What Is Data Integration (And Why You Need It)?
As much as 67% of businesses relied on enterprise data integration in 2020, with nearly a quarter of businesses planning to start.
Data integration is an important step towards ensuring efficiency within your business. But not everyone knows quite exactly how to go about implementing data integration into their internal structures.
Today, you’ll have the opportunity to learn the basic principles of data integration and how they play into your business needs. Keep reading to find out more!
What Is Data Integration?
Simply put, data integration is the process of consolidating disparate sources of data into a unified entity. This collection of data is then used to gather and disseminate business intelligence (BI).
Whether you want to use marketing automation tools or track the performance metrics of your business, data is at the forefront.
Making that data useful means integrating different data sources so you get front-row tickets to the knowledge it can offer.
Data integration happens in numerous ways, from data warehousing to data virtualization.
Extract, transform, and, load (ETL), in particular, is an infamous strategy for copying data from heterogeneous sources to a destination source.
Computer scientists began building systems for data integration in the early 1980s, with the intention to suppress the differences of relational databases using business rules.
Though initial data integration was much more dependent on physical infrastructures and tangible repositories, today cloud technology allows for more digital integration efforts.
Either way, the overriding objective of data integration is to centralize data collection, storing data in such a way that it is accessible to those that need it most.
Types of Data Integration
It’s also worth noting that data integration comes in many forms. Below are the most common types of data integration.
☐ Manual Data Integration
In manual data integration, there is no unified view of the data. Users simply operate by collecting data from several sources and utilizing the relevant information accordingly.
☐ Data Integration with Middleware
Middleware data integration takes place between the application layer and the hardware infrastructure.
This involves transferring integration logic from the application to the middleware component.
☐ Uniform Data Access Integration (UDAI)
With uniform data access, data remains in the source systems and the end goal is to generate and sustain a simplified view of the holistic data. Such a view is a translation of sorts because the original data does not move.
High storage requirements are a non-issue given this method but it can still be burdensome to have multiple data access points seeing that there is no permanent transference of data.
☐ Common Storage Data Integration
Common data storage is a physical data integration configuration where a copy of the source system data is stored and managed independently within a new system.
☐ Application-Based Data Integration Tools
Application-based data integration refers to software that effectively locates, fetches, formats, and integrates data.
Pre-built connectors and automation are frequently used in this type of data integration.
Data Integration Techniques
Besides the various types of data integration, there are more specific approaches to data integration that might help you better conceptualize the process.
☐ Extract, Transform, Load (ETL)
ETL is a tried-and-true data integration strategy that calls for making copies of data from heterogeneous sources, harmonizing them, and loading them into a data warehouse or database.
☐ Extract, Load, Transform (ELT)
ELT is similar to ETL, naturally, but the steps transpire in a different order. Extraction is always the first step. But in this case, data is loaded before being transformed for analytical use.
☐ Change Data Capture
This technique tracks changes from one database to another, applying those changes to the relevant data warehouses and repositories to keep everything in sync.
☐ Data Replication
Data replication is any process where the data in one database is replicated to other databases for synchronization.
☐ Data Virtualization
For data virtualization, data from different systems is collected in a virtual storage center for unified viewing.
☐ Streaming Data Integration
Streaming data integration is a real-time data integration method where continuous integration is achieved through the constant feedback of streaming data.
How Does Data Integration Work?
Data integration works by connecting source systems to target systems. Yet how these connections take place can largely differ.
Sometimes, this occurs in real-time via ETL and data warehouses. Other times, the data that moves from source to target is actually a carbon copy of the real thing, a procedure known as data replication.
Where data virtualization is concerned, APIs are a crucial piece of the puzzle. An API is a back-end structure that enables the interactivity of distinct software.
With this context, it should be fairly easy to understand why API integrations have a pretty big role in data integration as a whole.
To close the distance between what is a highly technical undertaking and business users, software developers and data integration architects create software such as low-code development platforms to ease the heavy lifting.
These platforms use drag-and-drop features and visual components to empower non-expert citizen developers to build their own business processes and workflows.
In general, there are many data integration tools available to ensure the ease of accessing and unifying critical data.
Data Integration Tools
Data integration tools are a type of software capable of syncing software systems together from a single platform.
