The proliferation of remote and online work maximizes workforce potential for companies but spreads data thin from platform to platform. From customer relationship management software to cloud services, data for your business could be hosted in multiple locations, leading to disorganization, data set errors and poor decision-making.
Companies are increasingly recognizing the problems that come with disparate data platforms and are leaning into data integration solutions. In this article, we cover some of the top data integration trends we’re seeing today and where they could lead in the future.
- What is data integration?
- The most interesting data integration trends in 2022
- What’s next for data integration?
What is data integration?
Data integration is when a business collects data — regardless of type or format — in a single place. This allows companies to obtain analytics more efficiently and gain perspective for solving problems. Data warehouses are frequently used to store this master data set, continually fed from various other data sources called data silos.
Once all data silos are integrated with and feeding into the master data storage location, companies can more easily achieve data-driven progress without wasting time or money on repetitive data entry.
The most interesting data integration trends in 2022
The following integration trends are propelling companies into a more organized and stable future. Learn how these data integration strategies and trends are helping users gain higher profits while optimizing data touchpoints:
Getting started with an integration platform as a service solution
Taking advantage of all that the cloud offers could be a trend in and of itself. However, this trend focuses specifically on integration platform as a service, or iPaaS. iPaaS uses cloud services to regulate cloud information and applications between individuals and companies who need access. This includes communication with any onsite resources.
SEE: Boomi vs MuleSoft: iPaaS comparison (TechRepublic)
iPaaS standardizes resources across organizations of all types and sectors, creating consistency among its users for correctly formatted and cohesive data integration. It also reduces overhead for separate teams to revise data streams and provides stable expectations across organizations, even if there are new applications or updates implemented.
Investing in the iPaaS adoption trend may be the best option to integrate data from vendors and providers, particularly for complex industries like healthcare. One benefit of iPaaS is it combines seamlessly with customer experience initiatives and platforms to offer avenues for maximizing data streams and providing current data.
Leveraging real-time integrations
Real-time data integration is non-negotiable for every industry with any data volume. It allows a company to adapt quickly and have the highest return on investment for projects and events. Social media teams can more accurately adjust feeds and executives can provide the most up-to-date metrics for investors.
These are some cross-industry examples of how real-time integrations benefit data-driven operations:
- An airline needs to incorporate real-time data into its operations in order to not oversell a flight.
- A shipping services company needs to provide updates on tracking numbers to keep its customers satisfied.
- A company is hosting a virtual event and wants to know how its pre-event advertising campaigns are performing and how registration numbers are changing over time.
In all of these cases, real-time data integration enables real-time data knowledge and action. Real-time data supports companies in communicating more accurately to businesses or consumers based on market needs.
Enabling machine learning and AI
Automation is the future for many businesses and industries. As more mundane tasks are automated by machine learning and AI, humans have increasingly more time to devote to more valuable business objectives. Machine learning is a particularly useful strategy to incorporate into data integration efforts, especially as continued data input makes machine learning models more accurate and useful.
Integrating AI into your data integration flow can help to detect data patterns, which enables data integration operations to do the following:
- Assist in fraud detection or information manipulation
- Reinforce cybersecurity initiatives
- Adjust pricing based on market fluctuations
- Identify disruptions in supply chains
SEE: The Complete Big Data and Machine Learning Bundle (TechRepublic Academy)
AI integration can also simplify data integration by providing more streamlined processing. With its ability to comb through big data at faster speeds, it’s no wonder AI is one of the top significant data integration trends in 2022. AI is autonomous, making it possible for AI models to make simple data integration decisions on their own.
Eliminating data silos
One of the biggest hurdles in consolidating data integration processes today is identifying every data silo that currently exists, especially since data silos can be isolated to specific departments or organizations. These data silos prevent executives from having a detailed picture of analytics across the company and also tend to encourage an exclusionary company culture.
Data integration strategies will alleviate many of these problems by proxy. However, there are other ways to ensure data silos are no longer a problem. The first and most obvious is to prevent new silos from being created. Many companies are catching onto this simple solution and are now implementing new data use and storage policies across their organizations to stop new data silos from being created.
Establishing a metadata management strategy
For a long time, IT departments have resorted to purchasing and utilizing countless software platforms to achieve data goals; then, they have had to find a way to make this tech stack work together. Companies are increasingly recognizing that this process should be reversed.
SEE: Metadata: The challenges of unstructured data management (TechRepublic)
Instead of wasting time trying to fashion a makeshift suite of products that don’t intuitively work together, data teams are beginning to use metadata management strategies that provide a unifying foundation. Metadata management provides more precise insights to supplement any data collection initiative and can also automate compliance to free up IT resources.
This trend is a must, especially for organizations that cannot effectively describe their data sets today. If you’re a library, it’s essential to know your data set includes the book catalog — but does the data have publication years or genres? That’s critical metadata to understand the collection as a whole.
What’s next for data integration?
Few sectors are advancing at the same pace as data management. With the exponential growth of data accessibility and cross-departmental data initiatives, establishing a seamless data interaction strategy is critical for business development. Organizations will need to keep up with these data integration trends and invest in top data integration tools if they want to ensure profit margins and productivity stay at their peaks.
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