Everyone in data management (DM) knows that the volume of data continues to increase; this is true for conventional, structured data as well as new big data and unstructured data. Equally challenging is the dramatic increase in the complexity and diversity of data. As if handling traditional enterprise data weren’t challenging enough, many organizations are now presented with data from sources with which they have little or no experience. This includes big data (from Web
applications and scaled-up enterprise applications), streaming data (from sensors and monitoring applications), and human language text (from social media and traditional customer relations applications). In a related trend, more and more businesses want to compete on analytics, which in turn requires DM to provision data for a broadening range of analytics.
Although there are many data-driven business opportunities an organization might seize, IT and data management teams in many organizations today are struggling to deliver data in the time frame and with the quality that business initiatives require. To seize data-driven opportunities with agility, organizations need a data management architecture (or infrastructure) that is comprehensive, flexible, and highly productive.
Today’s diverse data and burgeoning use cases demand a comprehensive approach. End-to-end data management is one way to adapt to data’s new requirements.
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