Data integration helps in ensuring the accuracy, consistency, completeness, as well as the validity of an organization’s data. Let’s dive deeper into understanding what data integration means, its benefits and approaches, along with challenges and strategies for your business.
Explain Data Integration
Data integration meaning can be referred to as the process of bringing together data from various sources into a single and unified view. By doing so, an organization can gain access to complete, accurate, and updated datasets for data analysis, and business intelligence, along with other applications and business processes.
The process of data integration includes data replication, ingestion, and transformation that helps in combining different types of data into standardized formats so that they can be stored in their target repositories such as data warehouses or data lakes.
There is no universal approach to data integration. However, it usually includes a network of data sources, a master server, and clients accessing data from the master server. In the typical process of integration of data, the client sends a request to the master server.
It then takes the needed data from internal and external sources. This data is then extracted from the sources and combined into a single and cohesive data set which is served back to the client for use.
Examples of data integration can be an e-commerce platform merging inventory data from warehouses and suppliers for real-time stock management. It can also be a company integrating customer data from multiple platforms to gain unified insights and personalized experiences.
What are the main approaches for data integration?
To implement the data integration approaches, data engineers, developers, and architects manually code using SQL, or use specific data integration tools to streamline the development process and automate the system.
The five main approaches to data integration are:
ETL stands for Extract, Transform, and Load. In this approach, data is extracted from one system, transformed, and then loaded into a target repository. Data is essentially transformed in a staging area before the loading process takes place, usually in a data warehouse.
This helps in accurate and fast data analysis in the target system. ETL is ideal for small datasets that require complex transformations.
- ELT
In the ELT pipeline, the data is immediately loaded and transformed within a target system. This system can be a cloud-based data lake, data lake house, or data warehouse. When you have large datasets and timeliness is important, the ELT approach is preferable.
ELT usually operates in two ways- micro-batch or change data capture (CDC) timescale. Micro-batch is like grabbing the new stuff added to your shopping cart since you last visited the store. It only considers the change since the last time.
On the other hand, the CDC is like having a radar that instantly grabs anything new that appears on the shelves of the store. It’s always checking for changes and collecting them.
- Data Streaming
In the streaming data integration approach, data is moved to a new repository in batches instead of loading new data. This process is done in real-time from the source to the target destination.
In simple terms, instead of gathering data in groups, it is sent as it happens, in a constant flow. Modern data integration (DI) platforms can deliver such analytics-ready data into streaming and cloud platforms, data warehouses, and data lakes.
- Application Integration
The application integration approach allows separate applications to work together by moving and syncing data between them. An application data integration example is that this approach can help your HR and finance systems remain on the same page by keeping their data consistent.
Moreover, different apps have their unique APIs for giving and taking data. Therefore, Software as a Service (SaaS) application automation tools can help you create and maintain native API integrations efficiently and at scale.
- Data Visualization
Data visualization is similar to that of data streaming as this is one of the types of data integration that also helps in delivering data in real time. However, this approach only happens when data is requested by a user or application.
This approach can still create a unified view of data and make data available on demand by virtually combining data from different systems. Both data streaming and data visualization are ideal for transactional systems developed for handling high-performance queries.
These five approaches to data integration are always changing and growing along with the technology around them. Previously data warehouses were the main place for data storage. However, with new technology like data Integration Platform as a Service (iPaaS), bigger datasets can be managed in numerous ways along with quick analysis. Due to this, ELT, streaming and API are preferable to ETL.
What are the Benefits of Data Integration?
The major purpose of data integration is to bring data from different systems together so that it provides you with a reliable, single source of governed data. Data analysts, data scientists, or engineers can analyze the complete dataset to identify patterns and relationships that can lead to actionable insights and help in improving business performance.
Therefore, the key benefits of data integration are:
- Helps in Business Success
An organization receives multiple and complex datasets from different and unconnected sources like web analytics, marketing automation, or CRM systems. To use the full dataset, it needs to be combined in a single system where the data can be cleaned, organized, and analyzed.
This can help in fewer errors, duplications, and rework. Therefore, it provides a reliable, single source of accurate, governed data that you can trust for your business.
- Improves Unification of Systems
Employees in different departments often require the company’s data for shared and individual projects. For this process, a secure solution is needed so that data can be safely delivered via self-service access to all business lines.
Moreover, employees also generate and improve data that the rest of the business needs. Therefore, data integration needs to be collaborative and unified so that it improves collaboration and unification across the organization.
- Saves Time and Boosts Efficiency
A major advantage of data integration is that it helps save time and work smarter. For instance, when a company gets its data in order, it takes way less time to make it ready for analysis. It eliminates the process of organizing the data from scratch every single time.
Additionally, using good tools instead of coding everything also saves time. This time can be used for conducting in-depth analysis and making the company better and faster at what it does.
- Reduces Errors
With data integration, a company’s data resources are in a single place. Without this process, an employee would have to manually know the location of every data so that they can gather it for analysis.
They would also need to know different data software to execute the data gathering accurately. And if they are unaware of a data repository, then the employee would have an incomplete data set.
