More and more business establishments demand for advanced analytical tools to influence data-driven methods of communicating with customers and value chain. Artificial Intelligence and Machine Learning (AI/ML) is going to overtake the analytics marketplace in the near future. The focus will be more on the ways of generating insights, their consumption, sharing methods and how they are taken into consideration. This era is in the new orbit that powers time to insights, validity and diluted bias. The transformation from a manual process to AI brewed machine learning escalated capabilities for authoring and manipulate context is worth experiencing.
Evolution of Analytics with Adaptation of Technology
• It began with the logical layered plan, where enfolding of involved relational data is done using a beam of truth for operational reporting.
• Interactive analysis was later introduced with visual-based considerations alongside user trials.
• Now there is a need to saturate AI and machine learning to regulate the process dealing with data science models and operations.
• Considering the present stats, over-consumption and interaction with analytics, it can be predicted that natural language will be a dominant player in times ahead.
Need of Augmentation in Analytics Framework
• There is an extensive requirement of understanding and being benefited from important relationships in data, important drivers, segments, outliers and
• Use of speech recognition innovations – Alexa and Siri, can be done for exploring links with natural language in terms of asking questions.
• Inarguably, explanation of data relationships in natural language besides data visualization is entailed.
• Having the ability to experience augmented analytics serve us with analytics and interaction with data through conversation and also being lodged in applications that we use, are some prerequisites to be considered.
Drawing Parallels Between Manual and Augmented Approach to Analytics
The whole idea of analytics revolves around the known data, known questions and reporting solutions based on the exploration. As the industry prospers, new tools and aspects are getting introduced in the process.
Taking off From Manual Data Analytics
While building analytics model through a manual process, following steps are required:
• Preparation of data (including – manually selecting tables, analyzing and joining them together).
• Finding patterns in data.
• Sharing findings and insights.
• Operationalizing insights.
• Adding a variety of business logic, calculations, groups and hierarchies.
• Cleaning the data and enriching it in different ways by adding new data sources.
• Cataloging, tagging and having a lineage of the data.
Landing at Augmented Analytical Approach & Modern BI
In discovering arrangements in data, today manual exploration of data is done using interactive visualization. Featuring of engineers and building advanced analytics model is manually executed. While augmented data preparation embraces the aspects such as algorithms are used to make a lot of manual processes effortless. It auto-detects the variables and schemas and profiles the data, redefines shapes of data, outliers while making recommendations about cleaning, enriching, lineage and metadata.
With the advancement of augmented analytics, interaction concepts such as NLP (Natural Language Processing), NLU (Natural Language Understanding) and NLG (Natural Language Generation) have been proposed. These concepts have features like auto selection, models are auto selected, even increasingly code is auto-generated. With the addition of natural language narrations, users with different analytical expertise decode the same insights from the pool.
Therefore, the modern BI and augmented analytical approach have proven themselves as a core of visual/audio-based data discovery and exploration in the current scenario of reporting solutions. As the voyage of experiments and exploring the unknown continues, the industry is in need to harmoniously uncover the path to resolve nexus of analytical disputes.