The demand for data integration tools is high. This is because businesses need an easy way to improve their workflows for business operations.
Information platform as a service (iPaaS) is a product category that emerged out of this need.
And major tech vendors have taken advantage of this gap in the industry to extend data integration tools to businesses that can make use of them.
Traditional iPaaS software stresses cloud compatibility and having a large assortment of pre-built connectors for diverse integrations.
Dell Boomi, Talend, and Tray.io name some of the most popular data integration tools on the market.
Conventional data integration tools prioritize ease of use over custom solutions; this has been a common challenge those new to the industry are trying to overcome.
Why Is Data Integration Important?
Businesses must constantly re-assess and refine the foundation of their organization to stay afloat. Competition is heated, and relying on inefficient operational activities can ultimately lead to the downfall of a company.
Every scaling business must be prepared to work with large and complex data sets. And failing to organize this data in a cohesive manner can prove to be detrimental.
Business applications depend on data every day across all departments. Order invoices, product inventory, and supply chain logistics are some examples where you might recognize data’s integral nature.
Advanced analytics and refined management processes are only a few of the benefits that successful data integration can elicit. But it should be clear why data integration is paramount.
Big Data in Data Integration
Big data may sound like a cute expression. In reality, big data is a concentrated subfield of data science that consists of analyzing and transforming data that is too large and complex for traditional data processing models.
To follow, big data integration is a reference to the complicated processes of advanced data integration that data engineers use to manage enormous volumes and varieties of data.
Big data analytics requires high-performing, scalable tools that emphasize the importance of business insights and data quality.
Benefits of Data Integration
Besides simply unifying systems, there are a number of other benefits to gain from data integration. See below to learn more.
Data integration allows for more team collaboration. In business, a cross-silo organization characterizes a team that works horizontally where hierarchal barriers do not prevent information sharing.
Data integration means you have everything in one place. As a result, data preparation and analysis can happen at a rapid pace.
Using data integration tools and low-code development, you can likewise save time and boost efficiency.
You can reduce and rework errors as well with the aid of data integration. Manual data collection is a painstaking and time-consuming process.
What’s more, it’s quite a task to ensure that the data you gather will be complete and accurate. An incomplete data set can cause adverse effects to your BI, disrupting the internal organization of your company.
In contrast, automated data integration guarantees the delivery of valuable data. A centralized system mitigates quality issues and can make improvements where necessary.
Big Data Leveraging
Modern businesses need data integration to leverage big data. You can employ data integration to create data warehouses and data lakes in order to manage highly complex and voluminous data.
Naturally, this will lead to better navigation of your BI. You can compile more information with data integration, and more accurate data at that.
Data Integration vs. Application Integration
Data integration and application integration are more alike than different. The primary purpose of data integration is to retrieve data from relational databases.
Comparatively, application integration aims to combine independent applications. This requires data consistency to manage the flow of multiple tasks.
A single user interface, similar to what’s expected in data in integration, is also a standard component of application integration.
Additionally, to achieve application integration, cloud data integration must ensue. In essence, data integration is a specified strategy towards application integration.
As an illustration, application integration might look like combining HubSpot and WordPress, so you can have a customer relationship management (CRM) tool and content management system (CMS) in one platform.
However, this application integration requires the process of data integration. Thus, application integration is a manifestation of data integration itself.
Looking for a Data Integration Solution?
The biggest obstacle integration projects face stems from cookie-cutter approaches to data integration. Often, source systems are not compatible with your unique business system.
Enterprise data warehouses and mainstream cloud environments do not always take into account the nuances and custom integration requirements of every business unit.
In short, finding a fully comprehensive data integration platform can be difficult, if not impossible.
In turn, Trio’s job is to provide you with the custom software you need to support your pragmatic business decisions.
Hire Trio data engineers today to see your business reach new horizons!
Frequently Asked Questions
If you’re looking for some information, but can’t find it here, please contact us.Go to FAQ
Data integration is the process of consolidating disparate sources of data into a unified entity.
Big data is a concentrated subfield of data science that consists of analyzing and transforming data that is too large and complex for traditional data processing models.
Data integration tools are a type of software capable of syncing software systems together from a single platform.