Don’t Let your Data Lake become a Data Swamp
In an always-on, competitive business environment, organizations are looking to gain an edge through digital transformation. Subsequently, many companies feel a sense of urgency to transform across all areas of their enterprise—from manufacturing to business operations—in the constant pursuit of continuous innovation and process efficiency.
Data is at the heart of all these digital transformation projects. It is the critical component that helps generate smarter, improved decision-make by empowering business users to eliminate gut feelings, unclear hypotheses, and false assumptions. As a result, many organizations believe building a massive data lake is the ‘silver bullet’ for delivering real-time business insights. In fact, according to a survey by CIO review from IDG, 75 percent of business leaders believe their future success will be driven by their organization’s ability to make the most of their information assets. However, only four percent of these organizations said they are set up a data-driven approach for successfully benefits from their information.
The reality is that all these new initiatives and technologies come with a unique set of generated data, which creates additional complexity in the decision-making process. To cope with the growing volume and complexity of data and alleviate IT pressure, some are migrating to the cloud.
But this transition—in turn—creates other issues. For example, once data is made more broadly available via the cloud, more employees want access to that information. Growing numbers and varieties of business roles are looking to extract value from increasingly diverse data sets, faster than ever—putting pressure on IT organizations to deliver real-time, data access that serves the diverse needs of business users looking to apply real-time analytics to their everyday jobs. However, it’s not just about better analytics—business users also frequently want tools that allow them to prepare, share, and manage data.
To minimize tension and friction between IT and business departments, moving raw data to one place where everybody can access it sounded like a good move. The concept of the data lake first coined by James Dixon in 2014 expected the data lake to be a large body of raw data in a more natural state where different users come to examine it, delve into it, or extract samples from it.
