Every business runs on more and more applications than it did just years ago, a CRM, a marketing platform, an invoicing system, a stock management tool, and dozens more. Each one holds a piece of the picture. Data integration is what turns those separate pieces into something a business can act on.
Get it right, and reporting, automation, and decision-making all get faster and more reliable. Get it wrong, and the same problems tend to be resurfaced in different forms, from data that's late, and data that's duplicated, to data nobody fully trusts.
In this article, we'll be covering what data integration actually is, the main types and techniques available, the challenges you're likely to run into, why it matters even more now that AI is in the mix, the best practices that separate successful projects from failed ones, how it compares to ETL, and how to start choosing the right approach for your business.
Data integration is the process of combining data from multiple sources to extract additional value, typically involving joining, cleansing, validating, and enriching data along the way.
There are three main types of data integration: consolidation (combining data into one location, like a data warehouse), propagation (copying and enriching data across systems), and virtualization (a unified view without moving the data).
The most common data integration challenges include business and IT misalignment, poor error handling, and custom-coded solutions that become unmaintainable as they grow.
According to CloverDX's own research of data leaders from organizations across the US and UK, 92% of organizations are already using AI in data workflows, but many still lack the data foundations to make it reliable at scale.
Data integration and ETL are related but distinct: ETL specifically describes moving data into a data warehouse with a transformation step, while data integration covers any process that combines data sources.
Starting small with the minimum data needed for a specific business goal, rather than building one comprehensive unified model upfront, is one of the most effective best practices.
Data integration is the process of combining data from multiple sources in order to extract additional value. The process usually involves joining, cleansing, validating, and enriching the data along the way.
Even smaller organizations may use dozens of applications within their business, and that number can reach the thousands for larger organizations. The ability to work with all data across each of those different applications is crucial. Data integration makes this possible.
For example, a simple data integration task might consist of combining various Excel spreadsheets with the information in an Access database. At the other end of the scale, an extensive BI (business intelligence) platform needs to work with data from a customer database, web analytics platform, invoicing system, stock management platform, and third-party systems such as address databases – all in sync and without overlap.
There are three main types of data integration: consolidation, which combines data into one central location, propagation, which copies and enriches data across systems, and virtualization, which provides a unified view without physically moving the data. Which one fits your situation depends on how much control you need over where the data lives.
Data consolidation is a process that combines data from multiple systems and stores it in one location. The goal is to simplify the application landscape and provide a single data point for other applications to access without needing to deal with the complexities – and often impossibilities – of dealing with data at the original source. Consolidating data makes processes such as BI, reporting, and data mining far more straightforward.
The standard example of data consolidation in action is a data warehouse. The primary objective of a data warehouse is to aggregate information from multiple systems and present that data to reporting or data mining systems to extract maximum value, all from a single data source.
Data propagation is the process of copying data from one system to another, often while adding further information from elsewhere to enrich the data and prevent duplicate entries across multiple systems.
For example, enriching a licensing application with data from a CRM application can allow teams that manage licenses to serve customers more effectively by avoiding the need to enter customer information into two different applications.
Data ingestion vs data integration: What's the difference?Another typical example is the propagation of data from production systems to QA or pre-production labs to support end-to-end testing of various in-house applications during regular development and maintenance cycles.
Such data propagation often involves logic that anonymizes data or selects only a subset of the source data due to security or volume concerns.
Data virtualization provides a near real-time unified view of data across multiple different systems without having to store all of that data in a single location.
When data is queried, the virtualization system queries the source systems and builds a unified view of the data on demand. Various caches are often employed to ensure that queries are fast and do not overload the source applications.
Being able to expose virtualization queries through multiple mechanisms, including easily configured API endpoints, is vital to make the query results as accessible as possible.
There are several ways in which data integration can be completed, ranging from the manual integration approach with little or no automation to the more comprehensive common data storage technique, for example a data warehouse.
Users operate with all the relevant information, accessing all the source systems or web page interfaces. No unified view of the data exists.
This approach requires particular applications to implement all the integration efforts. The downside of this technique is that it's only manageable when there’s a minimal number of applications.
This approach transfers the integration logic from applications to a new middleware layer. Although the integration logic is not implemented in the applications anymore, the applications still need to participate in the data integration partially.
Read more on the importance of a data integration layer:
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The uniform data access, or virtual integration, technique leaves data in the source systems. It defines a set of views to provide and access a unified view of the customer across the whole enterprise.
For example, when a user accesses customer information, the details of that customer are transparently acquired from the respective system.
