By Beate Thomsen, Co-founder & Product Design - November 12, 2024
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People often refer to data as the new oil. And it’s not an exaggerated metaphor. Data fuels everything – from delivering top-notch customer service to shaping strategic decisions.
Modern businesses are gathering more data than ever before from countless sources.
Legacy systems, sensors, log files, mobile devices – all of these generate data that give companies business intelligence and insight. That’s the exciting part.
But here comes the challenge – making sense of this avalanche of information.
Now, that’s what data integration solutions are for. But what exactly is data integration, and how does it work?
This comprehensive guide will introduce you to the fundamentals of data integration and how it can help your business thrive.
Just like putting puzzle pieces together to create a full picture, data integration is the act of combining information from disparate sources into a unified repository.
In other words, it’s the process of combining data from different sources in order to get a complete and accurate view of your business, which can help you make better decisions about stock, pricing, and marketing.
It’s time to put this definition into perspective with the help of examples.
PERSONAL EXAMPLE: CITY BREAK
Imagine you’re looking for information on a city you plan to visit for the first time. You set your dates, bought tickets, and now you need to find information about the city’s history, location of your hotel, places of interest to visit in that city, weather, traffic, food, people, and even local customs.
All this information may be found in different sources/databases. But that’s not very convenient, is it? What if all this separate data was consolidated into one place? This way you get a comprehensive overview of all city-related information – all at once and in one application.
B2C EXAMPLE: BOOKSTORE
Imagine you own a bookstore. There you use three separate systems: a cash register, an inventory system that tracks books in stock, and an online ordering system for customer purchases.
Each system contains valuable information, but they're all separate. Data integration would involve combining the information from all three systems into one central database or system.
This would help you see which books are selling best both in-store and online, automatically update your inventory when a sale is made, and identify trends in customer preferences.
B2B EXAMPLE: EQUIPMENT MANUFACTURER
Imagine you manufacture and sell industrial equipment to other businesses.
Typically, you would have a few separate systems, such as a CRM solution, an ERP system, a support ticketing system, and an accounting system. That’s a lot of data you need to look up in at least four different places.
By integrating all the systems and allowing data to flow seamlessly between them, you can turn disconnected data into a unified tool for improved customer service, more efficient day-to-day operations, and better decision-making.
HOW DOES DATA INTEGRATION WORK?
Put simply, data integration is like gathering and organizing scattered information so you can easily understand the whole story. So what does this process look like?
Let’s take a look at the structure of how different data sources are connected and combined into a unified system. There are eight key components of the data integration structure.
Data sources
The original locations where data is stored or generated, like databases, applications, or even external systems.
Data extraction
The act of pulling data from various sources.
Data transformation
This process of cleaning, formatting, and standardizing the data so it all "speaks the same language."
Data loading
The act of putting transformed data into its final destination, often a data warehouse.
Data storage
The central place where integrated data is kept, which can be accessed and analyzed centrally.
Data access layer
The place where users or applications interact with the integrated data, like a user interface or API.
Metadata management
This step involves tracking of information about the data itself, for example where it came from and when it was last updated.
Data governance
Rules and processes to ensure data quality, security, and proper use.
To better understand this structure and the process of data integration, let’s consider this example.
EXAMPLE: CUSTOMER DATA
A typical company has three main departments (sales, support, and marketing) that collect customer information in separate systems:
Sales collect information on customer purchases; customer support collects details about customer service requests and complaints; and marketing tracks email interactions and campaigns.
In order to see everything about a specific customer in one place, the company needs data integration, which involves these steps:
Extract customer data from all three departments.
Transform the data. For example, standardize formats, like making sure phone numbers are written in the same way.
Load the combined data into one system that shows a complete view of each customer, including their purchases, service requests, and emails.
Such a process brings numerous benefits to the business.
First of all, you get a complete picture instead of scattered bits of information. Second, you can see trends and insights that you might miss when data is separate. Third, you can save time by having all data in one place rather than searching through multiple systems.
MOST COMMON METHODS OF DATA INTEGRATION
The two most common methods of data integration are ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
ETL
ELT
Extract → Data is pulled from its source system.
