As enterprise data flows in from diverse locations, consolidating heterogeneous data sources becomes a significant hurdle in optimizing data processing. Standardizing data emerges as a prerequisite for effective and accurate analysis. The absence of a suitable integration strategy can lead to application-specific and intradepartmental data silos, hindering productivity and delaying results.
Unifying data from disparate structured, unstructured, and semi-structured sources is a complex task. According to a Gartner survey, one-third of respondent companies consider "integrating multiple data sources" as one of the top four integration challenges.
Recognizing 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
Extracting source data is the initial step in the integration process. However, it can be complicated and time-consuming if data sources have different formats, structures, and types. Moreover, once the data is extracted, it needs to be transformed to make it compatible with the destination system before integration. To overcome this challenge, 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, opt for a tool that supports structured, unstructured, and semi-structured sources to simplify and streamline the extraction process.
Data Integrity
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. To ensure that correct and accurate data enters 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.
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