The business environment in the coming days is going to be difficult for any organization, but the ones in the tech industry have a very good chance of being successful depending on product type, quality, and demand. But how do you maximize these three aspects while keeping costs at a minimum? The solution is simple, an agile methodology. If you ever visit a start-up or talk with someone involved in one you will hear this word used an overwhelming number of times, sometimes they say the word even if when it doesn’t mean anything. Let’s see what ‘agile’ model really is.
The agile methodology involves breaking up the task into several stages and constantly improving the product by means of collaboration with customers and end-users. The main principle of an agile model is the constant improvement at every possible iteration. The development process will be divided into manageable parts, these parts are assembled at the end of every stage for review and testing by customers. By all rights, the product is out in the market. The customers use the product, make suggestions on how to improve, and the next stage begins with the results of the first release as the template. This allows a company to keep releasing their products fast and then re-release better versions in the next development cycle. It is also important to keep trying even when you do succeed as the tech industry is the fastest-changing field in play.
Data science is an interdisciplinary field that makes use of scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in both structured and unstructured forms. The major ways of processing data are deep learning, data mining, and big data analytics among others. In such a field it impossible to specify a given timeline for research as everything changes by the second as more data is processed. The results are almost always uncertain in the context of everyday applications, this makes data science an ideal field to incorporate agile methodology.
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. It originally began as a set of best practices, since then it has matured becoming a new and independent approach to data analytics. It is the application of the agile methodology in Data science as it focuses on collaboration, division of workflow, and iterative development.
The term ‘agile model’ has evolved so much that it sometimes loses its meaning, to be agile you need to adopt a certain philosophy and also apply it to every aspect of the workplace. It means adapting to each new situation as they arise, taking quick action while learning from past actions, trying different things, and creating a dynamic environment.