How Advanced ETL Solutions Help Mitigate Various Data Integration Problems?

The data landscape is rapidly expanding, owing to the growth and proliferation of different digital communication channels. Today, businesses understand the importance of making the best use of data for the growth of their business operations. They not only have access to various forms of big data but also receive data from APIs, IoT applications, websites, social media platforms, and so on. Although all these data may serve individual purposes, they are not in a standard format, and a business cannot use them to their competitive advantage. This is where a good ETL data integration software can help. There are various data integration software solutions that promise safe and efficient data integration. However, many of these fail to address the key challenges of data integration. Let’s see what are the key challenges of data integration, and how a good ETL data integration software can help you overcome them. 

5 Key Challenges of Data Integration for Business Enterprises

The data integration is performed with an objective of combining different sets of data into a single platform. This integration provides a unified view to the user, who may be a key decision maker or a concerned business executive. It may sound like a simple integration, but the process is driven by several challenges. The below-discussed are five major challenges of data integration faced by enterprises. 

  1. Increasing Dependence on Exogenous and Indigenous Data Sources: As discussed earlier, today businesses receive data from different exogenous and indigenous digital sources. These data sources follow different data structures and models. At times, the representation of the same semantics may differ across these data sources. Also, there may be heterogeneity in terms of their operating systems, as well as access methods. This makes data synchronization difficult. 
  2. Individual Characteristics of Data Sources: Although designed for information delivery, the data sources have typical characteristics. They serve as independent information systems and are not designed for data integration. So, they cannot be altered or manipulated to work in a way you want unless there is a knowledge of how a particular data format will work if they are transformed into a new format. 
  3. Addressing Data Changes: As the technology landscape is fast evolving, so data sources are also regularly updated. However, that doesn’t mean that these updates across various sources are happening simultaneously or at regular intervals. Handling the changes in data models, thus becomes a challenging task in data integration.  
  4. Data Mapping: An in-depth understanding of source systems is a mandatory requirement when data mapping between two or more systems. There is a misconception that an understanding of data formats and their naming conventions will serve the purpose, which is not true. This task requires a deep understanding of the relationship between different data sets. The different systems are governed by separate business rules, so they must be understood while aiming an integrated data set.
  5. Accuracy and Performance of Queries: During the data integration, queries from different heterogeneous sources are always formulated to create a unified model. This formulation may bring challenges due to data duplication, and several other unidentified errors.  

Unlike earlier years, nowadays several ETL solutions are designed to address these challenges while being process-centric and results-oriented. How do they mitigate these challenges? The discussion is continued in the next section. 

Understanding the Various Problem Solving Features Offered by Advanced ETL Solutions

  • Advanced ETL solutions use real-time events and polling triggers to initiate data synchronization between different heterogeneous data sources. They help eliminate data silos, and make available the right data to business users whenever required. 
  • Data translation, as well as mapping, is not easy, because they require extensive and complex coding. Any error in the coding means that data will fail to serve the purpose. This is where an ETL solution will help. There are several ETL solutions in the market with in-built data translation capabilities. These solutions help avoid extensive coding and allow users to transform data to fulfill their operational requirements.
    Some ETL solutions are equipped with data mapper modules, which enable users to gain an understanding of various rules governing data formats. Many of these data mappers can be visualized through drag and drop interface, which enables users to understand the mapping rules, as well as create, manage, and store data maps for future uses.
  • Many old-fashioned ETL solutions have limitations when it comes to handling data changes and huge data volumes. However, advanced ones are designed to handle data changes and bulk data volumes. Equipped with features such as data mapping engines, and a schema parser, these solutions can easily handle huge data volumes at any given time. As the name suggests, schema parser enables data transformation on the basis of schema definitions. These features also lend scalability to ETL solutions, thereby making them future-perfect in all senses.
  • ETL solutions equipped with intelligent data enrichment and cleansing features help enrich data by removing duplicate values, and create rules to highlight unidentified errors, and offer solutions to fix them as well.   

Thus, while choosing an ETL solution for your business it is important that you select a one that addresses the key challenges – mentioned in this post, and the ones specific to your business. Ensure that the chosen solution is scalable and responsive to your future business needs, and enable you to easily manage data integration across your enterprise. 


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