Transform →The extracted data is cleaned, standardized, and manipulated to match the target system's requirements.
Load →The transformed data is then loaded into the target system.
Extract → Data is pulled from its source system.
Load →The extracted data is loaded into the target system without any transformations.
Transform →The extracted data is transformed inside the target system.
While ETL is a traditional approach that emphasizes data quality and consistency before loading, the ELT method prioritizes speed and flexibility, allowing for more dynamic and scalable data integration processes.
The choice between ETL and ELT depends on the data volume, its complexity, and specific business needs.
TYPES OF DATA INTEGRATION
As with every other technical process, the type of data integration that is right for you depends on your business needs and the goals you set. That analysis should precede all your decisions.
But for now, let’s go over the main types of data integration out there.
❗Note: Of course, you can always go down the simplest path and perform a manual data integration, in which you manually collect data from different sources and merge it all in a spreadsheet.
But it’s outdated, time-consuming, and prone to errors. Also, you wouldn’t be reading this guide, if you wanted to do that, would you? A dedicated solution is a way more reliable method.
Consolidation Integration
Like gathering all your information into one big container, this type of data integration typically involves businesses combining data from all of their locations into a central database.
Data is typically extracted from source systems using specialized software or scripts. Then, extracted data is securely transferred to a central location. After that, the transferred data is cleaned, formatted, and standardized, and finally loaded into the target system.
The key benefit of consolidation integration is that you get a single source of truth for all company data, which makes it easier to analyze and make decisions based on the full picture.
➡️ For example, a healthcare organization collects patient records from different clinics, hospitals, pharmacies, etc. The central system takes care of connecting and integrating this data, so doctors can view a patient’s complete medical history in one place.
Application integration
Making different tools work as one, this type of data integration connects different software systems and makes them work together. In this case, it’s a special application that pulls data from various sources, processes it, and integrates it into a unified view.
It’s great because all the work is done by a special app. Yet, on the flip side you may need multiple tools for different kinds of data.
➡️ An example of the application integration is when a CRM system is connected to an email marketing tool, making sure that customer information is always up to date in both.
Propagation integration
This type of data integration is based on copying or moving data between systems in real-time or on a schedule.
During this data movement, each system keeps its own copy of the data, and the data is propagated (transferred) from one system to another, typically triggered when changes are made in the source system.
The propagation integration type offers real-time updates, while each system has its own copy, eliminating the need for a central data storage space.
But it can be quite difficult to keep all systems synchronized due to sheer volume of changes and systems involved. It also can lead to data redundancy – when the same data exists in multiple places.
➡️ For example,a bank can update a client’s account balances across all its systems immediately after a transaction.
Federation / Uniform Access integration
It’s a type of data integration that keeps data in different systems without moving it. Instead, the systems are linked together. This type allows linking data across systems without copying it, offering a virtual view of all the data together.
Users can access and query the data across these systems without physically combining it into one place. The idea is to create a federated view or a virtual system that connects all the data from different sources and presents it as if it’s all in one place.
It's useful when you need real-time access to data from multiple sources without the hassle of physically moving or duplicating the data. It also saves storage space. The downside is that it can cause performance issues or slow down the response times.
➡️ To illustrate, imagine you’re looking for information about a movie: release date, reviews and ratings, and whether it’s on a streaming platform. This information is stored in different databases. Instead of checking them all separately, you can enter a search for a movie, and the system pulls together data from all three sources, presenting you with a unified view of the movie’s release date, ratings, and where you can stream it.
Mediated integration
With this integration type, the central system takes charge by integrating data from multiple sources. This central hub manages everything and users only interact with it to get relevant information.
It’s a super-centralized way of data integration, but may require advanced systems and setup.
➡️ For example, a travel agency offers services like flights, hotel bookings, and car rentals. Each of these services is managed by different external providers. The agency’s central system then communicates with the databases of the external service providers in real time – to bring all the information together.
Middleware integration
Like a translator helping two people who speak different languages communicate, the middleware integration connects different applications. Basically, it sits between different systems and manages the flow of data between them.
Not storing data itself, middleware helps systems (that may not otherwise be compatible) work together. It converts data from one format to another, enabling smooth interaction between systems.
