Data integration aims to create a dataset that can be used many times without reformatting or loading into a new system. There are many approaches to integrating data so that it is fresh and relevant. However, the diversity of data sources and the need for quick responses have proved challenging. In addition, multiple approaches to data integration have resulted in a proliferation of data integration tools.
Conventional data integration approaches risk creating a "data museum" – valuable, perhaps, for looking at past performance, but not for real-time or predictive analytics. Therefore, enterprises must address data quality and integrity to maximize the value of business data. They should also ensure a consistent and timely flow of data into business intelligence and analytics applications. Significantly, enterprises must ensure that business leaders act on the insights derived through analytics.
Intelligent integration helps enterprises extract value from their data. Automation allows users who are not specialist data scientists to pull data sources together for analysis. Furthermore, enterprises must deploy tools that provide better visibility of data assets. Master data management policies, strong data science capabilities, and a chief data officer with a seat on the board are also needed. These put together will ensure that enterprise data retains its relevance and freshness.
Today, because enterprises drive their business through digital ecosystems, they have to be much more flexible and adapt to knowing what data is needed, where that data resides, and how best to integrate it, at the right time, at the right freshness, so that information is relevant.
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