What is Augmented Analytics
Augmented Analytics can be broadly defined as using machine learning and natural language processing to prepare data, extract insights, and produce analytical applications. Across all phases of the data life cycle, augmented analytics make a data worker’s life easier when dealing with the ever-growing data assets.
How does Augmented Analytics work?
By adopting advanced algorithms, a software would communicate with data as humans would do but on a massive scale. The analysis procedure frequently begins with data collection from private or public platforms.
After data is collected, it requires to be prepared and explored in order to bring out insights together with action strategies to do a little with the knowledge, that should be forward later with the company.
Examples of Augmented Analytics
A notable example of augmented analytics is robot commentators in sports. Natural language generation and transfer learning have helped broadcasting networks produce realtime summaries of in-game actions based on box scores. The box scores as raw data usually are inserted manually into a OLTP system such as SAP. Then it will get fed into a NLG model that can produce commentary. The commentaries sometimes are published directly to the web, often they can also provide references for the human commentators.
Another example is how named entity recognition (NER) and image classification help autonomous vehicles “see” the road. Although road information is complex, the computer vision system is improving itself and making the data increasingly more structured and digestible for decision making such as stops, accelerations, and making turns. An autonomous car with 5G connection is essentially a piece of cloud software that constantly analyzes data.
Benefits of Augmented Analytics
1. Accelerated Data Preparation
Augmented data preparation collects data together faster from different sources. Algorithms are used to discover join keys, schemas, and table connections so transformation can be expedited and even automated. Data quality and enhancement recommendations can be auto-generated by the system. There may also be suggestions for profiling, annotation, and data tagging before the data preparation process even begins. Altogether, clean data will be produced quicker and more efficiently in a software that provides augmented analytic capabilities.
2. Automation of Insights
Covid-19 has accelerated the process of digital transformation, and forever changed the way we work. A remote, digital-first team structure is now seen everywhere. Workflow automation has therefore become a key to operational efficiency and resiliency.
Repetitive tasks such as data preparation, ETL, and visualizations no longer require teams of different functions to get involved. Once data gets ingested, AI can now help parse the data into a readable format, detect patterns and relationships, and suggest insights.
In other words, augmented analytics can be understood as an AI-assisted analytical process that shortens, and sometimes even replaces the time and efforts needed for an analytical project.
3. Reduction of Biases
Allowing the machine to produce analytics can help minimize pre-conceived biases a data worker may have for certain datasets. By focusing on the statistical importances and surveying through a wide range of different datasets, the algorithm may reduce some of the biases and yield better results.
Future of Augmented Analytics
At Acho, we envision a future where data, science and AI are blended into one trusted, instantly accessible resource. Ultimately, businesses should be able to find answers, and deliver value from their data without too much human capital invested.