Middleware also synchronizes updates in real-time or on a schedule and routes messages from one system to the other, ensuring that everything works smoothly behind the scenes.
The good thing about this type of integration is that it makes sure the data is flowing between systems smoothly and reduces the need for custom coding by offering ready-made solutions for connecting systems. The bad thing is that it can be complex to configure and costly to implement and maintain.
➡️ For example, a retailer uses middleware to connect its inventory system and its point-of-sale system with its website.
Data warehousing integration
A specific implementation of consolidation integration, this data integration type is based on storing data from various sources in one central repository, called a data warehouse.
Data warehousing typically involves transforming data into specific schemas optimized for analytical processing and historical reporting. Even though this type of integration often involves batch processing, real-time data warehouses are becoming increasingly more common.
The warehousing of data is a convenient way to access up-to-date information. Yet, it requires significant setup and resources to maintain.
➡️ An example of this integration is a retail chain that stores data from all its stores, online sales, and customer service in one data warehouse for analysis of the stock.
Data streaming integration
Like a news ticker that updates constantly, the data streaming integration processes and integrates data in real-time, as soon as it’s created or updated.
This way the users always get the latest information as it occurs. Great for time-sensitive data handling, this type of integration requires sophisticated infrastructure to handle real-time data flows.
➡️ For example, in a smart home system, devices like thermostats, security cameras, and lights stream data continuously to the central automation system. This real-time data integration ensures that devices respond instantly to events, such as temperature fluctuations or turning on lights when motion is detected.
Data virtualization integration
Unlike other types, data virtualization doesn't move or copy data to a central place. Instead, it creates a virtual layer that lets users access and view data from multiple sources – even though the data is still in its original locations.
The good thing about this type is that there’s no need to physically move or store data, saving time and resources. However, the overall performance can suffer if the original data sources are slow or unavailable.
➡️ For instance, a business stores customer data in multiple databases but uses a virtual integration tool that allows employees to access and query customer information across all databases without physically moving the data.
BENEFITS AND CHALLENGES OF DATA INTEGRATION
Anybody who’s tried to fetch information from multiple systems for reporting purposes understands the importance and the complexity of data integration. And in the era of big data, it doesn't get easier due to large volumes of data.
So, it’s time to analyze the pros and cons of data integration for businesses. This should help you weigh in all the risks and make a better-informed decision.
BENEFITS
There are many generic benefits behind data integration.
From improved decision-making based on facts not guesses and better efficiency due to elimination of manual data gathering – to increased collaboration between teams, access to the same set of information, and more comprehensive analysis of trends and patterns.
But, let’s zoom in on the most common and practical example of a CRM-ERP integration, and examine why combining these two make a huge difference for a company’s operational success.
Here is how companies typically benefit from the CRM-ERP integration:
Reducing manual data entry – Getting rid of errors, freeing resources, saving times, and focusing on value-adding tasks.
Optimizing and automating business processes – Improving the sales process, getting instant access to business-critical data, and planning ahead.
Having a 360-degree view of customers – Better insights into their customer base, longer and stronger relationships with customers, and ability to determine whether it’s time to take relationships to a new level (e.g. upsell or cross-sell).
Securing high system ROI – Through increased user adoption, ability to always find information at any time, better daily performance and productivity indicators.
Better customer service – Due to easy access to real-time information about inventory, orders, and shipping, and ability to provide accurate and timely responses to customer inquiries.
CHALLENGES
Many of the companies that embark on a data integration project find themselves faced with a few serious challenges. Most of the time it is because of the complexity of a data integration project.
Let’s dissect the key things you should be aware of.
Too many systems used. As data volumes increase with the number of data sources and data warehouses you have, you may end up with too many systems. Some run on multiple platforms (e.g. on-premise, cloud or private hosting), in various versions and different geographical locations. When data resides in so many silos it adds a whole new complexity and security dimension to a data integration project.
Complexity of different systems. Businesses often use many different systems with unique data formats and structures, making it tricky to get them to communicate and share data smoothly.
Customizations made to the systems. Most implementation projects include extra customization and development efforts to support enterprise, division or user-specific working processes and habits. This can result in hundreds of custom modules or features, which can turn into a maintenance and upgrade nightmare.
