The availability of complete and high-quality data holds the power to positively impact any organization’s decision-making process. However, data remains siloed away in multiple locations for many businesses worldwide. Applying data integration concepts in your data ecosystem allows you to combine data from all sources, transform it into meaningful form and load it to a centralized data store.
But how can one implement it? What are the best practices one should follow? Which type of data integration is best for the business? What tools are available for data integration? What challenges do firms face while applying it?
No worries, we have got simple, yet detailed answers for all your data integration queries in this article.
Table of Contents
What is Data Integration?
Data integration is the process of combining data in various formats and structures from multiple sources into a single place like a database, data warehouse, or a destination of your choice. It is often used to support business processes, such as analytics, reporting, or data management. Its goal is to provide a comprehensive and accurate view of data from multiple sources, enabling users to analyze and gain insights that would not be possible with data from a single source.
Data Integration Architecture
Data Integration architecture is a defined structure for designing, organizing, and managing a fluid flow between IT systems across your firm to form a single unified view of your business.
Taking data in multiple formats and structures into consideration, a data integration architecture includes a framework to capture, aggregate, cleanse, normalize, synthesize, and store the data in a form useful for processing.
For instance, by establishing an effective architecture in your organization, you can replicate data from your marketing systems into a Customer Relationship Management System (CRM) or Enterprise Resource Planning (ERP) application.
A well-thought-of architecture promotes effortless and near-real-time access to a comprehensive view of every dimension of your business operations, customers, and markets. This also promotes collaboration across various teams in a firm by providing easy access to complete and accurate data for performing analytics. This improves the overall operational efficiency by eliminating any bottlenecks and manual data-cleaning efforts. Based on your business use case, replication frequency, data complexity, number of data transformations required, and volume, you can choose from a list of data integration types.
How Does Data Integration Work?
Before you start extracting and combining data, you need to determine your business requirements. This can include:
- Finding out what data and level of data quality are required.
- Exploring all the data sources to understand the data format, structure, and quality.
- Running a quality check for the data sources against the business requirements.
- Identifying the existing gap between the available data and its quality versus what the business has requested.
- Optimize business expectations or project costs and determine the best data integration approach for your use case.
- Identifying, finalizing, and modeling the necessary data infrastructure, i.e., staging areas, data warehouses, operational data stores, and data marts.
Now that you have defined the requirements, you can start working on an integration solution. This generally includes the client sending a request to the master server for data. The master server then intakes the needed data from internal and external sources. The data is extracted from the sources, cleaned, transformed, and then consolidated into a single unified view. Finally, this is sent back to the client for analytics and reporting. A common data integration approach used across enterprises worldwide is ETL(Extract, Transform & Load).
To get data in an analysis-ready form, ETL allows you to perform multiple transformations like data cleansing, quality, aggregation, and reconciliation.
How are Data Migration, ETL, and Application Integration Different from it?
Data Integration vs Data Migration
When applying or researching integration solutions for your firm, you will also come across the term called Data Migration. When comparing data integration vs data migration, data migration focuses on transferring data from one or more applications or databases to another. It also involves transferring data from a legacy system to a new one or moving data from on-premises systems to the cloud.
Data migration often aims to upgrade data management and ease access by moving data to a more modern or better-suited system. Whereas data integration is focused on improving decision-making and enabling data-driven insights by combining data from multiple sources that provide users with a unified view.
Data Integration vs ETL
Extract, Transform, and Load(ETL) is the most versatile technique used for extracting data from multiple sources, applying rules or transformations to make data consistent with the target data system, then loading it to a data warehouse or a destination of your choice.
Data Integration is a parent process that includes multiple activities such as data ingestion, data cleansing, data transformation, and data distribution. When comparing data integration vs ETL, ETL can be termed as a subset of data integration that focuses on the extraction, transformation, and loading of data.
Data Integration vs Application Integration
Application integration allows you to connect two or more apps for data transfer. This is generally done between various OLTP (online transaction processing) applications, one at a time, using application integration software. This allows you to sync data between applications used across different departments and boosts productivity.
For instance, you can integrate an instant messaging platform like Slack with Salesforce, thereby allowing users to exchange information seamlessly between the two without any hassle. When comparing data integration vs application integration, data integration mainly focuses on a one-directional flow from multiple data sources to a data warehouse or a destination of your choice.
Benefits of Data Integration
Integrating data from several sources into a single source of information can help you reap the following data integration benefits:
- Data-Driven Business Decisions: Integrating data offers you a comprehensive view of your business that allows you to identify trends, patterns, and issues that may not be so obvious when surfing through data from a single data source.
- Enhanced Customer Experience: With complete, fresh, and accurate customer behavior data in a single place, you can better understand the customer needs and personalize your products, services, and marketing efforts according to them.
- Cost Reduction: Manually replicating data is often time-consuming, expensive, and prone to errors. Applying an automated solution via custom scripts or a tool can significantly reduce operating costs.
- Higher Revenue Potential: Deeper insights into customer behavior allow firms to identify new opportunities for growth, such as introducing new products or services that align with customer needs and preferences or optimizing marketing campaigns to reach a wider audience.
- Enhanced Innovation: High data quality and easy access to data promote more sophisticated data analysis and improved decision-making.
- Improved Security: With a defined set of regulations and safety protocols in place, you can easily identify the potential security risks for your single source of truth and take appropriate action to mitigate them.
