Many organizations have deployed Hadoop to gain additional value from big data. Most envisioned they could use Hadoop to easily combine and analyze large data sets to identify new market opportunities or better detect fraud. However, combining data sets within Hadoop is easier said than done. Most organizations, thus far, have faced obstacles and failed to achieve estimated return on investment from their Hadoop deployments.

Organizations can easily use Hadoop to process or model larger volumes of data from single sources. However, companies experience challenges when attempting to combine and analyze different data sources due to their varied structures, making connecting and mapping them together extremely difficult. Even data from a company’s internal accounts and systems may include multiple account numbers for the same customers, and the problems become even more complex when an organization makes acquisitions or merges with other companies.

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