Empowering application development teams with the best tools while creating a unified and highly flexible data layer still remains an operational challenge for the majority of businesses. Hence, data engineering is fast taking the center stage acting as a change agent in the way data is collated, processed and ultimately consumed. Not all AI/ML projects undertaken at an enterprise level are successful and this mainly happens due to lack of accurate data. Despite making generous investments in data analytics initiatives, several organizations often fail to bring them to fruition. Yet companies also end up spending significant time preparing the data before it can be used for decision modeling or analytics.

If Data Science was once the sole space of analysts and data scientists, Augmented Data Science represents the democratized perspective on this area. With Augmented Data Science, the average business user can draw in with cutting-edge analytics tools that take into account Automated Machine Learning (AutoML) and leverage refined analytical techniques and algorithms in a guided environment that utilizes auto-proposals and recommendations to lead users through the unpredictable universe of data science with ease and intuitive tools. As companies are progressively standardizing on augmented analytics, a related model is coming to fruition in the data and analytics market – augmented data management. The innovation is changing the information of the data management landscape and the role of data professionals. Augmented data management utilizes AI and machine learning to make enterprise data management disciplines, for example, information quality and integration, metadata management, master data management, and database management frameworks, "self-arranging and self-tuning," as indicated by Gartner.

When agencies seek to develop or improve upon artificial intelligence applications, they often find that many of today's IT systems are not robust enough to manage AI workloads at scale -- nor can they scale up and offer security at the speed required for AI modeling. This is especially true for legacy IT systems that are not purpose-built, AI-capable infrastructures. In fact, many infrastructures used today for AI have been force-fit -- and mis-fit -- into the AI space. Before we look at what scale is required, and what IT infrastructure model is ideal, let's quickly define the stages of advanced AI and machine learning development. In AI development, there is an initial training stage in which an AI practitioner will run AI model after model after model, drawing from deep wells of existing data. Since AI development is iterative, the data used in the training stage is often required to be live, and therefore sensitive.