The Future of Data Integration

Big data remains more important than ever to meet the goals of businesses in just about every industry imaginable. Because of data’s value, new advancements are being made every year to help organizations optimize and manage the data they collect and store to the fullest extent possible.  

Cloud computing and AI have driven recent advancements in big data integration, helping make data more accessible and higher-quality while remaining secure. And the future of data integration looks even brighter, with innovations currently on the horizon that will allow greater access and better stewardship than ever before. Let’s look at some of the most impactful data integration trends made recently and innovations you’ll hopefully see soon. 

Data Integration Trends

Here are eight recent trends and technologies that have contributed to improvements in data integration and will impact future best practices for data integration architectures. If these advancements aren’t on your radar yet, they may be soon. 

1. Real-Time Data Integration

Waiting for large volumes of data to be processed before you can gain insights from them is a thing of the past. With a move from batch processing to real-time integration that uses change data capture, you can extract the information you need from your most crucial data quicker than ever — even in real-time. You can analyze massive data repositories by employing parallel processing without straining your system resources, giving you insights at the speed of business. 

2. Cloud Computing

Cloud computing has been around for decades, but recent advancements are making it easier for non-coders to set up data storage in the cloud instead of data lakes and data warehouses on physical servers. Cloud computing makes unifying disparate data sources and accessing data much easier, faster, and less costly, and now, no-code and low-code solutions are making it easier to create cloud-native architectures. With edge computing now being integrated into cloud computing tools, data integration can move even faster. 

3. The Convergence of ELT and ETL

Because many businesses are moving their data repositories from data lakes to cloud servers, deciding whether to use extract, load, and transform (ELT) or extract, transform, and load (ETL) processes for data integration is becoming less common. A new data integration process with continuous transformations, sometimes called extract, transform, load, and transform (ETLT), is emerging to improve data quality by constantly refining the data for data mesh distribution.

4. No-Code and Low-Code Data Integration

Emerging no-code and low-code — also called “self-service” — cloud architecture tools are improving speed for the future of data integration, but they’re also making overall data integration processes much easier. Many data integration tools and data platforms are being streamlined so that professionals without coding experience can perform collection and analysis tasks that once required specialized expertise. Overcoming data integration challenges for those without coding knowledge means you can share data with more stakeholders who find it valuable. 

5. IoT Data Integration

The Internet of Things (IoT) is a system of internet-enabled electronic devices that can share data with each other. The type and complexity of data these devices can share — and feed into your repository — is advancing, but so is how that real-time data is integrated for business uses. The ever-increasing use of 5G and 5G RedCap makes transmitting data from things like health monitoring devices faster; edge computing advancements make processing the data faster and simplified, while more intuitive iPaaS solutions are making it easier to organize and manage data from multiple data-collecting apps. 

6. The Emergence of Data Mesh

First conceived in 2019, this data integration trend provides a business-minded alternative to data lakes that has become popular with organizations that collect large amounts of data, like Netflix and PayPal. Instead of storing data in a central location, it is distributed directly to the sources who need it, allowing faster access and dissemination. Remember that because data is moving straight from collection to analysis, you need to have a structured data conversion process in place so that those receiving the data can make sense of it. 

7. AI and Machine Learning

Possibly the most significant critical drivers in making the future of data integration faster, more accessible, and higher quality are advanced artificial intelligence and machine learning capabilities. Thanks to the recent evolution of AI, systems and processes that once required expert coders to build are now being constructed seamlessly into data integration platforms that allow more people to access trusted data. Machine learning is advanced enough to facilitate retail customer data integration from images, videos, and text, for example, but it also constantly refines and focuses data to make it easier to analyze buying habits. While their impact may not be noticeable now, AI and machine learning will begin streamlining and improving your data integration platforms and tools to increase efficiency, accessibility, and quality. 

8. Data Security and Governance

As more data integration processes move to cloud servers, rely less on coded systems and routines, and perform functions in real-time, there’s a huge need for increased cybersecurity and a more nuanced data governance framework. Many of these advancements aim to get more data to stakeholders who need it most but may not be as familiar with protecting it as data professionals are. To this end, Zero-Trust Architecture (ZTA) is becoming more popular, and data access permissions will become more stringent. This requires data governance professionals to focus more on stewardship and access control models that limit access or make it more sophisticated to prevent unauthorized access to data.  

Data Integration Trends: By the Numbers

Data is king, so don’t take our word for these trends shaping the future of data integration. Here are some statistics that provide insights into where the field is headed. 

• The global data integration market size is expected to reach $17.1 billion by 2025, with $4.87 billion of that belonging to the U.S. 
• Marketing, including in the retail industry, makes up the largest sector of data integration income at 26%, but HR data integration is growing.
• In 2022, 35.5% of organizations chose on-site servers over cloud-based solutions, with 32.3% of these citing cybersecurity concerns and 24.6% worried about proper data integration.
• AI and machine learning experienced an 85% increase for SaaS products in late 2023 compared to the year before.
• The average breach cost of data stored in public clouds is $5.17 million.
• 40% of business initiatives fail because of poorly integrated data sets.
• Data integration is the fastest-growing Data and AI market, showing 117% growth year-over-year (YoY).
• Using AI in data integration processes can improve data quality by up to 20%.

 Download the Data Integration Infographic

Looking Ahead

Since 2023, there has been no expense spared in developing generative AI technology, and the evolution of this technology shows no sign of stopping. Expect to see more AI, GenAI, and machine learning integrations in data processing tools, which should increase the quality of all kinds of data. These technologies should also make processes more streamlined, which can help overcome data integration challenges organizations have concerning cloud-based services. The lines between implementing ELT or ETL will continue to be blurred as more organizations adopt cloud computing, and data structuring and processing become more automated by AI and machine learning. 

Data is an ever-growing market, so expect more money to be spent on these data integration trends and other surprising innovations in the coming years.  

A Cutting-Edge Data Integration Platform

You can find many of these data integration trends already hard at work in hybrid integration platforms (HIPs) and solutions like DataConnect from Actian. You can automate data pipelines with low- or no-code, institute business rules and data quality standards that are automatically incorporated into your workflow, and enable real-time data connections.

About Actian Corporation

Actian makes data easy. We deliver cloud, hybrid, and on-premises data solutions that simplify how people connect, manage, and analyze data. We transform business by enabling customers to make confident, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.