Why IBM for Sales Performance Management?

In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.

There’s a pervasive perception held by many organizations that data governance or effective access management for data somehow reduces the ability to leverage and derive insight from information. This could not be further from the truth. Full control of data is necessary for both data protection and data leverage. In this webina

To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.

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