Today’s businesses compete on how fast and well they can glean meaningful insights from their datasets to generate better products, services, and, ultimately, experiences. The faster you can access insights from your data, the faster you can move into and lead your market.
But how do you glean those insights when you’re dealing with massive volumes of big data, different data sources, different systems, and applications?
Easy — enterprise data integration.
Well, maybe not “easy.” But don’t worry. That’s why we’ve put together “What Is Data Integration? The Ultimate Guide.”
What Is Data Integration?
Data integration is the process of bringing together different sources of data to make the data more useful. Brands can create a 360 view of their customers by integrating data from their marketing, sales, support, product, and finance tech stacks. This high-level view empowers them to make informed product, pricing, and position decisions.
Say your marketing team collects customer website activity data on landing pages while your support department tracks customer activity on your brand’s knowledge base webpage. A product team member might integrate these datasets and discover that customers often reach knowledge base resource pages from product landing pages — leading them to include more product resource pages on their landing pages to help educate customers.
Is Data Integration the Same as ETL?
ETL (extraction, transformation, and loading of data) is a type of data integration that involves moving data from transactional or operational systems into a data warehouse or data lake for analysis and reporting.
So, while ETL is a type of data integration, not all data integration involves ETL. For example, real-time data integration and data federation do not need the ETL process.
Why You Should Care About Your Data Integrations
Wherever you are in your digital transformation journey, data integration is the key to moving forward.
Data integration drives deep collaboration and increases efficiency by making workflow automations possible. The result? Your team members can spend more time analyzing data to make informed decisions instead of searching for information and handling simple admin work.
If you don’t believe in building high-quality data integrations, just look at your competitors.
Many brands are recognizing the competitive advantage of connecting their data sources to improve their workflows. Through 2024, manual data integration tasks will be reduced by up to 50% through the adoption of data fabric design patterns that support augmented data integration, according to the Gartner Magic Quadrant for Data Integration Tools. Machine learning and artificial intelligence will drive further growth in the integration sector in the years to come.
McKinsey also predicts that employees across all sectors will use data in their processes — creating a need for advanced integration solutions. The consultancy even expects companies to treat data assets as products, with dedicated teams responsible for integrations.
Convinced? Read on to learn about data integration types and methods, so you can start forming rich insights based on your organization’s connected information.
What Are the Different Types of Data Integration?
Data integration can be divided into different types based on how and why data is being moved, stored, and used.
1. Data Migration
Data migration is the process of moving data assets from one platform to another, making it a form of integration. You’ll hear this term when data is being moved from an on-premises database to a cloud-based one or vice versa. Migration also applies to moving data assets from one application to another.
2. Master Data Management (MDM)
Master data management (MDM) is the process of creating a single dataset for the whole organization to use. The dataset serves as a single source of truth for your organization and is called master data. It contains information on the core entities of a business, such as the customers, products, services, location, and pricing. For MDM, a robust integration model is needed. That’s because data from internal and external sources has to be reconciled, enriched, and de-duplicated before it goes into the master dataset.
3. Enterprise Application Integration (EAI)
Just like the name suggests, EAI refers to the integration between different applications and databases. You need data from within your tech stack to flow from one app to another, such as customer data going from HubSpot to Salesforce or employee data going from your LMS to your HRIS. To achieve that, you integrate those apps together using built-in, custom, or third-party integrations. All such integrations fall under the category of EAI. EAI is critical because it helps create omnichannel customer experiences.
4. Data Aggregation
Data aggregation is the process of gathering and compiling data to be either stored in its raw form or prepared for analytics. Think of a marketing campaign that uses email, social media, and pay-per-click advertising to promote an online event. The data about each marketing channel will live originally in separate tools, so it will need to be aggregated into a single report or dashboard before the overall performance of the campaign can be analyzed.
5. Data Federation
Data federation is the process of creating a virtual database that shows an integrated view of data. The virtual database does not store the data, only information on the data’s location.
6. Data Lake
A data lake stores massive amounts of raw data that has not been given a purpose or prepared for usage. It is a result of integration between multiple external and internal sources of data. This data may or may not eventually be used, but it is stored and held for its potential value. It is a repository for unstructured and structured data alike.
7. Data Warehousing
Data warehouses store structured data from multiple sources. This data first goes through the ETL process. It is loaded into the data warehouse, where it is then used for reporting, analytics, business intelligence, and data virtualization. Examples of popular data warehouses include AWS Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, and SAP Data Warehouse Cloud.
What Are the Different Methods of Integrating Data?
So, how do you integrate your data? There are several approaches, ranging from manual integration to data integration platforms. Sometimes, these methods are used in combination to build customized integration architectures.
Manual integration can be achieved by using point-to-point integration. In this model, developers integrate different apps and databases using custom code and application programming interfaces (APIs).
In uniform access integration, developers set up customized dashboards and reports that pull data from multiple sources and present them to the data analyst in a unified view. This can be done through custom software or SaaS solutions. The original data stays at its source and is only replicated at the time of the analysis.
This type of integration does not require a lot of storage and is highly scalable. But analysts do need to make data access requests with this type of integration, which puts a lot of strain on the servers.
Common storage data integration uses the same methods as uniform access integration, with the addition of a data warehouse. Data from multiple sources is pulled in, transformed, and stored in a warehouse. The data can then be used for analysis.
You don’t need to make multiple data access requests with common storage, reducing the load on your servers. But you need to invest in on-premises or cloud-based data storage.
Any software system that serves as a bridge for data to travel from one app or database to another is called middleware. With the right middleware, you can integrate every application in your tech stack.
Integration through integration platform as a service (iPaaS) is a type of middleware integration. iPaaS comes with advanced capabilities to serve as an integration hub for all of your organizational data. Other types of middleware may only serve as a bridge between two apps. Integration platforms like SnapLogic help you create workflows, uphold data quality standards, and take complete control of your integration architecture.
What Should You Look for in a Data Integration Solution?
Your data integration tool should make your process faster, more accurate, and easier by using AI and ML to prepare and process data.
Beyond this high-level requirement, here are some questions to ask when reviewing data integration platforms:
- Is it scalable? You may need to scale up or out as your data grows or you bring in third-party apps and databases.
- Can it manage environments from public to private and on-premise? You may have to integrate datasets from any combination of the three.
- Does it offer click, not code, for easier integration? No-code solutions will make it easy for your team to build and edit workflows and integrations.
- Is it robust enough for developers but easy enough for your business teams to use? You need both technical and non-technical people to manage your integration architecture.
Strengthen and Simplify Your Data Integrations with SnapLogic
The SnapLogic integration platform is here to make your data integration process more efficient and easier as a no-code solution. It offers hundreds of pre-configured integrations, and you can use drag-and-drop options to build more. Our Iris AI — an AI-powered integration assistant — also provides proven recommendations and guidance for smarter integration.