Combining data from various sources into a single, coherent picture is known as data integration. The ingestion procedure starts the integration process, including cleaning, ETL mapping, and transformation. Analytics tools can’t function without data integration since it allows them to generate valuable business intelligence.
There is no one-size-fits-all solution when it comes to data integration. On the other hand, data integration technologies generally include a few standard features, such as a network of data sources, a master server, and clients accessing data from the master server.
What is data integration?
Data integration, in general, is the process of bringing data from diverse sources together to provide a consistent overview to consumers. Data integration makes data more readily available and easier to consume and analyze by systems and users. Without changing current applications or data structures, data integration may save money, free up resources, improve data quality, and foster innovation. And while IT organizations have always had to integrate, the potential benefit has never been as significant as it is now.
Mature data integration capabilities benefit competitors who do not have them. The following are some of the advantages enjoyed by businesses with significant data integration skills:
- Reducing the time it takes to inter-convert and integrates data sets will improve operational efficiency.
- Analytics is a powerful tool for improving data quality. Through automated data transformations that apply business rules to data, you can improve the accuracy of your data and enhance your decision-making capabilities.
- Using a whole picture of data that businesses can more readily analyze, you may obtain more critical insights.
A digital firm is based on data and algorithms that analyze it to extract the most value from its information assets—from across the business ecosystem, at any moment. Data and associated services flow freely, securely, and unobstructed across the IT landscape in a digital firm. Data integration provides a complete overview of all the data moving through an organization, ensuring that it is ready for examination.
Data integration types
There are a variety of data integration techniques:
Data warehousing
Data warehousing is a data integration approach that uses a data warehouse to cleanse, format, and store data. Data warehousing is one of many integration systems that allows analysts to view statistics from several heterogeneous sources to provide insights into an organization.
Middleware data integratıon
Middleware data integration aims to use middleware software as a gateway, moving data between source systems and the central data repository. Before sending information to the repository, the middleware may help format and check it for errors.
Data consolidation
Data integration is bringing data from many systems into a single data source. Data consolidation is often aided by ETL software.
Application-based integration
An Application-based integration is one in which data is extracted and integrated using the software. The application validates the data during integration to ensure that it is compatible with other source systems and with the target system.
Data virtualization
Users may get a near real-time, consolidated view of data via a single interface even though the data is kept in separate source systems when using a virtualization approach.
Comparison: Data integration vs application integration vs ETL
The terms data integration, application integration, and ETL/ELT are often used interchangeably. While they are linked, there are several differences between the three phrases.
Data integration merges data from many sources into a centralized location, which is frequently a data warehouse. The ultimate destination must be adaptable to handle various data types at potentially huge quantities. Data integration is ideal for performing analytical activities.
The term “application integration” refers to moving information between applications for them to remain up to date. Each application has its method of emitting and receiving data delivered in significantly smaller quantities. Integration is perfect for operational use cases that need to be maintained. For example, making sure that a customer support system contains the same customer data as an accounting system is one way it can be done.
The term ETL is the acronym for extract, transform, and load. This refers to extracting data from a source system, changing it into a different form or structure, and loading it into a destination. There are two types of ETL: data integration and application integration.
Importance of data integration
With the ever-increasing volume of data, data integrity has become more vital. Data integrity is all about assuring that your data is recorded and kept as intended. And that when you look for information, you receive what you want and anticipate.
Businesses must be able to trust the data that goes into analytics tools to trust the outcomes. You get reliable results if you feed good data.
Maintaining a single location to view all of your data, such as a cloud data warehouse, can aid in data integrity. Data integration projects help to improve the quality and validity of your data over time. Data transformation methods can spot problems with data quality while it is being moved into the primary repository and correct it.
Data integration use cases
Many types of areas can benefit from data integration.
Multicloud data integration
Connecting the correct data to the appropriate people is a simple way to enhance security and speed innovation. Connect diverse data sources promptly so that businesses may combine them into beneficial data sets.
Customer data integration
To improve customer relationship management (CRM), you need data from distributed databases and networks.
Healthcare data integration
To make rapid data available for patient treatment, cohort treatment, and population health analytics, combine clinical, genomic, radiology, and image data.
Big data integration
Businesses use sophisticated data warehouses to deliver a unified picture of big data from various sources to make things easier.
How does data integration work?
One of the most challenging tasks organizations face is getting and understanding data about their environment. Every day, businesses collect more data from a broader range of sources. Employees, users, and clients need a mechanism for capturing value from the data. This entails organizations being able to assemble relevant data from wherever it is found to help with reporting and business processes.
However, essential data is frequently split across applications, databases, and other data sources hosted on-premises, in the cloud, on Internet of Things devices, or delivered via third parties. Traditional master and transactional data and new sorts of structured and unstructured data are no longer kept in a single database; instead, they’re maintained across multiple sources. An organization may have data in a flat file or request information from a web service.
The physical data integration approach is the conventional method of data integration. And it entails moving data from its source system to a staging area, where cleansing, mapping, and transformation take place before the information is transferred to a target system, such as a data warehouse or a data mart. The second choice is data virtualization, a software-based data integration type. This approach uses a virtualization layer to connect to real-world data stores. Unlike physical data integration, data virtualization does not require the movement of any actual data.
