources, and operations.

How do I ensure my harmonized data stays harmonized?

Maintaining data harmonization requires regular audits, consistent data governance policies, continuous monitoring for data quality, and adopting a flexible data integration framework that can accommodate changes in data sources or structures.

Many organizations hold periodic data validation sessions and update their data models accordingly to reflect new or evolving data standards, ensuring harmonization remains intact.