Data quality issues. If the data in different systems is incomplete, outdated, or inconsistent, integrating it can be difficult and may lead to unreliable results. Also, errors caused by inconsistent data and manual data entry can prove very costly for companies and disrupt business activities.
Lack of a consolidated approach. In some cases, data integration is approached as a multitude of point-to-point custom integration scripts without a common direction. In that case, a data integration project may fail to deliver the desired unified view of business data.
High cost and time. Money, time, and skilled personnel are the essential elements of any data integration project; this can be a strain on a company’s budget and timeline.
Security and privacy concerns. When integrating sensitive data from multiple sources, you need to make sure it’s protected from breaches and meets privacy regulations – not always an easy thing to do.
WHO NEEDS DATA INTEGRATION?
There are a number of business types and industries that benefit greatly from data integration.
The key reason behind the need to integrate data is the need to handle large volumes of data from various sources. By integrating data, various industries can operate more efficiently, make better decisions, and improve their services or products.
In this chapter let’s quickly overview the main industries that tend to opt for data integration. Here's a short list of industries that typically use data integration, along with explanations of why they do it.
RETAIL CHAINS
They use data integration to merge in-store and online sales data, inventory levels, and customer information for improved stock management and personalized marketing.
FINANCE
They use data integration to bring together transaction data, market information, and client profiles for risk assessment, fraud detection, and personalized financial advice.
MANUFACTURING
They use data integration to connect data from production lines, supply chain, and quality control for optimizing operations and predicting maintenance needs.
HEALTHCARE
They use data integration to combine patient records, lab results, and insurance information for better patient care and more efficient operations.
EDUCATION
They use data integration to access student records, course data, and learning management systems for better tracking of student progress and tailoring educational experiences.
LOGISTICS & TRANSPORTATION
They use data integration to merge GPS tracking data, traffic information, and delivery schedules for route optimization and improved delivery times.
TELECOMMUNICATIONS
They use data integration to combine network performance data, customer usage patterns, and billing information to improve service quality and create targeted offers.
ENERGY AND UTILITIES
They use data integration to combine consumption data, grid performance metrics, and weather information for better energy distribution and predictive maintenance.
AGRICULTURE
They use data integration to combine weather data, soil sensor information, crop yield history, and market prices for better farm management and decision-making.
DOs AND DON’Ts OF DATA INTEGRATION MANAGEMENT
The need for consolidating data has existed for a long time – much before modern data integration tools were developed. IT executives have been fighting data silos ever since IT systems have started collecting data in different systems.
In the beginning, integrating multiple data sources typically meant a lot of ad hoc hand coding between different data sets. This resulted in an expensive solution with difficult maintenance.
Often, these integrations were developed from scratch in-house or by a partner and poorly documented. And, if the developer who developed them left the company, updating or modifying the integration was a real headache.
Luckily, today’s situation is different. Modern data integration solutions are made to handle data in an efficient, transparent and highly adaptable manner.
Here are some practical tips of what to do and not to do when handling a data integration project.
DO:
✅ Have a clear strategy in mind. Define your goals and what you want to achieve with data integration. This will guide your efforts and help you measure success.
✅ Start small and scale up. Begin with a pilot project or a single department and learn from this experience before expanding to the whole organization.
✅ Involve all stakeholders. Get input from different departments, because they know their data best and can help identify important integration points. Listen to their feedback and opinions.
✅ Prioritize data quality. Clean and standardize your data before integration. Bad data in – means bad data out, no matter how good your integration is.
✅ Implement strong data governance. Set clear rules about who can access what data and how it should be used. This maintains security and ensures proper data use.
✅ Invest in the right tools and avoid hand-coded, homemade solutions. Choose integration tools that fit your needs and can grow with your business. The right tools can make the process much smoother.
✅ Give training to your teams. Make sure they know how to use the integrated data system, because it will amplify the benefits of your integration efforts.
✅ Plan for maintenance. Data integration isn't a one-time task. Plan for ongoing updates and improvements to keep your system effective.
DON’T:
❌ Ignore data privacy laws. Be aware of regulations like GDPR or CCPA. Violating these can lead to hefty fines and damage your reputation.
