There are two ways in which data can be created: either two existing datasets are merged to create a new dataset or a whole new dataset is entered in the database. The latter approach is hard as creating new effective data is time consuming and requires a different skillset. Merging data to create new data or insights is less expensive than creating new data. What's good about merging data is that the whole process of merging data can be automated. Automation is a valuable component when it comes to scaling your business.
A data warehouse is powerful because it retrieves data from different data sources such as IoT devices and combines them to form a renewed perspective. This process of merging data from different sources is called data integration.
In the present world, big data integration is more important, as organizations are collecting and storing huge amounts of data from various sources. This data is highly valuable for business decisions, but it can also be overwhelming to deal with. Data integration helps in managing this big data, by providing a unified view of the data and making it accessible for analysis and reporting.
Data integration ensures that there is a consistent flow of data from one point to another; to be able to do this it automates the ETL and ELT processes. In this article, you will learn what data integration is, its use case and different approaches used to implement it.
What Is Data Integration and Where Is It Used?
Data integration is the process of combining data from multiple sources into a single, unified view. This can be accomplished in a variety of ways, depending on the specific needs of the organization and the types of data being integrated.
The data integration architecture comprises:
A network of data sources
A master server
Clients who access the data
To make sure that data is up to date, data integration employs the Change Data Capture mechanism, which notices data changes in a database and then applies to the data warehouse. While data streaming integration is employed to ensure that there is a continuous flow of data integration in real time. You can get more quality data by automating the data transformation process.
Data integration is important for facilitating big data. Fetching data from different sources expands the horizon for your business intelligence platforms. Having multiple data sources gives you more perspective and insights. Many companies such as Open AI need a consistent flow of fresh data from millions of data sources to keep ChatGPT running. Below is a list of use cases where data integration is used.
Data integration plays a vital role in the field of IoT (Internet of Things) where data from various devices and sensors is collected and integrated for analysis and decision making. It is also used in various industries like healthcare, finance, and retail to gain insights from the data and make better decisions.
One of the most common use cases for data integration is the creation of a data warehouse or business intelligence platform. In this scenario, data is extracted from operational systems, such as databases and flat files, and then transformed and loaded into a central repository. This allows organizations to gain a more comprehensive understanding of their data and to perform advanced analytics and reporting.
Another use case for data integration is customer data integration (CDI). In this scenario, data from various sources, such as customer relationship management (CRM) systems, social media, and web analytics, is integrated to create a single, unified view of the customer. This allows organizations to gain a more complete understanding of their customers and to personalize their interactions with them.
Benefits of Integrating Data
Data integration is important for companies because it collects data from different departments to give the annual performance of the company. Automated data integration processes save the company lots of money and time. Without automated data integration companies would have to keep on manually fetching data from different sources.
Manually integrating data is costly because employees have to keep on reporting any data changes since the data is not synchronized. Also, manually integrating leads to many errors as employees may not know the exact locations of the data source and can fail to know when data is incomplete or not fresh.
There are many benefits to data integration, including:
Improved data quality: Organizations can increase the precision and completeness of their data by integrating data from many sources, which in turn enables them to make better decisions.
Efficiency gains: By automating many of the laborious procedures associated with combining data from several sources, companies can save time and minimize errors.
Better decision-making: Organizations can better understand their data and base decisions on that understanding by having a unified, single view of the data.
Greater insights: By combining data from different sources through data integration, organizations can gain new insights and identify patterns and linkages in the data.
Better customer interactions: Organizations can personalize their contacts with customers and deliver a better customer experience by having a single, unified view of the customer.
The Process of Integrating Data
Data integration utilizes many queries such as union of conjunctions and aggregation when matching data. To get a holistic view data integration uses query maps to draw the relationship between schemas and between the Data source and the target database.
ELT and ETL are the core of data integration as they move data from the data source to the target. ETL stands for:
Extract: This involves replicating the requested data from the data source
Load: This involves moving the replicated data from the source to the target
Transform: This involves cleansing data to meet the desired standards. For example:
Applying mathematical functions
Converting data types
Modifying text strings
ETL transforms the data before sending it over to the target. This is to make sure that the data is completely compliant with the target. ETL makes it easier to comply with security standards because you can omit any sensitive data before sending it to the target.
On the other side, ELT extracts the data and loads into the target without transforming the data. The data will be transformed and enriched by the target. This method favors structured and unstructured data. ELT is faster than ETL because data is sent to the target without any transformations and this can happen simultaneously. ELT is highly used in business intelligence and big data analytics.
There are different ways data integration ways in which data integration can be implemented and approached. Here are 3 data integration approaches you should know:
In this implementation, applications are the ones that locate, fetch, and merge data.
Middleware data integration
This implementation strategy uses an intermediary component or application to make the data compatible and then merge the data.
Manual data integration
This approach is different from the above approaches as the user has to locate, fetch, and merge data manually from different sources.
Factors You Should Consider When Integrating Data
These factors are:
Data version: This determines the age of the data found in the data warehouse. You have to be very careful when choosing data that has an older version as the data is likely to be inaccurate and misleading.
Data examination and matching: This factor determines how data will be merged. Will the data that is from the IT department data source merged with the one from the finance department data source in the computer science database? This factor is concerned about having an integration strategy or algorithm.
Data specification and granularity: For your data integration procedure to be successful in giving groundbreaking insights and perspective you have to know how detailed and specific the data should be. Collecting what you only need is important to avoid storing any unwanted data that will drive storage costs. You need a complete idea of which data types and tables you have to retrieve for your data integrations process to produce complete insights.
Drawbacks of Using Data Integration
Data integration is very complex in a way that getting to build the infrastructure is challenging. Also new technologies are demanding. Are you going to use structured and real-time data? Your infrastructure must be flexible and ready to accept different types of data.
Integrating data from sources such as legacy systems can give you data that is incomplete.
One of the key challenges of data integration is dealing with data quality issues. Data from different sources may be structured differently, use different codes or formats, and contain errors or inconsistencies. These issues must be identified and addressed during the integration process to ensure that the resulting data is accurate and reliable.
Another challenge of data integration is dealing with data security and privacy. Data from different sources may contain sensitive information that needs to be protected, such as personal identifying information or financial data. This requires careful planning and management to ensure that the data is properly secured and that access to it is controlled.
Overall, data integration is a complex and multifaceted process that requires careful planning, management, and execution. It is essential for organizations to have the right tools, processes, and expertise in place to effectively integrate data from multiple sources and to ensure that the resulting data is accurate, reliable, and secure.
In conclusion, data integration is a vital process that allows organizations to combine data from multiple sources into a single, unified view. This enables organizations to make more informed decisions by providing a complete, accurate, and up-to-date view of their data. Data integration is a complex and multifaceted process that requires careful planning, management.
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