“Smart Data Discovery goes beyond data monitoring to help business users discover subtle and important factors and identify issues and patterns within the data so the organization can identify challenges and capitalize on opportunities. These tools allow business users to leverage sophisticated analytical techniques without the assistance of technical professionals or analysts. Users can perform advanced analytics in an easy-to-use, drag and drop interface without knowledge of statistical analysis or algorithms. Smart Data Discovery tools should enable gathering, preparation, integration and analysis of data and allow users to share findings and apply strategic, operational and tactical activities and will suggest relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns and helps to forecast and predict results for planning activities.
Augmented Data Preparation empowers business users with access to meaningful data to test theories and hypotheses without the assistance of data scientists or IT staff. It allows users access to crucial data and Information and allows them to connect to various data sources (personal, external, cloud, and IT provisioned). Users can mash-up and integrate data in a single, uniform, interactive view and leverage auto-suggested relationships, JOINs, type casts, hierarchies and clean, reduce and clarify data so that it is easier to use and interpret, using integrated statistical algorithms like binning, clustering and regression for noise reduction and identification of trends and patterns. The ideal solution should balance agility with data governance to provide data quality and clear watermarks to identify the source of data.
Augmented Analytics automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.”
The definitions above come from a post by Kartik Patel entitled, “What is Augmented Analytics and Why Does it Matter?.”
Over the years I’ve loved playing with data and learnt so much from it – about networks, about services, about opportunities, about failures, about gaps, etc. However, modern statistical analysis techniques fall into one of the categories described in “You have to love being incompetent“, where I’m yet to develop the skills to a comfortable level. Revisiting my fifth year uni mathematics content is more nightmare than dream, so if augmented analytics tools can bypass the stats, I can’t wait to try them out.
The concepts described by Kartik above would take those data learning opportunities out of the data science labs and into the hands of the masses. Having worked with data science labs in the past, the value of the information has been mixed, all dependent upon which data scientist I dealt with. Some were great and had their fingers on the pulse of what data could resolve the questions asked. Others, not so much.
I’m excited about augmented analytics, but I’m even more excited about the layer that sits on top of it – the layer that manages, shares and socialises the aggregation of questions (and their answers). Data in itself doesn’t provide any great insight. It only responds when clever questions are asked of it.
OSS data has an immeasurable number of profound insights just waiting to be unlocked, so I can’t wait to see where this relatively nascent field of augmented analytics takes us.