The Complexities of Big Data Adoption

Even the smallest application today provides a huge amount of data. While this is good news for businesses, it is also rather difficult to sift through the maze to find a single piece of relevant information. Given the sheer volumes of information, there are also a number of other challenges faced while handling Big Data, such as methods for storing all of the data and how your organization does it, identifying the most important data points, dealing with data even if it exceeds your expectation in size, how to implement a proper analysis process, extreme complexity in data, finding sufficient talent of qualified personnel who can deal with data, data explosion and diversity, lack of self service and automation of processes , challenges in cloud computing, operational analytics, identifying the applications that provide maximum value, bypassing the learning curve.

The first issue with Big Data adoption is taking a ‘technology intensive’ approach to the whole thing. The technology is undoubtedly complicated and the skills required to deliver are relatively scarce. Most common Big Data adoptions start with the data warehouse team reorienting themselves to deliver Big Data. They tend to make the mistake of taking a very heavy handed approach – beginning with defining data types, schemas, mappings and transformations, ETL jobs and processes and data quality processes and procedures. Soon, a Big Data effort transforms into a 3-4 year effort much like traditional ERP implementations, which completely overrules the point of such an implementation.

The best proposed way to tackle the implementation of Big Data is to divide-and-conquer. The first bifurcation is to separate Big Data from Data Science. The second problem of talent crisis includes both a problem of scarcity in professionals qualified to handle Big Data, as well as an issue in the way organizations operate. The late twentieth century was the age of the specialist, however, with such a mega combination of disciplines there is no way specialists can deliver. The need of the hour is actually a group of generalists who are qualified enough to run the show. Keeping a generalist Big Data engineering team to manage the infrastructure and operations simplifies complexity to a large extent.

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