Guide to Data Harmonisation

Data is a record of the past and a decisive input for our decisions. But analysing siloed, disparate data in various formats can be laborious and lead to costly missteps. By ensuring data compatibility, harmonisation can bring confidence in your data-driven decisions.

This guide will break down what data harmonisation is, its benefits, real-world applications and the tools you need to make it work for your organisation.

Data harmonisation is the process of ensuring data from various, often massive datasets is consistent and compatible. It involves organising and aligning data from different sources to the same format or standard. Harmonisation removes duplication and errors, which is essential when, for example, data originates from different countries, measurement units, years or data platforms. The end result is datasets with consistent units or formats, easy to compare and analyse.

For example, analysing your ad performance across various ad platforms, each with distinct performance metrics, can be burdensome and time-consuming. But if the data is harmonised, deciding where your budget is best spent becomes an easy task that can be automated.

Harmonisation tools and platforms can harmonise data from multiple sources, such as ERPs, CRMs, public health records, scientific measurements, social media, email campaigns and web analytics.

Below are some harmonisation dimensions.

• Data format: standardising data in .csv, HTML and JSON files.
• Schema: Reconciling differences between structured, semi-structured and unstructured data.
• Unit measures: Converting miles to kilometres or pounds to kilograms.
• Semantics: This involves reconciling the meaning of words, which often involves reconciling meaning even when the same terminology is used. For instance, several datasets may be measuring incidence of a disease in “young adults.” However, some may be defining the term as ages 18 to 22, while others as ages 18 to 30.

In our automated, data-driven world, harmonised data can bring confidence in your data-driven decisions and insights. It can help you to decide, for instance, about budget allocation, market expansion, new product development and customer engagement strategies.

Data harmonisation also helps with collaboration across teams. For example, sales and customer service departments can collaborate more effectively once the data they generate is harmonised to one common standard. Reducing redundant data processing and automating manual tasks cuts down on operational expenses so you can focus on new ideas, products and innovation.

Finally, consolidating and standardising data means you can meet regulatory requirements more easily. With harmonised data, you can quickly generate accurate audit reports and maintain data governance practices, reducing the risk of fines or reputational damage while building trust with stakeholders.

To truly understand the power of data harmonisation, it helps to see it in action. Here are some real-world examples of how you can use harmonised data in your organisation.

Data harmonisation plays a foundational role in AI and especially agentic AI. AI models train on vast datasets. Harmonisation reduces data “noise” and delivers consistent data for large language model (LLM) training. AI agents, which make decisions and act autonomously based on data from disparate sources, can take action that leads to the expected outcome when the underlying data is clean and harmonised. For example, an e-commerce AI agent can act as a digital concierge: converse with a customer, help them place an order or process a return, based on near real-time inventory, sales and customer ID data.

Fedex once faced the challenge of siloed customer web browsing and sales data, which took the sales teams three weeks to sort through before they could keep in touch to customers who abandoned their delivery quotes. Unifying and normalising data by combining CRM, social and transactional data gives you a 360-degree view of your customers, which means your marketing and sales teams can make quick decisions and launch targeted campaigns before sales are lost.

As OpenTable, which helps restaurants fill 1.7 billion seats a year, grew globally, so did the demands on customer service. Many of the support requests were repetitive, but OpenTable service agents had to navigate disconnected and fragmented data to manage routine tasks like booking changes. Once their data was integrated and harmonised into a single source of truth, Customer Service could focus on servicing the more complex situations.

Connecting and harmonising data from service tickets, interaction logs, emails and chat transcripts, can give you a unified picture for each customer that can drive automation and better customer experiences.

Data harmonisation requires a clear data strategy and the right tools. Here are some examples of systems and platforms.

• Master Data Management (MDM) platforms: They help establish a single source of truth by standardising and synchronising core data across the organisation. Examples include CRMs and ERPs.
• Data integration tools: Unify or transfer data. For example, zero copy, which unifies data without duplication and Extract-Transform-Load (ELT) tools, which transfer data in batches.
• Data lakehouses and sub-second real-time data platforms: Large repositories of data that let you apply your own business rules, analyse data, train LLMs and deliver AI applications.
• Data quality and cleansing tools: Identify, correct and prevent inconsistencies, so harmonised data remains trustworthy.

To decide which tools and platforms to use, start by defining the desired and specific outcomes you want to achieve with harmonised data. Consider the volume and variety of your data, your budget, internal expertise and number of resources.

A well-executed data harmonisation strategy requires focus and collaboration. Here are three foundational practices for success.

Define the purpose of your data harmonisation efforts from the start. Are you looking to improve decision-making, optimise customer experiences or improve regulatory compliance? Clear objectives guide your approach, but they also help measure the success of your efforts.

Data harmonisation often involves multiple teams, including IT, Marketing, Sales, Customer Service and Data Science. Engage stakeholders early to make sure everyone agrees on goals and address data needs across departments. This creates a shared understanding of how the harmonised data will be used.

You can automate data harmonisation to make the process smoother. Automated tools can handle repetitive tasks such as mapping fields, resolving inconsistencies and reconciling formats — saving time and reducing errors. Using automation means your team can focus on gathering insights rather than cleaning data.

These core practices can help you to create a strong foundation for successful data harmonisation, making it easier to unlock the full potential of your data.

Even the best technologies and practises come with challenges and data harmonisation is no different. Fortunately, there are some practical solutions to help you to overcome common obstacles.

Harmonising data is only effective when the original data is of high-quality. Incomplete or outdated data can undermine the process.

Solution: Implement careful data validation rules to identify and fix errors. Regularly monitor data sources and integrate quality checks into your harmonisation workflows to maintain high standards.

Different systems may define the same data point in various ways. For example, “customer ID” in one system may correspond to “client code” in another. These semantic mismatches can create errors.

Solution: Create a standardised data dictionary that defines key terms and fields across all systems. This ensures consistency and avoids misinterpretation during harmonisation.

Combining data from multiple sources increases the complexity of meeting privacy regulations, as well as the risk for a breach. Mishandling sensitive information can lead to compliance violations or open the door to security breaches.

Solution: Incorporate data governance policies into your harmonisation process. Use tools to track data lineage and apply automated compliance checks to make sure privacy and regulatory requirements are met.

At first glance, data harmonisation and data integration may seem similar, but they serve distinct roles. Data integration combines data from multiple sources into one system, such as a data lake, without addressing inconsistencies. Data harmonisation takes this a step further by standardising and aligning data and ensuring data consistency for accurate analytics and decisions.