3 Challenges of Integrating Heterogeneous Data Sources

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3 Challenges of Integrating Heterogeneous Data Sources

Here are three common challenges generally faced by organizations when integrating heterogeneous data sources and ways to resolve them.

· Integration Zone ·
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With enterprise data pouring in from different locations — CRM systems, web applications, databases, files, etc. - integrating heterogeneous data sources is a major challenge in streamlining data process. In such a scenario, standardizing data becomes a pre-requisite for effective and accurate analysis. The absence of the right integration strategy will give rise to application-specific and intradepartmental data silos, which can hinder productivity and delay results.

Consolidating data from disparate structure, unstructured, and semi-structured sources are complex. A survey conducted by Gartner revealed that 1/3 respondent companies consider “integrating multiple data sources” as one of the top four integration challenges.

Understanding the common issues faced during this process can help enterprises successfully counteract them. Here are three common challenges generally faced by organizations when integrating heterogeneous data sources and ways to resolve them:

Data Extraction

Challenge: Pulling source data is the first step in the integration process. But it can be complicated and time-consuming if data sources have different formats, structures, and types. Moreover, once the data is extracted, it will have to be transformed to make it compatible with the destination system before integration.

Solution: The best way to go about it would be to create a list of sources that your organization would be dealing with regularly. Look for an integration tool that supports extraction from all these sources. Preferably, go with a tool that supports structured, unstructured, and semi-structured sources to simplify and streamline the extraction process.

Data Integrity

Challenge: Data quality is a primary concern in every data integration strategy. Poor data quality can be a compounding problem that can affect the entire integration cycle. Processing invalid or incorrect data can lead to faulty analytics, which if passed downstream, can corrupt results.

Solution: To ensure that correct and accurate data goes into the data pipeline, create a data quality management plan before starting the project. Outlining these steps guarantees that bad data is kept out of every step of the data pipeline, from development to processing.


Challenge: Data heterogeneity leads to the inflow of data from diverse sources into a unified system, which can ultimately lead to exponential growth in data volume. To tackle this challenge, organizations need to employ a robust integration solution that has the features to handle high volume and disparity in data without compromising on the performance.

Solution: Anticipating the extent of growth in enterprise data can help organizations select the right integration solution that meets the scalability and diversity requirements. Following a piecemeal approach is also beneficial in this scenario, where one data point is integrated at a time. Evaluating the value of each data point with respect to the overall integration strategy can help prioritize and plan.

For example, an enterprise wants to consolidate data from three different sources: Salesforce, SQL Server, and Excel file. The data within each system can be categorized into unique datasets, such as sales, customer information, and financial data. Prioritizing and integrating these datasets one at a time can help organizations scale the data processes gradually.

Conquering the challenges of heterogeneous data integration is critical to enterprise success. Have you encountered any problems when integrating data from disparate sources? Were you able to resolve them? Let us know in the comments.

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Published at DZone with permission of Tehreem Naeem . See the original article here.

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