Extract, Transform, and Load (ETL) is a widely used data integration method. Information is physically taken from several source systems, transformed into a new form, and stored in a single data repository.
Data integration example
Let’s assume that a firm called See Food, Inc. (SFI) makes a mobile app in which users can photograph different items and determine whether or not they are hot dogs. SFI uses numerous tools to conduct its operations:
- To acquire new consumers, you’ll want to use Facebook Ads and Google Ads in tandem.
- Using Google Analytics to keep track of events on its website and mobile app.
- To store user data and image metadata (e.g., hot dog or not hot dog), we’ll use a MySQ l database.
- Send marketing emails and nurture leads via Marketo.
- Zendesk to handle customer service.
- Netsuite for accounting and financial management
Each of those applications contains a silo of information about SFI’s operations. That data must be combined in one location for SFI to acquire a 360-degree view of the business. Data integration is how it’s done.
How to choose data integration tools?
Compared to custom coding, an integration platform may save time to value integration logic by up to 75%. For organizations that wish to use an integration platform within their approach, the first step is to consider three essential factors:
Company size
SMBs have different needs than large businesses. According to industry experts, small and medium-sized businesses typically prefer cloud-based integration solutions for applications. Most recent application server architectures have moved away from on-premises servers and toward enterprise integration or hybrid integrations.
Source data and target systems
Do you have access to the data, or are you currently using any specialized software? What data do you currently possess, and how is it structured? Is it primarily structured or a mix of structured and unstructured information?
Consider which sources you want to incorporate. Integrating your transaction and purchasing data with your CRM data is a more straightforward endeavor. Alternatively, integrating your entire multi-channel marketing stack may be more difficult, as it might include connecting all of your customer touchpoints into a single view of them.
Required tasks
A strategy to achieve your goals is critical in any integration project.
Businesses can use integration projects for various activities, including data integration, application integrations, cloud computing, real-time operation, virtualization, cleaning, profiling, and so on. Some jobs are more specialized than others; understanding what you need and what you don’t will assist you in keeping your costs low.
Types of data integration tools
Here are the various types of data integration solutions:
On-premise data integration tools
These are the tools you’ll need to combine data from various local or on-premises sources. They’re coupled with unique native connectors for batch loading from diverse data sources housed in a private cloud or local network.
Cloud-based data integration tools
iPaaS, or integration platforms as a service, is the term given to services that aid in integrating data from diverse sources and then placing it into a cloud-based Data Warehouse.
Open-source data integration tools
These are the most pleasing alternatives if you’re attempting to avoid proprietary and possibly costly enterprise software development solutions. It’s also ideal if you want complete control of your data within your organization.
Proprietary data integration tools
The majority of these software systems are intended to be more expensive than open-source alternatives. They’re also frequently built to cater to particular business use cases.
3 best data integration tools
Now that you’ve learned about the criteria and types to consider when selecting data integration solutions. Let’s take a closer look at the top data integration tools.
Dataddo
Dataddo‘s goal is to make it easier for businesses of all sizes to get valuable insights from their data. Data integration, ETL, and data governance are just a few processes simplified by our solution. A no-code, cloud-based ETL platform that prioritizes flexibility – with a wide range of connections and fully customizable metrics, Dataddo makes building automated data pipelines simple.
The platform seamlessly links with your existing data stack, saving you money on needless software. With a user-friendly interface and straightforward setup, Dataddo allows you to focus on putting your data together rather than wasting time learning new activities. It’s 100% managed by API updates so that you can create and forget your pipelines. If Dataddo does not already have a connection available, it may be included in the platform within ten days of submitting an inquiry.
Key features:
- Easy, quick deployment.
- Flexible and scalable.
- Connectors that have been installed in less than ten days.
- Security: GDPR, SOC2, and ISO 27001 compliant
- Connects to existing data infrastructure
Informatica PowerCenter
The Informatica Powercenter software is a cloud-native integration service that incorporates artificial intelligence. Its simple user interface lets users take decisive transformative action, allowing them to pick between the ETL and ELT approaches. PowerCenter’s multi-cloud capabilities are focused on giving customers complete control over their data, with several pathways depending on client needs, such as Data Warehouse modernization, high-level data security, and advanced business data analytics.
Key features:
- A metadata-driven AI engine, CLAIRE, is at the heart of this content creation system.
- High-level data security for any business.
- Interoperable with a wide range of third-party platforms and apps and other software.
- Designed to assist businesses in gaining new insights from their data.
Pandoply
Through a combination of pre-built SQL schema and rapid compatibility with any and all business intelligence platforms, Pandoply fulfills its promise of “analysis-ready data” by providing a series of pre-built SQL schema. It gives complete control over how a source is built, allowing the user to participate in the table creation process when creating a data source. Built-in performance monitoring and simple scaling for growing enterprises are additional advantages.
Key features:
- Users and data queries are unrestricted.
- The number of data sources accessible is over 100.
- Artificial Intelligence-driven automation in a Smart Data Warehouse.
- Data schema modeling is easier.
