Data is undeniably one of the most important assets of any successful organization as industry leaders in the market consider data to be the major factor while making important business decisions and defining strategy. However, the data may become impracticable if not found at the right time or place. This is where data integration comes to the rescue. Experts define data integration as the process of combining data from various sources and presenting them in a unified way. Without data integration, there is no easy way to access the data gathered in disparate systems or to combine data sets so they can be processed, analyzed, and acted on. In fact, the greatest value is derived from combining data sets to extract value and insights. The benefits of integration have never been as high as they are now.
Before data’s full potential can be realized, it must be readily available to those who need it across all areas of the business; data in silos is of limited value to the broader organization. A company that integrates data gains access to information that would otherwise be unavailable. This can aid departmental communication, improve customer service, streamline processes, improve decision-making, and boost overall productivity. One quite contemporary example is data from the Internet of Things (IoT). IoT applications gather data from several devices, so data integration is used to gain insight from the mass of detail.
Data integration done well can decrease IT costs, free up resources, increase data quality, and stimulate creativity without requiring broad modifications to existing systems or data structures. It bears mentioning that a narrow focus on a tactical approach to data integration might easily miss the larger picture of digital transformation, and thus DI cannot be viewed in isolation.
A Few Problems That Data Integration Solves
While data integration can solve an array of problems, these are some of the most common issues that many technologies and IT departments are struggling with today.
Big Data
Handling big data may appear difficult owing to its large volume, but the variety of data is often a more significant challenge. Whether data is generated internally or acquired externally, data integration can help make sense of all the data housed and generated within your organization.
Data Silos
Heterogeneous data sources that store data in specific locations are referred to as data silos. They are often proprietary, poorly supported, and by their very nature, disconnected from the rest of the organization. Years ago, it made sense for departments to choose data storage software and methods based only on their requirements. But now cross-functionality is the overriding consideration. Consolidating data allows you to transfer proprietary, legacy data into new systems that are accessible to all team members.
Semantic Integration
Within a given organization a variety of data types—that convey exactly the same meaning— can be arranged differently and therefore are incompatible with each other. The way dates are kept (“DD/MM/YYYYY”, “MM/DD/YYYYY”, “Month Day, Year”, etc.) is a common example of this. You will be able to find data, analyze patterns, and make sense of it much more efficiently if you remove variations and create an organized, clean data warehouse.
Accessibility
“Create once, deliver to many,” by establishing a common data source. All data consumers inside your firm will have access to the same information, reducing the number of queries asked, speeding up data access, and lowering the risk of erroneous replicated data. End users can obtain what they need from a central location, while authors can continue to use their preferred solutions.
Five Essential Types of Data Integration
Data Consolidation – Data consolidation physically combines data from several systems, consolidating the data in single data storage. Data consolidation is frequently used to minimize the data storage site.
Data Propagation – Replication of data from one location to another is known as data propagation. It is event-driven and could be either carried out synchronously or asynchronously.
Data Virtualization – Virtualization employs an interface to give a near-real-time, unified view of data. Data virtualization obtains and analyzes information without requiring standard formatting or a single point of access.
Data Federation – Technically, a federation is a type of data virtualization. It establishes a standard data model for heterogeneous data from many systems using a virtual database.
Data Warehousing – The phrase “data warehousing” is used, it refers to the cleaning, reformatting, and storing of data, which is essentially the same thing as data integration.
Data Integration Methods
Data integration can be accomplished in several ways of varying complexity. The following is an introduction to a few of these.
Physical Data Integration: The physical data integration strategy is the classic method of data integration that entails physically moving data from its source system to a staging area, where it is cleansed, mapped, and transformed before it is physically relocated to a target system (data warehouse/data mart).
Data Virtualization: With this method, a virtualization layer is used to link to physical data stores. Data virtualization, unlike physical data integration, entails the production of virtualized views of the underlying physical environment without the requirement for physical data transfer.
Data Warehouse: Another solution is based on data warehousing. The warehouse system extracts, transforms, and loads data from heterogeneous sources into a single common quarriable schema so data becomes compatible with each other. Software applications also known as the middleware drive the data from the collective sources into the main data warehouse, where they are curated and stored for future use.
Extract Transform and Load (ETL): ETL is a typical data integration technique in which data is physically taken from many source systems, changed into a different format, and loaded into a centralized data storage.