- Promotes Collaboration: Without having to query multiple data sources separately, it provides a single centralized data repository that can easily cater to all your needs.
Types of Data Integration Techniques and Strategies
Based on the disparity, complexity, and number of data sources in your firm, you can choose from various types of data integration techniques and strategies, such as:
- If you are just starting out with a few sources of data and low volume, then manual data replication might be the way for you.
- If you have multiple data sources and just need to combine data in a single place for further analysis, then data consolidation might cater to your needs.
- By providing a more business-user-friendly interface, you can opt for a data federation that uses a virtual database to get data from the desired sources.
- Data Integration techniques based on a middleware application allow you to transfer data from multiple applications and source systems into a central repository along with data validation and formatting.
Data Integration Challenges
Implementing or scaling data replication in your business will always have its own sets of challenges, namely:
- Diverse Data Sources: Data present in multiple sources have different formats, structures, and schemas. They generally need significant transformation and mapping in order to integrate data from all your sources.
- Data Quality: Data usefulness and reliability are often hampered by outdated, inaccurate, incomplete, and poorly formatted data.
- Data Security: Ensuring the security and privacy of data is a major concern when integrating data from multiple sources. It is important to have robust security measures in place to protect sensitive data.
- Ineffective Integration Solutions: Poorly designed or implemented integration solutions may have issues such as poor performance during fluctuating workloads, difficulty in mapping data from different sources, or a lack of support for different data formats or structures.
- Hybrid Cloud On-Premise Systems: It becomes a complex task to integrate the data stored in multiple locations, such as on-premise infrastructure and cloud systems and networks.
For a clearer understanding of all the data obstacles and how to tackle them effectively, you can read more about them in the Burning Data Integration Challenges article.
Data Integration Tools
If you have multiple data sources with near-real-time data needs, you can go for data replication tools to unburden your engineering team, remove any bottlenecks, and jump right to analysis with fresh data readily available. While selecting a data replication tool, you have to ask the right business questions, ie. Does it support my data sources? Can it scale on demand easily? Will my data remain secure & protected? Can it provide real-time data? Or Can it transform my data to an analysis-ready form?
After you have drafted your business requirements, you can select from the top data integration tools, such as Hevo Data. Check out the article on the top data integration tools to make the right choice for your business.
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Data Integration in Business Intelligence
Data integration is a critical component of business intelligence because it enables organizations to bring together data from multiple sources and make it available for analysis and reporting. It ensures that the data used for analysis and reporting is accurate and consistent, as it is sourced from multiple systems.
With data delivered in near-real-time without any compromise on the quality, you business analysts won’t need to clean and prepare the data manually. Once you have data that is consumable, you can build visually informative custom dashboards and reports. This provides the leadership with a more comprehensive and accurate view of their business, which can help them make better-informed decisions.
Data Integration Best Practices
While applying data integration at a large scale, you have to be careful and avoid common data replication mistakes. For that, you can follow a set of data integration best practices, namely:
- Define Clear Goals and Objectives: You have to define the scope of the project and determine which data sources and systems need to be included.
- Select the Optimal Data Integration Tool: Select the tool that can scale on demand, is economical, automate tasks involved in data replication, such as data cleansing and transformation, and can also help to ensure data quality and security.
- Choose Simplicity: Select an integration tool that is business user-friendly and requires minimal assistance from the IT team.
- Know Your Data: Explore your data for any potential issues or challenges, including the data structure, format, and quality.
- Assign Roles: Assigning specific roles and permissions to business users can streamline coordination and improve overall effectiveness.
Data Integration FAQs
Here’s a compact list of the most frequently asked data integration questions:
What is an example of data integration?
An example can be a firm that has customer data stored in multiple systems, such as a CRM system, an e-commerce platform, and accounting software. To get a complete view of a customer, the company might use data integration to bring all of the customer data together into a single, unified customer record. This could include information such as the customer’s name, contact information, purchase history, and financial data. By integrating this data, the company can get a complete understanding of its customers and make more informed business decisions.
What is the purpose of data integration?
By integrating data from multiple sources, organizations can uncover insights and patterns that might not be apparent when looking at data from single data sources.
Also, it can help organizations ensure that their data is accurate, complete, and consistent by reconciling differences between data sources and identifying and correcting errors.
What is data integration in ETL?
Data integration in ETL involves bringing together data from multiple sources, cleaning and transforming it, and loading it into a destination system for analysis and reporting.
Data integration involves a number of steps, including defining the scope and goals of the integration project, cleaning and transforming the data, and using no-code automated tools or other approaches to combine the data. Setting up an effective system for integrating data can be challenging due to issues such as data quality problems, data format and structure differences, and security concerns.
However, it is an important part of business intelligence, as it enables organizations to access and analyze the data they need to make informed decisions. By following best practices for integrating data, organizations can overcome these challenges and effectively use data to drive business success.
Now that you have a deeper insight into data replication, you can start working towards a solution that caters to your business needs. If you rarely need to transfer data from a couple of sources, then writing custom scripts or manually replicating the data is an effective choice. However, if your business teams need data from multiple sources every few hours in an analysis-ready form, you might need to burden your engineering team with custom data connections.
This is a time-consuming task that requires continuous monitoring of the data pipelines to ensure no data loss. Sounds challenging, right? Well, no worries. You can also automate your data replication process with a No-code ELT tool like Hevo data, which offers 150+ plug-and-play integrations.
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Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.
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