This is where data integration plays such a crucial role. With integrated data, analysts, development, and IT teams can focus on strategic initiatives with time that isn’t taken up on manual data gathering and preparation processes.
- Delivers Valuable Output
Integrating data can help in improving the value of business data over time. As data is integrated into a centralized system, quality issues can be more easily identified and necessary improvements can be implemented. This results in more accurate data which is the basis of quality analysis.
Data Integration in Modern Business
It is important to understand that data integration isn’t a one-size-fits-all solution. The right approach can depend on different business needs. So, let’s look at some of the common use cases for data integration tools:
- Data Warehouses and Data Lakes
Large organizations often implement data integration initiatives to create data warehouses. Here, multiple data sources are combined into a relational database. Data integration in data warehouses mainly allows users to run queries, compile reports, generate analysis, as well as retrieve data in a consent format.
For instance, many companies depend on data warehouses such as Microsoft Azure to generate business intelligence from their data.
- Big Data
Using Big Data means tapping into huge pools of information like giant data lakes. Data lakes are centralized repositories created for strong, processing, and securing large amounts of structured, unstructured, and semi-structured data.
Companies like Google and Facebook process a non-stop influx of data from billions of users. This level of information consumption is commonly referred to as big data.
With more big data enterprises, more data is becoming available for businesses to leverage. In this context, the need for sophisticated data integration efforts can be identified so that data can be combined smoothly, helping in elevating business performance.
- Business Intelligence (BI)
Data integration in business intelligence makes the overall process easier. By bringing together data from different sources, companies can quickly understand what’s going on. This helps them make smart decisions based on what’s happening right now and the actions of the past.
Unlike predicting the future, BI focuses on describing and understanding the present situation to help businesses with important decisions. It works well with data warehousing, where it provides easy-to-understand or readable information.
Challenges to Data Integration
Tackling data integration can prove to be tough. According to the data integration definition, the process is like solving a puzzle and making everything fit together. However, you may come across different challenges like:
- Reaching the Finish Line:
It’s important to understand data integration more than finding a solution for a specific business problem. It is critical to know what data is needed, where it can be found, which system will use the data, what type of analysis will be conducted, and how frequently data and reports will be updated.
- Handling New Types of Data:
In the present time, you can find diverse systems generating different types of data like videos, sensors, and cloud data. You need to figure out how to adapt your data integration infrastructure to meet the demands of integrating all these data. Adapting all these quickly can pose a significant challenge.
- External Data Issues:
Data collected from external sources may not provide the same level of information as internal sources. This can make it difficult to examine the data. Moreover, contracts with external vendors can add to the difficulty of sharing data across the organization.
- Data from Legacy Systems:
Data integration efforts include adding data stored in legacy systems. However, sometimes the older data do not have the same information or details as the modern systems which can cause problems.
- Keeping Up the Integration Process
Data integration is a continuous process. Even after you have set up the integration system and it is running, the task is not complete. Your team needs to always update and keep up with new methods, rules, and regulations to obtain quality results.
Data Integration Strategies for Business
There are different ways to integrate data, depending on the size of your business, its requirements, and available resources. Below are some of the integration strategies that you can implement:
- Manual Integration
In this process, data is manually gathered from different places by accessing interfaces directly, before it is put together. Although this method is not very efficient, it works best for small organizations with minimal data resources.
- Middleware Integration
This approach utilizes a middleware application that acts as a mediator to normalize data and bring it into the master data pool. In simple terms, it helps different systems understand each other and is specifically useful as older systems do not easily share data.
- Uniform Access Integration
Uniform access integration makes data look consistent when it is accessed from different places. Despite different sources, the data is left in its original source. It is like creating a common appearance without moving the data itself.
- Application-based Integration
In app-based integration, software does the heavy lifting by locating, retrieving, and integrating data. During the data integration process, the software is responsible for making different data systems compatible with one another so that they can be transmitted from one source to another.
- Common Storage Integration
One of the most frequently used approaches, here a copy of the data from its original source is kept in an integrated system and processed for a unified view. This strategy is opposite to uniform access which leaves the data in the source. The common storage approach is essentially the idea behind traditional data warehousing where everything is available in one place for easy use.
Features of Data Integration Tools
To seamlessly conduct data integration for your business, you can leverage different data integration tools. However, these tools need the following feature so that it can make the whole process easier for you:
- Diverse Connectors: The more connectors your tool has, the quicker your team can work with different systems.
- Open Source: This provides more flexibility and avoids getting tied to one specific company’s tools.
- Easy to Use: Identify tools that are easy to learn and use. Moreover, if it has a visual interface, it helps you see your data flow.
- Cloud Compatibility: The tool should work well whether you are using one cloud, multiple clouds, or a mix of cloud and other systems.
- Portable: The ability to build integrations once and use them anywhere is a significant advantage. This can help more companies to move to a hybrid cloud setup.
- Transparent Pricing Model: Ensure the pricing stays fair even if you add more connectors or deal with bigger data volumes.
Conclusion
Thus, data integration is the backbone of creating a streamlined and cohesive data ecosystem. With this process, you can organize different data sources and enable organizations to extract meaningful information that can help in making informed decisions.