Database Replication: Database replication duplicates data from source databases like MongoDB or MySQL, say, into a cloud-based data warehouse to be used for other purposes and in combination with other sources.
Insight into the business world (BI)
It is crucial to organize, clean, and prepare data for analysis before employing these technologies. The gathered information is also used to create appealing reports.
Improved decision-making
When data is left unstructured, segregated, or difficult to access it becomes far less useful for decision-making.
Master Data Management (MDM)
By definition, master data management sounds a lot like data integration; however, data integration happens before master data management.
Relationship between the customer and the company
You will inevitably be able to deliver better customer service by integrating and organizing client information in an organized manner.
Virtualization of data
Data virtualization enables users to view, manipulate, and query data without having physical access to the data. To easily virtualize data, you will require a well-constructed back-end structure.
The Best Data Integration Tools, Platforms, and Vendors
Boomi
Boomi is a cloud-based integration tool. It enables organizations to easily integrate apps, partners, and consumers over the web, thanks to a visual designer and a choice of pre-configured components. Boomi is capable of a wide range of interesting activities for businesses of all sizes. It includes everything you will need to create and maintain integrations between two or more endpoints.
Key Features of the Boomi platform
- This technology, which is utilized by both SMBs and major corporations, provides several application integrations as a service.
- From private cloud, on-premises, and public cloud endpoint connections to ETL support, Boomi offers a variety of connectivity and data management options.
- Through a consolidated reporting platform, organizations can manage Data Integration in a central location.
The best-suited use case for Boomi
Boomi is an excellent option for managing and moving data in hybrid IT infrastructures.
MuleSoft Anypoint platform
MuleSoft Anypoint Platform is an iPaaS data integration solution that allows businesses to link two cloud-based apps, as well as a cloud or on-premises system, for smooth data synchronization. The platform—whether locally or in the cloud—saves the data stream from data sources. It is capable of accessing and transforming data using the MuleSoft expression language.
Key features of the MuleSoft Anypoint platform
- It has mobile capabilities that enable users to manage their workflow and track tasks from backend systems, legacy systems, and SaaS apps.
- MuleSoft is compatible with a wide range of corporate solutions and IoT devices, including sensors, medical devices
- It enables users to conduct complicated integrations using pre-built templates and out-of-the-box connections, which speeds up the data transfer process.
The best-suited use case for the MuleSoft Anypoint platform
MuleSoft is best suited for businesses that need to connect to a variety of data sources in both public and private clouds, as well as to retrieve obsolete system data.
Informatica
Informatica specializes in data integration and software development. It offers services such as ETL, data masking, data quality, data replication, data virtualization, master data management, and more. It can connect to many heterogeneous sources, retrieve data from them, and execute data processing as well.
Key features of Informatica PowerCenter
- PowerCenter is a complete platform for data integration, migration, and validation.
- It is a tool that is widely used by small and large organizations alike.
- It is part of a more extensive suite of solutions for big data integration, cloud application integration, master data management, data cleansing, and other data management tasks.
The best-suited use case for Informatica PowerCenter
If you have a lot of old data sources that are mostly on premise, Informatica PowerCenter is an excellent solution.
Jitterbit
Jitterbit is a harmonious integration tool that allows businesses to link apps and services via API connections. It works with cloud, on-premises, and SaaS applications. It includes AI capabilities like speech recognition, real-time language translation, and a recommendation system in addition to data integration tools. Jitterbit is also called the swiss army knife of data integration platforms.
Key features of the Jitterbit platform
- With its pre-built Data Integration tools templates, Jitterbit offers a sophisticated Workflow Designer, allowing users to construct new integrations between two programs.
- It has an Automapper to assist your map-related data and over 300 formulae to simplify the transformation.
- Jitterbit provides a virtual environment in which integrations may be tested without interrupting live systems.
The best-suited use case for the Jitterbit platform
Jitterbit is an EiPaaS (Enterprise Integration Platform as a Service) that can be used to solve complex integrations swiftly.
PreludeSys – Experts in Data Management and Integration
PreludeSys provides data integration services to large and small organizations with an enviable track record of success. Our partnership with the top four data integration vendors allows us to give you an unbiased consultation without compromising on quality or your expectations. Request a strategic consultation from our data integration experts today.
