What is Data Harmonization? Definition, process, and best practices

Data harmonization can help cure brain injuries, bring your enterprise extraordinary revenues, and tell you what the right business decision is.

What’s necessary is to convert raw data from different sources into a coherent, standardized, and comprehensive format for analysis. This process of creating a composite, homogeneous data set containing only high-quality data cleared of errors and duplicates is called data harmonization. This is a relatively new approach in data analytics and visualization which aims at creating a single source of truth that facilitates informed decision-making.

Let’s consider a few cases so you can form a clearer picture and figure out if you and your company can benefit from it.

Most Common Use Cases

Use Case No. 1

Imagine you own a company. A new trend appears and most of the competition decides to follow it. Should you as well? How can you know? You should analyze data. Yes, but there's so much data from all different sources, you've already invested a lot to gather it but you're not quite sure how to put it into use. You need business intelligence you can rely on.

Use Case No. 2

Clinics all around the world deal with patients with brain injuries daily. They all have their own records, statistics, and conclusions. Each clinic owns a rich data set which is unfortunately not good enough for medical researchers to draw conclusions to assist in making discoveries in science that will help the patients. What's necessary is to combine all these disparate data sources and enable researchers to make use of them.

Use Case No. 3

You work very hard on promoting your brand across different regions, industries, markets, and target audiences. These efforts of yours generate a huge variety of marketing metrics. These are very often expressed using different terminology by different marketing channels. For example, quite often it happens that Google Analytics and CRM systems label the same metric in different ways. Your company ends up with loads of raw data that can hardly be of any use for data analysis or for gaining insights into your brand positioning in the market.

The solution to all of the situations mentioned above is data harmonization. What is it exactly? What does it involve? How different is it from integration and standardization? Let's dive in.

Data Harmonization - What and Why?

All three cases described above have one thing in common - disparate and unaligned data, difficult to analyze, extract insights or take any value from.

Raw data gathered from various surveys, workshops, social media, and internet of things devices often contain irrelevant pointers, false values, and redundant statistics. In order for data to be valuable to business users and analysts and to provide consistent business intelligence insights, it needs to be harmonized. Furthermore, harmonization creates democratic access to hierarchies that enable broad views across multiple sources.

By harmonizing data companies provide access to an accurate view of sales, trends, and other metrics which makes it possible to get a big picture of where your company is as well as detailed specific insights. Besides, it is possible for all different company units such as marketing, sales, and customer service to rely on one consistent data set instead of creating separate systems which are often expensive, error-prone, and unreliable. Harmonized data is not static - it can continue to be updated either on a regular basis or in real time.

Harmonization basically integrates these various data sources to create a coherent whole that enables you to access the appropriate levels of information. Otherwise dissimilar data sets can easily communicate with one another by synchronizing data points across goods, channels, times, and geographical locations.

Data Harmonization / Integration / Standardization / Aggregation - What's the Difference?

There are so many processes that you can perform within data management, all very similar, but with considerable differences as well.

Data harmonization is not the same as integrating various sources of data into a single warehouse. Few of the tools available on the market can effectively harmonize diverse data sets to the degree necessary to provide robust analytics.

In some cases, integration is all you actually need. You might not require a comprehensive harmonization if the goal is to only add to your existing data warehouse or evaluate the correctness of the data. Your data may only need to be cleaned by setting up a straightforward "extract, transform, load" (ETL) procedure or hiring a data service provider.

Yet, if your organization aims at being analytics-ready, then you should start the process of data harmonization. As opposed to simple integration, it should involve:

• metric-to-metric comparison across sources,

• the creation of attributes in the form of additional fields or derived metrics,

• a process that is scaleable and can handle extra metrics and domain specifics.

The distinction between data aggregation and data harmonization should also be made. Data aggregation is the process of gathering information from databases in order to get readily combined datasets for processing. While data aggregation includes assembling data into datasets for further analysis, data harmonization provides perspectives across disparate datasets.

Another common error is identifying data harmonization with data standardization. Similarly to data harmonization, data standardization transforms various datasets into the same format using a Common Data Model (CDM). When working with data, this enables more collaboration and interoperability. However, while standardization is about conformity, harmonization is about consistency. Standardization indicates that the same techniques, guidelines, and standard operating procedures are always applied, while harmonization is more flexible and realistic in a collaborative context which helps reduce data heterogeneity.

