Businesses’ daily operations generate a significant amount of data every second, Data is the most important thing for any business. Even though data quality is essential for making important business decisions, more than 70% of organizations still don’t have a clear, centralized plan. The problem with data silos is that data is spread out across different systems. This makes it hard for different departments, processes, and systems to work together. Without data integration, getting to a single task or report would require logging into multiple accounts or sites on different systems. Also, organizations could face disaster if they don’t handle data correctly.
Data integration can be seen as one of the essential parts of the process of managing data. It gathers data from various sources and puts it into a single dataset or data warehouse to deliver a unified view that helps in efficient data management.
Data integration tools attempt to integrate data regardless of its kind, structure, or volume because of the exponential growth in data volume, formats, and distribution. Ingestion approaches such as ETL (Extract, Transform, Load), mapping, data cleansing, and data transformation are used in the data integration process by business organizations enabling them to abstract valuable information from data.
Organizations are leveraging big data and its benefits to stay competitive in the market. Complete and accurate information can be derived using data integration through efficient management of large datasets. Data integration provides a comprehensive view of financial risk, key performance indicators (KPIs), supply chain operations, and business processes to support businesses by delivering business intelligence and advanced analytics.
Data Integration Techniques
Various changes have occurred in the process of data integration. The complexity of the data integration process has made it difficult to develop a universal approach to the data integration process. The major methods used in the data integration process are:
• Manual Data Integration
• Application-based data integration
• Middleware data integration
• Uniform data access integration
• Common storage data integration
Manual Data Integration
Manual data integration or hand-code data integration is one of the common methods of data integration used when only a small number of data need to be integrated. As mentioned in the name, the data needs to be collected manually by the user from various sources, and cleansed data is uploaded into a single database or a data warehouse. The user needs to thoroughly understand the location, logical data format, and data semantics while manually integrating data.
Manual integration can’t provide a unified view of the data and has scaling limitations. Manual data integration is applicable in the case of a few data sources and a small volume of data. The possibility of errors is high while handling complex data connections and big data queries.
Application Based Data Integration
Software applications are leveraged in the process of application-based data integration. Data from various sources are located, retrieved, cleaned, and integrated. Data can be easily transferred between sources because of the interoperability delivered through the solution. The whole process can be done using a single application makes it simple for data scientists and doesn’t require technical knowledge to carry out the process.
The requirement of applications in the data integration process makes the process difficult while handling large volumes of data sets and sources. Only a limited number of applications and data sources and applications can be integrated using this method as it requires different applications for integrating various types of data.
A middleware or software is used in the middleware data integration method to transfer data to a database. This method is mainly used while integrating legacy systems with modern systems. It also delivers better data streaming by automatically transforming and sharing data. The method also delivers easy accessibility between networks in a system using coded software.
The operational costs are high in middleware data integration, as there is a requirement for technical expertise to install and maintain middleware. The middleware data integration has only a limited functionality as it doesn’t support all systems.
Uniform Access Integration
Enterprise data from various datasets can be accessed and presented uniformly, allowing data to remain in its original location. The method provides a simple and unified view of the data to the end user. The data can be stored in low storage as it doesn’t require large storage space. Various systems and applications can be connected easily to a central source using this method.
The method is only applicable while handling the same kind of dataset or database. Frequent data access requests brought on by utilizing source systems to retrieve data might strain data host systems, impose functional restrictions, or introduce delay. Data integrity and data quality can also get affected due to the multiple access points.
Data warehousing or common storage method is one of the most popular methods of data integration. The data used by various applications or programs are securely managed and stored in this method. Only one source of data can be accessed using this method as the information collected is initially subjected to data transformation before it is transferred to the data warehouse. The data version management feature allows the users to combine data from various sources, including mainframes, databases, flat files, etc.
As the data volume increases, the cost of the process also increases with it in this method. The requirement of technical expertise to set up the integration, oversee and maintain it increases the maintenance cost.
No matter the size of the company or its accessible resources, precise and efficient data processing and administration will improve understanding of the company’s customer experience and overall ecosystem. Businesses can implement timely and well-informed company strategies through the process to boost performance.
It’s essential to keep in mind that data integration is a continuous process. Technologies are constantly developing at a rapid pace. Integration solutions must be able to adapt and change with the times. If not, they rapidly become out-of-date and useless. Hence it is essential to ensure that the data integration projects are agile enough to address the future when planning them properly.