The main benefits of virtual integration are nearly zero latency of the data update propagation from the source system to the consolidated view, and there's no need for a separate store for the consolidated data.
However, drawbacks can include limited information about the data's history and version management, restrictions on being able to apply the method to only 'similar' data sources, and the fact that access to the user data generates extra load on the source systems, which they may not have been designed to accommodate.
This usually means creating a new system that keeps a copy of the source system’s data to store and manage independently.
The most well-known example of this approach is a data warehouse, the benefits of which include data version management and being able to combine data from very different sources, such as mainframes, databases, and flat files. Physical integration, however, requires a separate system to handle vast volumes of data.
Data integration comes with a predictable set of challenges, from misalignment between business and IT to the risks of custom-coded solutions that become harder to maintain as they grow. Recognizing them early is usually the difference between a project that stays on track and one that quietly derails.
Any data integration initiative should be driven by business, not IT. The project needs a thorough analysis of the requirements so it can be designed in a way that will achieve business goals. This includes having a business champion who understands the company's data assets and can lead discussions about the long-term aims of the data integration project.
Blog: The 6 Biggest Data Integration Challenges (and How to Solve Them)The most significant data integration challenge is often integrating data from disparate, often incompatible, sources.
To help design a successful project, you need to analyze thoroughly which systems the data will be sourced from, whether all the data to fulfill the requirements is available, the options for extracting the data, how often you'll need to run your extracts, whether the required data fields are populated correctly and consistently, and what volume of data you're going to need to process.
Beyond the technical integration, you'll need to determine who will manage the new system, what the support and SLAs are, and who will fund and manage ongoing maintenance. Considering future requirements early, whether your integration can scale to handle larger volumes or new sources can save expensive headaches later. Scaling and lifespan mistakes like these are common enough that we've covered them in their own dedicated guide: 16 costly data integration project mistakes.
As the number of systems involved in your integrations grows, so does the possibility of failure. Having a strategy for handling errors, including automated error notification, helps issues like downtime or poor data quality get detected and fixed quickly rather than discovered by accident.
White Paper: How to Design Your Systems to Effectively Control Bad DataCustom-coded data integration solutions can work perfectly well for simple or smaller projects. But as the code grows, maintainability becomes a severe challenge, logging often gets treated as an afterthought, integration with new technology is slow to implement, and the departure of a developer who carries undocumented knowledge in their head can leave a serious gap. We've covered this specific problem, and what replacing it looks like in practice, in Replacing scripting with modern data integration.
AI has added a new, urgent reason to get data integration right, and it's exposing gaps that were easy to overlook when the only audience for your data was a human reading a report.
According to CloverDX's own research of data leaders from organizations across the US and UK, 92% of organizations are already using AI in data or engineering workflows, but many still lack the data foundations to make it reliable at scale. A separate finding from the same research shows 36% of organizations cite data quality as a barrier to wider AI adoption specifically.
None of the challenges covered above go away once AI enters the picture, they just get harder to ignore. An AI model trained on poorly integrated, inconsistent data will confidently produce poorly integrated, inconsistent answers, just faster, and at a scale that makes the underlying problem harder to trace back to its source. Getting the fundamentals right isn't a separate project from becoming AI-ready, it's the same project.
This is also why the challenges above, business and IT alignment, error handling, and the risks of custom-coded solutions, are worth taking seriously now rather than treating them as background maintenance. Every one of them determines how much you can trust the data an AI system is working from.
Good data integration isn't just about getting the technical approach right, it's about the habits that keep it running reliably over time. These five data integration best practices are what separate integrations that stay trustworthy from ones that quietly degrade.
Monitoring every involved application is vital to prevent polluting multiple applications with low-quality data from a single source.
Data integration works with live systems that usually cannot be shut down while the integration is querying or updating the data. Proper resource monitoring will help ensure that the integration doesn’t negatively impact the involved applications. For example, monitoring response times and disk or memory usage can all help detect overloads of production systems.
Since data integration is a continuous process, implementation needs to be as robust as possible, created through rigorous testing before deployment to production.
It is good practice to test production data by copying it and configuring testing instances of all involved applications or systems. Such end-to-end testing can be beneficial not only to the integration itself but to the other systems involved in the integration.
When designing a data integration, it is always important to decide how to represent the data. If the integration copies data from one application to another (data propagation), the design is given by the applications involved.
However, for data consolidation and virtualization efforts, selecting the suitable representation of each entity can make or break a project.
If the data representation is hard to understand, it will be avoided, resulting in applications adding point-to-point interfaces rather than using a centralized data location.
