Data Integration Mic Drop: 5 Trends to Watch
Data, data, and more data: It’s the common currency of virtually any business or industry today. But the value of that data depends greatly on data integration tools. Without it, most organizations struggle to organize, analyze, store, and secure their data.
No matter how good you are at data collection, data has little value unless you can make sense of it. Indeed, many of the benefits of data integration coalesce around that fundamental goal: Increasing the value of all the information at your disposal.
That also explains why data integration has become a multi-billion-dollar industry unto itself. Organizations are managing ever-growing piles of data, as well as increasingly diverse sources of that data. Bringing it all together in a manner that makes it understandable and usable, among other needs, is crucial.
"Data integration is the cornerstone of modern businesses, enabling seamless collaboration and decision-making across disparate systems and sources,” says Rohit Maheshwari, co-founder of NMG Technologies.
Like data itself, data integration is always adapting and evolving to new capabilities and requirements, too.
“The landscape has transformed remarkably from the early days of basic ETL processes to today's sophisticated real-time integration solutions,” Maheshwari says.
With that in mind, we asked Maheshwari and other data experts to weigh in on the most important data integration trends unfolding in 2024 and beyond. Here’s what they had to say.
Trend #1: Organizations are consolidating their data operations and management
Increasingly, where you find data integration, you’ll also find data consolidation: Specifically, the ability to bring multiple data management practices onto a single platform, even if the data itself comes from a wider and wider array of sources.
“We’re seeing a push to consolidate,” says Erik Duffield, co-founder and CEO of Hakkoda, a data consultancy specializing in the Snowflake cloud. Duffield’s firm recently surveyed 500 data leaders and decision-makers in large, U.S.-based companies; nearly three out of every four respondents (74%) reported plans to implement a centralized cloud for their data operations in 2024.
Lucas Wyland, founder of the video game analytics firm Steambase, notes that merging disparate data operations into a single place comes with some initial complexity and usually requires careful planning, migration, and system compatibility, among other needs. But the benefits are often worth it.
“Data redundancy is a significant challenge for organizations,” Wyland says. “Therefore, they streamline data operations by consolidating data pipelines, ETL processes, and data warehousing. It simplifies management and reduces redundancy.”
Duffield from Hakkoda shares an example from a recent client, a regional bank whose data was essentially split into three different silos. That caused fragmentation in the bank’s overall data management strategy.
“We worked closely with the client to consolidate its siloed data sources into a single source of truth, which allowed them not only to piece together a more holistic view of its data, but also lay a strong, flexible, and scalable foundation for future innovation, including AI integration," Duffield says.
(Hold that thought on AI integration – we’ll get back to it in a moment.)
Trend #2: Hybrid cloud and multi-cloud architectures underscore the need for data integration
Even as more organizations consolidate their data management and operations, the same cannot be said for their broader portfolio of infrastructure and applications. In many organizations, infrastructure, and applications are increasingly distributed across multiple environments – underlining the need for robust data integration tools and practices.
This is likely also feeding the consolidation trend. Maheshwari notes that integrating data spread across diverse cloud environments can create its own challenges, catalyzing a need for “[data integration] solutions offering flexibility, scalability, and interoperability.”
Distributed infrastructure and applications also highlight data integration's growing role in data governance and security, according to Maheshwari, who adds that tech leaders prioritize data privacy, regulatory compliance, and risk management.
Trend #3: AI will (eventually) become pervasive in data stacks
Yes, AI is already “everywhere” (not really). But in terms of data technology stacks, we’re just scratching the surface.
Hakoda’s survey of data leaders found that the overwhelming majority (85%) plan to get generative AI tools running in production in 2024, for example, and Duffield says that roughly half of those firms are already using AI in various forms of automation.
“But we’re seeing sophisticated use cases like AI copilots, ETL/ELT, and schema matching and integration much less often – at least for now,” Duffield says.
Emphasis on for now: “IT organizations are in an amazing position to leverage AI on internal processes to change the way IT gets designed, built, and supported,” Duffield adds.
Maheshwari likewise anticipates the growing adoption of AI and machine learning algorithms in data integration processes and the automation of data management tasks like data cleansing, transformation, and reconciliation.
Maheshwari’s company worked with a large e-commerce company to deploy AI capabilities in their data integration processes, aiming to improve consumer shopping experiences.
“By integrating real-time customer interaction data with product inventory and sales history, we enabled our client to deliver personalized product recommendations, significantly increasing conversion rates,” Maheshwari says.
Trend #4: Metadata-driven automation will also speed up and enhance data management and integration
Not all automation needs AI—not by a long shot. Data management and integration processes offer opportunities to reduce time-consuming (and error-prone) manual effort. For organizations that aren’t quite ready to dive head-first into AI, Duffield points to metadata-driven automation as another important trend.
“We’re seeing plenty of opportunity for metadata-driven automation when ingesting, integrating, and delivering high-value analytics solutions,” Duffield says.
Robust metadata can catalyze early-stage data integration processes and have cascading advantages downstream in terms of trust, accuracy, lineage, and usability.
“Cloud platforms that contain significant technical metadata allow for rapid deployment and scale of data pipelines, as well as providing insight into data lineage automatically – giving data consumers trust that the data is accurate and coming from the correct source,” Duffield says.
Wyland from Steambase points out that this capability – robust metadata and governance – will be vital as more companies adopt LLMs and other forms of AI.
“Understanding the data flow is critical,” Wyland says.
He anticipates an overlapping trend here: the growth of “red-amber-green” (RAG) models that visually represent data lineage and quality.
“They help professionals understand data flow across systems and identify potential bottlenecks or issues,” Wyland says.
Trend #5: Verticalization will come to data integration
Given the dynamic nature of data integration and data itself, Wyland also sees significant untapped opportunities for solutions tailored to industry-specific requirements and use cases.
While there might be some data management and integration fundamentals that apply to virtually any sector, one-size-fits-all approaches may not capture the nuances – or full-blown differences – of data integration in, say, a manufacturing setting and a healthcare setting. As a result, Wyland
“Verticalization involves tailoring [or] customizing data infrastructure solutions to specific industries or use cases,” Wyland says. “It enables organizations to achieve better alignment with their business needs.”
Doing so effectively will require domain expertise and the flexibility to adapt to unique requirements – which means considerable opportunities for data professionals and data integration providers that can deliver.
The Bottom Line
Data integration is hot – and for good reason. It’s a critical means of making sense of your data and generating tangible value. Otherwise, you’re just collecting data for the sake of it.
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