How to Obtain Harmonized Data? - Key Steps

Step 1 - Defining Goals and Objectives

First of all, setting a clear goal is essential to preventing the procedure from turning into simply integrating data. By having a clear vision and identifying the use cases you will be able to determine the relevant fields and focus your efforts and resources most effectively toward enabling the analytics solutions you require. In order to achieve this, all the parties involved in reaching harmonized data should commit to providing the appropriate infrastructure, establishing firm governance processes, and engaging the right personnel.

Step 2 - Data Mapping

As already mentioned, data sources are numerous. In order to perform the data harmonization, it is necessary to identify all the sources your data come from in order to create data patterns and align your insights. Once the data map is ready, the rows, columns, data labels, types of information contained in these structures, and the connections between the data should be examined. This will provide information about necessary data transformation.

Step 3 - Data Transformation

This is a core step in the process of data harmonization where all the magic happens. Data can be transformed by implementing an ETL solution or through data virtualization.

• ETL Solutions

ETL technologies, a fundamental aspect of data engineering, integrate with the current data architecture and immediately harmonize raw data. The process involves extracting data from the original datasets, transforming it into a usable format, and loading it to destination datasets.

Most ETL systems automatically perform all necessary operations which include data aggregation, cleansing, filtering, integration, validation, and splitting.

• Data Virtualisation

In data virtualization, a separate layer is created where applications can access, retrieve, and manipulate data as needed. It consolidates all the data into a single virtual place, enabling real-time access without the need for ETL.

Step 4 - Testing

After data is processed, converted into a common format where needed, and pooled, a thorough quality check is run on data in order to make sure it has maintained an acceptable level of integrity and validity.

Best Data Harmonization Practices and Challenges

The best data harmonization typically combines both automated and manual processes. By combining calibrated artificial intelligence (AI) and machine learning with the work of knowledgeable data scientists, a significant portion of the whole procedure can be automated over time. By utilizing AI to its fullest potential, errors are considerably decreased and the process of gaining insights is sped up.

Data mapping and a clear understanding of how and when the company's numerous data sources will interact with the current data infrastructure are two of the main issues in managing data for an organization. If the team lacks knowledge in this rather specialized field and is unsure of the best tools to employ, it becomes a difficult assignment.

Data Harmonization vs Master Data Management

Master data management (MDM) aims to handle communal master data effectively so that it is accessible to various parties. It offers open access to the centralized data cache of an organization. Additionally, it resolves data issues by emphasizing business process simplification, data quality, and the comprehensive integration and standardization of information systems.

Harmonization takes this one step further by cleansing data in order to eliminate errors and discrepancies and build a cohesive, clear picture.

Benefits of Harmonized Data to Your Business

In plain English, data harmonization increases the usefulness and value of data. Organizations can also convert fragmented and erroneous data into usable information, producing fresh analytics, insights, and visualizations. The user can access business intelligence faster, find important observations, and spot early disturbances. Businesses can gain knowledge about their clients, shifting market dynamics, and even rival strategies.

Utilizing inadequate data sets for crucial business analytics could result in misguided decisions and substantial financial loss for the business. On the other hand, working with a stable and reliable data source gives your company greater agility and competence enabling the management to make decisions quickly and confidently.

Software applications can be created more quickly, at lower cost, with lesser maintenance requirements, and with greater scalability thanks to well-harmonized data.

We all know that collecting, aggregating, and preparing data is rather time-consuming. Utilizing data that's harmonized gives you access to a knowledge base that makes the implementation of new analytics technologies easier and quicker.

Directly related to the reduction in time is the reduction of expenses. Less time means less money.

Furthermore, harmonization makes data governance much easier. All other data sets will automatically update in response to changes made to one. This is not only a huge time-saver but a benefit that can save your company from making costly errors.

Data harmonization enhances the performance and effectiveness of machine learning models by providing a standardized and compatible dataset for training, improving generalization and reducing bias.

Data Quality Equals Business Quality

If we stop and think about the universal truth that decisions are as good as the information they are based on, we would realize that data harmonization makes even more sense than we believed initially.

After performing data harmonization, organizations will be able to access the huge value contained in datasets that are presently unused or, even worse, producing false insights. Data harmonization also makes it possible for companies to get a full-spectrum perspective of their organization and the marketplaces they operate in by combining the flood of data that is now available into a coherent, cohesive whole. Quality business data leads to quality decisions which all together lead to quality business.