Data integration is the collection of data from different sources into one unified source. Data integration begins with data assimilation, scrutinization (cleaning), ETL mapping, and data transformation. Hence, data integration makes it possible for analytic tools to produce efficient business intelligence solutions.
When it comes to data integration, there is no such thing as a universal approach. However, a data integration solution usually involves common elements like a connection of data sources, a central server, and clients retrieving information from the server. The client forwards a request to the main server, which collects the information from internal and external sources, cleans it, and collates it into a single, unified data set. The final form is now sent back to the client for use.
The importance of data integration to businesses
The importance of data integration to businesses cannot be over-emphasized. Many organizations collect enough data to run their business. However, the data often ends up in separate sources. Accessing relevant information becomes harder over time due to data being stored in different systems that are not necessarily compatible. This constitutes what is known as data silos.
The data that needs to be integrated within any organization may be CRM system data, marketing operation software, web traffic, partner data, or customer information data. Information from these different sources is useful for analytical purposes and can help business leaders make informed decisions. Bringing all this information together manually can be taxing for data engineers and developers.
Before the development of data integration tools, typical business reporting involved accessing multiple systems, collecting data with native apps, and manually cleaning and reformatting the data to make it usable.
The key benefits of a well-organized approach to the integration of data include the following:
1. Improves data unification and collaboration
Different departments may be located in different places and usually need access to the company’s data for collaborative use. IT requires a safe solution for providing data through self-service access between locations.
In addition, there is always the constant generation of data by employees in various departments that other departments of the business need. Data integration helps to collect, clean, and unify this data to enhance organizational collaboration and unification.
2. Saves time and increases productivity
A company that takes the time to integrate its data properly cuts down on the time it spends preparing and analyzing data. The automation of the unification process saves the company the time of gathering information manually. Employers are no longer required to set up a new manual connection whenever they need a new report to be built.
Also, utilizing the right tools instead of manually coding the integration helps the development team save more time and resources. The company can put the extra time to other uses to improve productivity. Reserving hours for analysis and implementation helps to develop a more competitive organization.
3. Reduces errors and workload
When it comes to a company’s data resources, there is a lot to keep track of, which makes the work very taxing. Manual collection of this information means that the employee must be conversant with all the locations and accounts necessary to collect information. It also requires a company to have all the essential software applications installed before it begins the integration process.
Any omission or addition of data without the employee’s awareness will result in data inaccuracy. Without an effective data integration solution, the employee will have to repeat the data collection and scrutinizing process more frequently for any changes to take effect. The automated update makes the process easier in real-time whenever the need arises.
4. Provides better quality data
Data integration solutions lead to the collection of quality data, which adds value to a business. As the data integrates into a central system over time, it will help the company identify quality issues and implement the requirements for improvement. Data integration solutions are the bedrock of quality data analysis.
The role of data integration in modern businesses
The demands of data integration vary from one organization to the next depending on different factors. Some of the use cases for data integration include the following:
Leveraging large datasets
Large companies like Google and Facebook receive continuous data entry from millions of users around the globe. The process is what data professionals refer to as big data. With the growth of big data generation, more data becomes available for businesses to leverage. Therefore, a more sophisticated central data integration solution has become essential for many organizations.
The creation of data warehouses
Many large businesses use data integration solutions to create data warehouses that can be used for advanced analytics. The data warehouse allows users to query the system, gather reports, and analyze and retrieve data in a consistent pattern.
Simplify business intelligence
Data integration makes the business intelligence (BI) process simple. Organizations can easily view and understand the available information at their disposal. It helps the organization develop actionable plans regarding the current situation of their business. Data integration makes it easier for data analysts to collect and analyze disparate information for precise evaluation.
Data integration challenges
Taking several data sources and integrating them into one central system is technically challenging. As more businesses develop data integration solutions, it becomes necessary to create automated data integration solutions to ensure consistency in moving data to its appropriate locations. While the process can provide long-term benefits and cut costs, numerous obstacles can hinder its implementation.
Some of the common challenges facing organizations in the development of their data integration systems include the following:
Unclear paths to objectives
Many companies are usually aware that they need to integrate their data. Also, they are mindful of the solutions to specific business challenges. What often occurs if they fail to consider the right path to their destination? Any professional implementing a data integration solution must consider these aspects:
Data types of data that needs to be integrated
The sources of the data
The preferred method of data integration
The analytics system that will use the integrated data
Required frequency of updates
Data from legacy systems
Data integration solutions may also require access to data stored in legacy systems. However, such data often lacks markers like dates of activities and time. Identifying more current information becomes difficult in this case. Additionally, some data integration software may not support outdated storage systems.
Today, newer systems generate different data types, which include real-time and unstructured data from various sources like videos, IoT devices, and sensors. The volume, speed, and new data forms lead to new challenges. Finding ways to familiarize your data integration system to handle the structure of these more recent data forms becomes essential for business success.
Data from external sources may not provide the same level of detail as internal data. Therefore, it makes it difficult to analyze data at the same level. Also, contracting data integration to a third-party vendor can make data sharing across the organization difficult.
The job of data integration does not end after the system begins to function. The data team needs to keep the system up to the standard of best practices. They also need to ensure that the system meets the current organizational demands.
Data integration is now an essential part of every modern business. It helps business owners better analyze data to understand their business and make more informed decisions concerning its growth and survival. Data integration needs differ from one organization to the next, so the optimal data integration solution will be different for each business.
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