Good decision-making is the backbone of any successful business. Useful and accurate data drives the best decisions. That helps explain why enterprise companies have begun investing heavily in data integration efforts.
For years we’ve all heard about the importance of big data. Now, companies are finding ways to leverage it across their operations through smart integration practices.
But data integration can be a tricky subject to tackle. It’s a broad topic. As a result, people have questions about what data integration is, why it matters, and the technology that enables it.
We’re here to help.
We used Answer the Public to identify trending and popular search queries around the topic. We also pulled together common questions from Boomi customers. We’ve answered the most frequent relevant questions in a data integration FAQ.
Data integration is the act of moving data throughout a digital ecosystem to create a fabric of pervasive connectivity. That can include aggregating multiple sources of data in one unified resource. There, business users can access data to identify trends, patterns, and insights useful for analytical decision-making.
It’s essential to recognize that data integration differs from data migration, data replication, data ingestion, and data extraction. Think of data integration as an umbrella term that can encompass all of those.
Yes! Data integration can take many forms. Some of the most common include:
One example is aggregating disparate data into a central database (also called a data warehouse). The data is then cleansed and manipulated to ensure it’s ready for business users to analyze it for insights. That can include removing duplicate entries and extraneous variables, standardizing data, labeling it, and much more. It’s essential to structure the data in a manner that makes sense for the business.
Once the data is fully integrated, companies can use powerful filters, algorithms, AI, and machine learning to examine relevant insights within the context of the total available data. That way, businesses can make data-backed decisions from a macro standpoint.
Data integration paints a clear picture of total business operations to promote smarter decision-making. Previously siloed data is brought together to provide a 360-degree view of the business, providing insights into operational efficiency, sales and marketing efforts, and business growth.
For example, a retail store might use a rewards number to track customer purchases in-store and online. Through data integration between these two points of purchase, the store can get a clear picture of customer habits – what they buy, how often, where, and so on. They can use this data to influence better marketing campaigns on a per-customer or customer group basis.
Data integration tools are what make it possible to bring different data streams together. It’s a broad class of technologies that include:
These tools form an ecosystem that makes it possible to integrate data in whatever way is most compatible with an organization’s use of that data.
Salesforce is one of the most popular CRM platforms on the planet, and it holds an immense amount of data. Many companies rely on that data for decision-making, which means they need to connect Salesforce to their broader digital ecosystem. This can be tricky because Salesforce has precise rules for integration.
To learn more about permissions, data integration, app integration, and factors involved in establishing data sharing, we recommend reviewing the Salesforce data integration FAQ. But the fact that these rules exist make it even more critical to ensure your integrations are built and deployed right out of the gate.
Companies employ different techniques to view, access, and organize integrated data. Here are some of the most common interfaces and integration methods and how they function:
Data integration requirements represent the means for bringing siloed data together. They will vary from organization to organization. The more data sources you have and the broader the data capture mediums, the more integration requirements. The method of data integration you choose matters, too.
As an example, integrating four sources of data via a streaming integration may only require low-level resources. Conversely, a company integrating 100 sources of data via ETL might employ a full suite of tools to ensure all that data is handled accordingly. Data integration and the requirements that facilitate it happen at scale.
In the same way that software as a service (SaaS) and framework as a service (FaaS) have become booming industries in the digital age, integration platform as a service (iPaaS) is also on the rise. The model is simple: companies pay a third-party for the arduous task of building and maintaining a data integration ecosystem. That usually occurs via cloud systems managed by a vendor.
There’s tremendous benefit in paying for iPaaS because it alleviates the burden of developing and managing complex data integration systems. It can also ensure that companies don’t have to build out larger data integration ecosystems of their own. Plus, iPaaS goes beyond strict data integration, also providing the ability to connect applications, devices, technology, and people.
One of the biggest sources of confusion for anyone learning about data integration is the concept of data transformation. Where data integration involves bringing raw data sources together, data transformation consists of modifying them. The confusion arises because transformation is often associated as part of integration, in ETL and ELT for example.
The simplest way to remember the difference is that integration involves connecting and moving data, whereas transformation is focused on the manipulation of data. Both, however, are used to power digital transformation and better business outcomes.
Still looking for answers? We encourage you to explore the Boomi Resource Center or contact us to set up a call. We’re here to help simplify the data integration process. Let Boomi be your resource!