In today's "Big Data" world, data integration is a critical business process. With the exponential growth of information seen in enterprises all over the globe and increasingly competitive marketplaces, there is a real need to consolidate and analyze business data. 

Big Data is made up of large amounts of information from many sources: customer interactions, Internet of Things (IoT) devices, mobile apps, SaaS cloud services, and more. Data integration empowers businesses to consolidate all these diverse data sets into a unified, single view. After integration, all this disparate data can be used for analytics, business intelligence, and decision-making.

With insights from this business intelligence, companies gain a competitive advantage in the marketplace. It all starts with data integration.

Every year, advancements in technology bring improvements to data integration. New techniques optimize data pipelines and streamline the process. Here is a look at the latest data integration trends and their use cases.

Key takeaways from this years data integration trends:

  • Data warehouses have been the traditional destination for integrated data. Data lakes and data lakehouses are gaining in popularity.
  • AI and automation are rapidly changing how data is generated, collected, and integrated.
  • Businesses are looking for ways to achieve real-time data integration so that they gain insights into customers and the marketplace faster.
  • Self-service and no-code data integration tools are becoming increasingly popular in the enterprise.
  • There is an increased focus on security and privacy in the data integration process.

In this article, we will explore the most important data integration trends that are impacting businesses in 2023.

Table of Contents

What is Data Integration?

Data integration, also known as data ingestion, combines data from multiple sources and locations and stores it in a centralized repository.

One of the most popular methods for data integration is the ETL (extract, transform, load) process. A typical ETL workflow consists of these steps:

  1. Information is extracted from various data sources, such as databases and software applications.
  2. The extracted data is transformed from its source format so that it will fit in its destination.
  3. The transformed data is loaded into the destination, such as a data warehouse.

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The ELT (extract, load, transform) method is a popular variation on ETL. With ELT, the transformation phase is performed on the destination system. Some organizations find ELT provides more flexibility in the transformation process. It's common to see ELT used in data warehouses with large amounts of processing power.

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Another data integration approach is data virtualization. In this process, all data remains in its original locations, and the destination creates a sort of virtual data warehouse. From there, the information goes through a data modeling and abstraction process. This is especially useful for organizations with larger data sets needing real-time analytics.

Whichever approach is used, data integration provides a unified view of information from multiple sources. Once unified, viewing related information or performing data analysis is easier.

The types of data to be integrated varies depending on business needs. For example, e-commerce companies tend to focus on customer data. Healthcare providers will need to integrate patient data from different systems. Manufacturing organizations might need to unify data from IoT devices.

There can be many different sources for data to be integrated. Here's a look at some of the most popular systems for source data and some use cases.

Relational (SQL) and non-relational (NoSQL) databases contain vast amounts of enterprise data. They may be used as standalone data storage platforms or the backend for enterprise apps like ERP (enterprise resource planning) applications. A wide range of financial and operational data can come from databases and provide high-level insight once integrated.

CRM (customer relationship management) applications store a wealth of data on customer interactions. Phone calls, emails, in-person visits, quotes, sales, and other customer-facing activities are typically logged in CRM systems. Integration with other enterprise data can reveal high-level customer and marketplace information not found in typical data analytics.

Cloud SaaS (software as a service) apps power many business processes and operations today. They provide critical functionality, all backed up by data. Most SaaS applications provide access to data via APIs (application programming interfaces). When this information is included in a data integration strategy, companies gain an analytical view of their business that they didn't have before.

Although data warehouses are a common destination in the data integration process, other options exist. This includes:

  • Data marts are "mini data warehouses" that typically hold information for smaller teams.
  • Data lakes are like data warehouses for unstructured data. 
  • Data lakehouses provide the functionality of both data lakes and data warehouses.

Creating an excellent data integration strategy

Regardless of the sources and destinations, a data integration strategy is needed. The transformation stage of ETL can include a cleansing process for more accurate data. This step ensures that data analytics users receive insightful, high-quality insights. 

A list of cleaning criteria is just one of many things included in a data integration strategy. A robust data integration workflow can improve business processes, cut costs, break data silos, and strengthen data governance. Here are some of the steps required for a great  data integration strategy:

  • Define your objectives: Clearly define your strategy's objectives. Think about the insights you hope to gain from integrated data and make that your end goal.
  • Know your data sources: Perform an audit of all your enterprise systems to identify data sources. Research how information can be retrieved from each system (for example, via API, SQL query, file export, etc.).
  • Establish data governance: Create a data governance framework that will ensure data consistency and quality. This step is about defining data standards and creating the touchpoints for integration.
  • Plan for growth and scalability: Make sure your data integration strategy will be scalable and cover your company's growth. Consider the increasing amount of data over time as technology evolves, and more data is available.
  • Create KPIs and metrics: Assess the quality and timeliness of the information you expect from data integration. Establish key performance indicators (KPIs) and other metrics to help you evaluate how well your data integration practices meet your goals.

Use these basic steps as the start of a data integration strategy that suits your organization's needs.

Top Integration Trends in 2023

As digital transformation continues to be an enterprise trend, data management has become a priority for businesses. Analyzing the wealth of data that most companies have is the key to understanding customer needs and discovering new market strategies. A recent study shows that 72% of companies believe data management and analysis are essential for accomplishing business goals.

Thus, Data integration and management strategies have become a priority for most businesses. Many seek new and unique ways to improve data quality and make data-driven solutions. This has led to some identifiable integration trends in integration solutions.

