Organisations looking to analyse big data typically have to pull it together from various systems, making data integration a fundamental component of a big data analytics platform.
For most businesses that have embraced big data analytics, the IT world in which the enterprise data warehouse was the one place where companies stored any data worth analyzing is a thing of the past. Today, such organizations typically possess a variety of analytical data stores, still including a data warehouse but also the likes of Hadoop clusters, NoSQL databases and department-level data discovery systems.
There's potential business value in combining data sets from different platforms for analysis and data visualisation -- uncovering that value is one of the primary goals of big data applications. But before analytics teams can start their work, organizations often need to develop an integrated big data analytics architecture so data scientists and other analysts can access all the information they're looking to explore.
For example, merging data pulled from a customer relationship management system with call center notes on interactions with customers could give a company a better understanding of issues with its products and services. And adding social media data to the analytics mix could provide a fuller view of customer sentiment. There are multiple ways to go about blending such data so it can be analyzed and visualized -- the technology options include conventional data integration software, pre-built connectors between systems and integration functionality built into data discovery and visualisation tools.
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