❌ Underestimate the time and resources needed. Data integration is a serious commitment, so be realistic about what it will take and plan accordingly.
❌ Neglect documentation. Keep clear records of your data sources, processes, and any changes made. This is crucial for troubleshooting and audits.
❌ Forget about data backup. Always have a backup plan! If something goes wrong during integration, you need to be able to recover your data.
❌ Overlook the importance of metadata. Keep track of where your data comes from and what it means. This context is vital for proper data use and future integrations.
❌ Assume one size fits all. Different types of data may need different integration approaches, so be flexible in your methods.
❌ Rush the process. Take the time to do it right. Rushing can lead to errors that are costly to fix later.
❌ Ignore user feedback. Listen to the people who will be using the integrated data. Their input can help you improve the system and ensure it meets real needs.
Remember – data integration is a process, and it requires ongoing attention and adjustment, but when done right, it can take your business to new heights.
HOW TO CHOOSE THE RIGHT DATA INTEGRATION TOOL
All of the challenges that a data integration project may entail can easily be overcome, if you choose the right data integration solution for your business.
Making the right choice is vital – it will allow you to bring all elements together and get the desired one view of your data.
CHECKLIST
When evaluating a data integration solution, you need to make sure that:
The solution is proven, stable and reliable. The solution is easy to understand on a technical level, and intuitive and simple to use for your business users, especially if your team isn’t very tech-savvy. So, look for user-friendly features, easy setup, and clear instructions. The solution is able to connect to the software and databases your business already uses. It should be able to integrate your current systems without major changes. The solution can spot errors, monitor data transfers, and proactively alert your administrators when an error occurs. The solution can scale with your business and when you add new data sources. The solution provides strong security features like encryption and access controls to protect your data. The solution comes with good customer support and detailed guides or tutorials – this way you’d be able to troubleshoot problems quickly if something goes wrong.
💡Bonus tip: Think about how fast you need the data to move between systems. If you need instant updates, look for tools that offer real-time integration. For less time-sensitive tasks, batch processing (scheduled updates) might be enough.
DATA INTEGRATION WITH RAPIDI
Data integration systems, such as Rapidi Data Integration Solutions, come out-of-the-box with a number of pre-configured integration points between pre-defined systems.
Providing simple solutions to complex data integration problems, Rapidi offers a number of data integration solutions – from simple to fully flexible, all coming with ongoing customer support.
What's the difference between batch integration and real-time data integration?
Batch integration processes data in large groups at scheduled intervals, e.g. nightly or weekly. This method is efficient for handling large volumes of data but requires periodic updates. Real-time data integration processes and updates data continuously as it's generated or changed. It provides up-to-the-minute information but requires more system resources.
How does data integration support Artificial Intelligence and Machine Learning?
Data integration is crucial for AI and ML as it provides these technologies with comprehensive, high-quality datasets. By combining data from various sources, businesses can create richer training sets for machine learning models, leading to more accurate predictions and insights. Integrated data also allows AI systems to access a wider range of information, enabling more sophisticated analysis and decision-making capabilities.
What is the role of APIs in data integration?
Application Programming Interfaces (APIs) provide standardized ways for different systems to communicate and share data, allow for real-time data exchange, facilitate easier integration of cloud services, and enable more flexible and scalable integration architectures.
APIs also support the development of microservices and event-driven architectures, which are becoming increasingly important in today's digital landscape.
How does data integration impact data governance and compliance?
Data integration affects how companies handle and protect their data. It requires clear rules for data quality and ownership, plus strong security measures. But it's not all extra work – having data in one place can actually make it easier to follow privacy laws. You can apply rules consistently, respond to data requests quickly, and show you're following regulations like GDPR or CCPA. So, while it adds some challenges, data integration can be a big help with data compliance.
About the author
Beate Thomsen, Co-founder & Product Design
As co-founder of the Rapidi Data Integration platform, Beate has spent over 15 years on its development, building it around her motto: 'keep it simple, functional yet beautiful.'
Your business is 10 steps away from perfectly integrated data systems. Learn about key preparation, best practise and more in our data integration handbook.