Data Integration Trends in the Cloud

In the era of remote and hybrid work, cloud computing offers enterprises the advantage of secure access to company data from anywhere. But this created the need to incorporate the cloud into their data integration strategies.

Cloud-based data integration solutions, such as cloud data warehouses, metadata storage solutions, and data lakes, will be more popular than ever in 2023. These platforms provide a centralized and scalable solution for managing enterprise data without the investment in hardware. Companies gain faster and more efficient data integration and scalability while lowering costs.

In other cases, enterprises are adopting hybrid and multi-cloud environments. With these strategies, businesses can leverage the benefits of cloud applications and their existing on-premises solutions. Deploying multiple cloud-based solutions from various providers creates a flexible data integration environment.

Artificial Intelligence (AI) and Automation

Every tech conversation today includes a discussion of AI, and data integration is no different. A significant number of businesses are taking on AI initiatives in 2023 and gaining new integration solutions.

AI-enabled data processing has the potential to reduce errors and improve analytical accuracy. AI-powered automation promises to help businesses make informed decisions based on rapid enterprise data analysis. Machine learning (ML) algorithms create visual data representations to help detect customer and market behaviors.

Companies use ML by analyzing customer purchases and then taking action. If a customer buys a certain product or spends over a certain amount, AI and ML aggregate data to see what they will likely buy next. Sales staff can use this info for their next interaction with the customer, or AI can set pricing that's statistically likely to encourage another sale.

AI is also used for data transformation tasks, speeding up the second step of the ETL process. The algorithms learn the relationships between bits of information, speeding up data cleansing. AI could even theoretically take care of inserting integrated data into a data warehouse or data lake, bringing a high level of automation to the ETL process.

Real-Time Data Integration

With the proliferation of mobile devices and the IoT, most companies produce massive amounts of data daily. Being able to analyze that data quickly becomes increasingly important, and this has created the need for real-time data integration.

Simply put, real-time data integration can be defined as processing and transferring information as soon as it's collected. Legacy data integration revolved around batch processing, typically after hours when company resources were freed up.

On-demand data integration that operates in real-time is replacing older ETL methods. It reflects a shift toward faster data management and performing faster data analysis. The end goal is to give enterprises faster access to valuable insights into their customers and their marketplace.

Data Integration in the Customer Experience

Real-time data integration is also important for customer experience. Customers have come to expect personalized responses delivered quickly. 

Whether your company communicates with customers over the phone, by email, or via SMS, they expect a connected, seamless experience. As customer data comes in, it's critical to integrate it in real-time to keep up with modern customer expectations.

Companies can expect to try to collect more customer data to feed into their data integration processes. This might take the form of quizzes, surveys, or even pop-ups on company websites. The collected information will be analyzed later to gain more insight into customer preferences.

Self-Service Data Integration

A part of making real-time data integration a reality is implementing self-service solutions. 

Most self-service data integration tools will have a simple drag-and-drop "no-code" interface. The complicated parts of data processing take place in the background, making these tools user-friendly for all employees.

With non-technical employees able to work with their own data without the IT department's intervention, a data management bottleneck is eliminated. Self-service tools can work with data from cloud applications, enterprise databases, and even APIs. This approach keeps companies agile and focused on their data.

Increased Focus on Security and Privacy

As we increasingly work in a data-driven economy, data privacy and security become critical subjects. Data breaches present a significant risk to a company's finances and reputation.

There is now a greater emphasis on security in data integration processes. Encryption technologies are quickly becoming the standard. Access control measures are also an area of focus, particularly with cloud applications.

Customer data is of particular importance. A breach that leaks information about customers would be particularly damaging to a company's reputation, in addition to any financial risks. The role of CRM systems (and their security) in data integration will receive more scrutiny.

Integration With Blockchain and Other Emerging Tech

There are many uses for blockchain technology besides being the ledger for Bitcoin transactions. Blockchain provides a verifiable record of transactions, making it a natural fit for data integration. The cryptography capabilities of blockchain enhance data integrity and security, giving business stakeholders peace of mind.

Blockchain is also emerging as a component of data fabric architectures. The data fabric framework was created to help hybrid and multi-cloud enterprise environments achieve seamless data integration. Companies with multiple locations that work with large amounts of data sources and formats benefit the most from data fabric. Increasingly, blockchain technology provides a governance layer and foundation in data fabric implementations.

A similar large-scale approach is to use edge computing tech. This is another emerging technology using distributed computing to process larger data sets. With edge computing, devices process data at the "edge of the network" before sending it to a central location. In essence, edge computing is ETL done on a large scale across a large network or the cloud. Offers the Latest in Data Integration

Looking at these trends, data integration is more important than ever. New integration technology, platforms, and approaches are emerging rapidly. That's because more businesses than ever are prioritizing data integration and are looking for ways to make it faster, more accurate, and more secure.

Staying on top of all these data integration trends can seem overwhelming. Fortunately, offers your business a complete solution.

The platform is perfect for all of the latest integration solutions. We offer simple workflows, enhanced security, compliance with data governance frameworks, and everything you would expect in a modern integration solution. Try it out yourself and sign up for a free 14-day ETL Trial. Or alternatively, schedule a demo with one of our experts. We'll help you get the most from your trial so you can learn if is the right solution for your data integration needs.