The combination of an accelerating rate if data generation and drastic changes in IT infrastructure, software and development environments leaves most organizations unable to keep up with, much less exploit new technologies while also maintaining critical IT business processes. Indeed, an organization’s IT legacy can leave it at a disadvantage to nimble, disruptive new competitors building digital businesses around cloud services and using agile, DevOps development processes. One approach IT organizations can take to resolve the paradox of trying to be both a business utility and technology innovator is through what’s popularly known as Bimodal IT.
As I detail in this post, there is a growing concensus among business leaders and IT analysts that mining the vast and growing quantities of data accumulating in most organizations will become a significant competitive advantage for those that can do it effectively. An important part of this transformation of data to information will be having the proper organizational structure and here, bimodal IT could pay dividends. Although often associated with agile, mode 2 DevOps teams using public cloud services to build new products, bimodal is increasing appropriate for data-related activities as organizations seek novel ways to exploit the flood of business information that often doubles in volume every couple years.
Follow the link to learn how the learning curve for big data and analytics software like Hadoop, Spark, R, Kafka and Storm and the complexity of building and deploying the distributed systems to run them mean that Mode 2 teams are the ideal vehicle for introducing data analytics to an organization, building aggregated data repositories and experimenting with new analysis techniques.