Using “big data” to create smart data
Robert Hell, President Systecon, reflects on a subject that few can avoid – "big data"
Summer is almost over, and most of us have thrown ourselves back into our projects. I hope this newsletter will provide some inspiration and ideas for the autumn, and I will begin by reflecting on a subject that few can avoid, namely "big data."
Big data and everything associated with it is perhaps the top technology trend right now. Most large companies are investing heavily in research in the field and adapting their operations to the new opportunities that arise. Google has been a driving force, even before the concept really took shape, but much of the development is now run by new, fast-growing companies. Many predict big data will change the way we work and require new types of skills.
Big data is created by everything around us. Systems and sensors collect data in real time, which are distributed instantly through mobile devices and networks (the internet of things). The evolution of technology makes it easier and faster for systems to communicate with each other and transmit large amounts of data. And the pace is increasing. Linked to the growing amount of data we are also seeing increased interest in doing something with all of the data collected. Consequently, analytics, or what is often referred to as business analytics, has also become an area that has received a big boost in interest. The question is how to transform big data into smart data?
Within Systems Lifecycle Management, Systecon's core area, this development provides new opportunities. The analyzes performed by Opus Suite is a good example of a type of highly advanced business analytics that makes it possible not only to react based on historical data, but also to predict the effects of various policy options. We see more and more customers who want to automate their analytical processes and connect Opus Suite with different information systems to speed up their analysis work. These systems contain various parameters in which data collection is increasingly automated and frequent rapidly expanding the amount of data. Technically speaking, it is not difficult to make that kind of integration between information systems and Opus Suite.
However, there are a lot of challenges to consider. Improperly handled, automated analysis processes might result in large and rambling models that take a very long time to run or even produce erroneous results. Instead of being able to quickly perform analysis, the risk is that you lose overall perspective and drown in details. Another risk with automated analysis processes is that you rely too much on the results presented from a simulation, for example, and that you do not understand what the results are actually saying and why. To build effective and relevant models and correctly interpret the results is still an art that will require both skill and experience.
However, automation provides major opportunities with the right approach. In an article Systecon's Development Manager, Tomas Eriksson, explains how to think in order to build effective models that support the decisions you'll make. It is important not to lose sight of these aspects or to try and remove the analysts' role when an increasingly large part of the processes is automated.
In another recent article, different types of capacity analyzes that help our rail customers make sound decisions and dimension their resources properly are explored. To anticipate and understand the consequences of various changes in operations, such as the expansion of traffic or the train fleet, the introduction of new types of vehicles or conversions of train depots, is not easy without sophisticated analysis support. My feeling is that many organizations take these issues too lightly and do not see how the many interacting parameters affect each other. This can result in project delays, disruption for travelers and increased costs. Systecon has for many years worked with this type of analysis. Opus Suite, in particular, is specifically adapted to study and optimize the interaction between production capacity and the operation and maintenance infrastructure that is designed to keep different types of technical systems available.
Another example of capacity analysis is the type of analysis that we provide the Swedish Defence Materiel Administration (FMV) and the Swedish Armed Forces with using the specially developed tool PVL (PrognosVerktyg Logistik). With PVL, the demands on the logistics supply for various operational scenarios are calculated. This tool has, for example, enabled us to support the planning of international operations and military exercises. We have also recently been able to provide information to FMV regarding resource consumption and the amount of damages for various operational scenarios inflicted on military units that the Swedish government has decided to station on the island of Gotland. Thanks to the results of these analyzes the Armed Forces will be able to better understand the consequences and risks of various policy options.
I would argue that in most organizations there are great benefits to be gained by taking up the opportunities provided by systematic and structured analysis activities. It provides a greater understanding of the challenges ahead and better-informed decision-making. Whether you have access to big or small data, you too can develop smart data.
Robert Hell
President, Systecon AB