Have you seen the news about the whereabouts of missing Malaysia Airlines flight 370? In his Article „Mystery Behind MH370 Search Released“ Oliver McGee describes how „Scientists and engineers inside the U.K. firm, Inmarsat, have used a „groundbreaking yet conventional mathematical series-based theoretical prediction” alongside radar data. Seems like this is another good example of how to apply Big Data analytics.
Despite the fact that there are several aspects of big data which are obviously oversold and despite my reservations with the name I do believe big data will be of substantial importance to many organizations in the future. This is because Data types like text and voice have been with us for a very long time, but their volume and corresponding business issues of locating information within the data as well as connecting information to create new knowledge rightly proclaim a new era. The same applies for new technologies that allow analysis of such data.
Looking at those technologies it is interesting to observe in bigger enterprises that internal customers like IT are treated very differently than customers that can actually buy goods and services from the enterprise. There is a lot of technology that can help improve our insights in customer satisfaction and customer behavior. Unfortunately we are nowhere near that application of that technology to improve IT itself. Of course the same applies for Big Data analytics supporting disciplines like supplier performance and supplier relationship management.
If companies like Google, Facebook and Amazon manage to use large amounts of seemingly unrelated data to deliver information that provides a competitive edge, wouldn’t this be interesting to be applied to how an IT organization operates?
Let’s assume that especially a multi-supplier enabled IT Organization is a data-intensive organizational function, just like marketing, finance and human resources. And we should also assume that where big data really fits – in terms of industry coverage – is either already highly data oriented online business or industries with a high potential to benefit from it. If you observe in what types of software companies invest today you might have already concluded that we are moving beyond automating transaction based software towards analyzing the data they generate. Even if we assume that the analysis of some business intelligence data sets results in accurate predictions, feedback loops will emerge as soon as others start to respond to the predictions.
People who know me also know that I strongly believe that while technology issues can be challenging, the more difficult issues involve management and people related topics. That is why I am still convinced that we in the IT industry need to move away from a bottom-up technology centric towards a top-down service centric way of providing value to different lines of business.
Consequently I’d like to see us in the itSMF discussing new organizational structures to accommodate big data. You can’t simply assume that big data goes into the IT organization. In large organizations, big data groups can be found in marketing, finance, product development, strategy, and IT. E.g. Will a retained IT have to focus their competencies around data analysis? Given the fact that business analysis is an essential competency for retained corporate IT when it wants to become a service broker, data analysis might become more and more important.
But analyzing big data is not enough. The real value comes from understanding what matters. Too many IT organizations are collecting more data than they know what to do with. Turning it into meaningful information requires a certain amount of understanding (internal or external, cloud or on-premise) supplier performance with the right mindset, because big facts and more information are not always better if the use of data is not taken into consideration. In conclusion Big data is valuable for Service Management as a whole, but does nothing to address the later steps in the IT organization’s decision process. Turning data into information and then building decisions by turning information into business scenario models for tactical predictions might well be a next step in maturing as an industry.