Today, the process for preparing and analyzing data, interpreting results, and using those results to streamline business processes is a manual, time-intensive job. As data volumes increase and become more complex, it becomes ever more difficult to identify the most accurate, relevant, and actionable findings.
Augmented analytics is used to assist us with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and Bussiness platforms
It uses data analytics software with augmented analytics makes use of Machine learning and NLP to understand and interact with data as humans would do but on a large scale. The analysis process often starts with data collection from public or private sources. You can think of the web or of a private database.
After data is gathered, it needs to be prepared and analyzed in order to extract insights, that should be then shared with the organization, together with action plans to do something with the learning. Here is the step by step process
- Augmented data preparation: Uses ML to find anomalies in data, check for data quality, standardizing and profiling data
- Augmented data discovery: Uses ML to automatically find, visualize and narrate relevant findings without having to write any algorithm or build any model
- Augmented data science: Automates key aspects of advanced analytics such as feature selection.
All these tasks are usually performed by data scientists, who spend 80% of their time on the collection and preparation of data, and just the remain 20% on finding insights. The goal of augmented analytics is to automate the processes of data collection and data preparation in order to save data scientists 80% of the time. However, the real, ultimate goal of augmented analytics is to completely replace the data science teams with AI, taking care of the entire analysis process from data collection to business recommendations to decision-makers.
Non-technical areas like marketing are going to be radically transformed with augmented analytics. It is a usual practice for brand managers, chief marketing officers, and other marketers to depend on an analyst for data processing and analysis. This dependency on third-party is time-consuming, cost-heavy, and inefficient.
Augmented analytics frees up data scientists to solve more complex problems by automating the other repetitive and laborious tasks that would otherwise consume a lot of time. With automation, basic queries and repetitive reports can be done with machine aid.
Sales professionals would benefit greatly from direct data access and receiving detailed and automated analysis of their sales-pitches, win-losses, and performance metrics tracking. Having a responsive analytics solution would enable them to be more agile.
As they do not have to wait around for weekly or monthly reporting, it would help them to improve immediately. With an augmented analytics platform, they will be able to get quick competitor performance comparisons and brand analysis.
Augmented analytics is not mature yet but is bound to grow at a very fast rate in the next couple of years to disrupt the BI and Analytics market. The demand for skilled data scientists is ever increasing. Augmented analytics is an answer to this shortage of skilled data scientists that can iteratively perform the data-to-insight-to-action activities. Organizations need to adopt this as the platform is mature to stay abreast and relevant in the industry.