To explain data integration is the process of combining data from different sources and giving a unified view of integrated data. This process enables one to handle, manage and perform analytics on all your data in a single interface (using statistics). With new centralized technology systems available for business processes, sources, and types of data continuing to grow, Thus it is more important to understand data integration methods and tools.
Importance of Data Integration
Data integration is radical when a firm stores a wide range of information in different applications.
Let’s discuss some issues that can help solve data integration:
- Data Silos
As the name suggests, a data silo is a collection of isolated data. In terms of business, different information means that a particular business unit or department is controlled and not available throughout the organization. Organizations also face this problem if the software used to store information does not match.
- Slow analysis
Data analysts and leaders are heavily dependent on reliable data in today’s decision-making, and that good data takes considerable time to combine and analyze. Today, businesses need to analyze real-time data to realize any business value. Therefore, a reliable and developed system is needed to combine data.
- Comprehensive View
When data is spread across different platforms, sources or applications, it is difficult to have a holistic view of it. For example, customer data of a firm from different CRM devices or applications may vary offline and online stores, but the firm’s data team is to map that data using customer information and geographical information to analyze in-depth to scale sales
Methods and tools for data integration
The struggle of businesses is not a lack of data, but data volume and its timely analysis. It is very difficult to work on the timely analysis of massive data flowing from various cloud applications to IoT endpoints across organizations and industries.
The process of connecting and routing data from source systems to the target system is achieved through various data integration techniques (standard traditional or modern methods).
Traditional methods are usually batched and data analysts are not given the opportunity to perform real-time data analysis.
Modern data integration methods were built to develop the agile nature of data and adapt to the ever-changing needs of data integration. Some successful modern approaches include automated ELT (extract-load-transformation) and cloud-based data integration.
ELT basically changes the transition phase to the end of the data pipeline where you can load the data before it can be converted. In this way, the data warehouse remains a single source of truth. Thus, the integrity of warehouse data was not compromised while performing the transformations.
Cloud-based data integration helps businesses integrate their data into a cloud-based data warehouse that is common (but not always) from various sources (cloud application as well as on-premises systems). This combination of data results in better operational efficiency and better internal communication for businesses. More businesses work with a hybrid mix of software as a service (SASS)Solutions and on-premises applications, experts have indicated that 90 percent of enterprises are more inclined towards cloud-based data integration. Such integration allows the real-time exchange of data and processes. Integrated data can then be accessed by a number of devices via a network or the internet. Some common cloud-based data integration platforms are K2View data integration, Informatica cloud data integration, Amazon Redshift, Snowflake, etc.
Starting with modern data integration
Modern data integration approaches, manual effort to manage and scrub data sets and then load data into personal data warehouse environments are outdated. Now, the data you need can be stored, streamed, and delivered from the cloud-based data integration platform, when you need it. For exampleK2View Data Integration is a data integration platform that handles data from different sources in any technology or format, modeling data fields for business entities (e.g., customer, location, device, product). Next, this data is ingested into micro databases. Later, other data processing stages, such as data masking and conversion (which uses an in-memory database to perform data conversion at high speeds), are performed. Finally, this integrated data is sent to consumer applications.
In the world of data integration, modern data integration approaches provide a number of benefits, from reducing engineering costs and enriching data to reducing insight time and increasing adaptability to change.
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