Building a comprehensive, unified data model may sound like a good idea, but these attempts almost always fail due to sheer complexity (even with just a handful of applications). In practice, it’s best to start small, using only the minimum required data that serves the business needs.
Data integration and ETL get used almost interchangeably, but they're not quite the same thing. ETL is a specific process for moving and transforming data, while data integration is the broader goal that ETL, among other techniques, helps you achieve.
ETL stands for Extract, Transform, and Load, and is a process for getting data from one place (the source system) to another (the target) while performing some kind of transformation on the data in the middle.
The cleaning step in the process ensures the data maintains a high level of quality.
Whereas data integration can define any process that combines different data sources, ETL is most often used to describe the process of transporting data into a data warehouse specifically. However, ETL can also be used to talk about any process where the data is transformed between the source and target system.
The first step covers getting the data from the source system and making it available for processing, ideally using as little resource as possible and in a way that doesn’t negatively affect the source system’s performance, response times, or any kind of locking.
Extracts can be done in several ways, either by taking only the data that’s been changed since the last extract or a complete extract of the data, which is then compared with the previous extract to identify changes.
The frequency with which these extracts are performed is fundamental, as there can be a massive amount of data to parse. For example, full extracts can often involve volumes that reach tens of gigabytes.
Cleaning the extracted data is essential to maintain the quality of the target system. This includes converting phone numbers and ZIP codes to a standardized format, making identifiers unique, removing or standardizing missing values, and validating address fields, including against each other, so State and Country or City and ZIP Code values stay consistent.
What's the difference between data ingestion and ETL
The transform step applies a set of rules to transform the data from the source to the target. This includes converting any data to the same dimension (i.e., conformed dimension) using the same units so that they can later be joined.
The transformation step can also involve joining data from several sources, generating aggregates; generating surrogate keys, sorting, deriving new calculated values, and applying advanced validation rules.
The load step involves loading the cleaned, transformed data into the target system (often a database). During this step, it's important to ensure the load is performed correctly and use as little resource as possible. To make the process efficient, good practice dictates that any constraints and indexes are turned off before the load and enabled again only after the load is completed.
ETL? ELT? What's the deal?
What's the difference between ETL and ELT?
Using specialist data integration software can make dealing with a large number of data sources, formats, or transformations easier. It can also help automate your data integration processes to save manual effort, increase repeatability and reliability, and build in error monitoring and handling.
Choosing the right software to suit your organization's requirements can be a complicated decision. Before you start the process, it’s crucial to have a solid understanding of what you want to achieve.
Other things to consider are the technical details of where your data sits, what your future needs might be so you can scale easily, who will be using your software and their level of technical expertise, and how you plan to implement your solution, whether that's doing it yourself or getting an expert to help.
Your data integration process should be as streamlined and straightforward as possible. Key to that is automation. By replacing ad-hoc Python scripts, sprawling spreadsheets and other unmanaged tools with an automated and reliable data pipeline, it becomes possible to increase transparency and visibility across the pipeline, correct any errors quickly thanks to instant error alerts, reduce downtime, make collaboration easier, including for less technically minded users, and eliminate the risk of relying on a 'black box' or that one person who understands the process because they built it.
CloverDX has helped many businesses with their data integration, including integrating multiple applications for a consolidated, reliable view for reporting, helping clients move away from Excel spreadsheets into automated integration, bringing thousands of systems together while tracking large volumes of data, and recouping six working days a month thanks to data integration automation.
Data integration is a broad term, covering a considerable number of challenges and outcomes. We speak with you to learn what you hope to achieve and consider what might suit your business.
Some questions to consider will include:
We can learn more about your data integration needs by asking the above questions and others. Every business is unique, and your data may vary in quality, quantity, storage, format, etc.
From building robust architecture to frameworks, data integration takes many forms. Moving on from scripts or Excel can seem daunting, but our customers set the pace and direction of travel. You decide what you need; we find a way to make it happen.
In summary, your business defines what data integration means to you. Want to save time and resources? You can. Are you hoping to reduce instances of error and enable better collaboration? It’s possible.
Whatever your goals, we’ll always do our best to help you achieve them and create data pipelines that are automated and scalable. The ultimate goal is to save on time, cost, and stress for your business.
Learn more about how CloverDX helps modernize your ETL.
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Data integration isn't a one-time project with a fixed end date, it's the ongoing discipline that determines whether reporting, automation, and now AI, can actually be trusted. The types, techniques, and best practices covered here give you the framework; the challenges section gives you a realistic idea of where things tend to go wrong.
Data integration is the foundation everything else, reporting, automation, AI, gets built on. Let's talk about what a stronger foundation could look like for your team.