Augmented Analytics

Introduction

Today, data plays a critical role in any organization. There is an abundance of data and it is being produced almost all the time from various sources. Big Data has become so much more complex, especially in terms of velocity, veracity, and volume. If you want to stay relevant in the market and keep up with the competition, it is very important to unearth the insights hiding in your data.

How the issue is being addressed today

Over the last few years, we have seen several resources manually connecting data sources by cleansing the data and creating a multitude of reports. The process of sharing these reports across the organization is tedious and error-prone. Subsequently, we witnessed the introduction of a new concept called “self-service BI”, which involved more user-friendly graphical interfaces that used to handle huge volumes of data from multiple data sources, where even non-technical users can choose and manipulate the data sources as shown below.

In both scenarios depicted above, data preparation is still manual and takes a lot of time. Further, there is user biasing, which may lead to wrong output and could have a major impact on organizational decisions. In addition, all these tasks are usually performed by data scientists who spend 80% of their time on data collection and preparation and just 20% of their time on finding meaningful insights. They often indulge in performing simple mechanical tasks such as labelling and cleaning their data, which is makes the process laborious. Furthermore, there is no guarantee that a data scientist analyzes 100% of the data, which can be very critical to any business. Due to these reasons, several small and medium-sized businesses are still in the early stages of analytics adoption despite a strong desire to make use of the data.

As an answer and a solution to all the above-mentioned challenges, there is a dire need to embrace the concept of “Augmented Analytics”.

What is Augmented Analytics?

Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert by automating many aspects of data science, machine learning, and AI model development, management, and deployment.

How augmented analytics augments resources

Augmented Analytics helps in enabling ML/AI created data and analytics by automating the data preparation, insight discovery, and key aspects of data science and AI.NL modelling, such as feature engineering and model selection (AutoML) as described below. It also makes use of machine learning and NLP to understand and interact with data as humans would do on a large scale, free of human biases. In addition, it will reveal insights that resources would never have realized existed; inferences like establishing connections among the data and also suggesting relationships and insights, without the user even thinking to ask for them.

For e.g. A user can ask the required output in a natural language like “What is the impact on revenue over the last 4 months because of COVID?”. These questions can also be extended to other chat platforms like chatbots and voice-based interfaces.

Benefits:

Improves a data scientist’s productivity and collaboration, and ability to focus on strategic projects.
AL/ML augmented models created through augmented analytics are being embedded in enterprise applications, which helps optimize decisions and actions of all employees, not just analysts and data scientists.
Automates the entire process of data preparation, deriving insights, and sharing of reports.
Reduces bias and improve explainabilty.
Gets rid of the need for any technical skills to derive the output.
Improves user adoption, data popularity, social BI integration, and data literacy.
Can be deployed with NLP and conversational interfaces thereby allowing more people across the organization to interact, make predictions, and get actionable insights from data without requiring advanced skills.

Summary:

In a highly competitive world, where static reporting and user-biased reports will limit the collection of insights, companies need to scale and adopt the velocity, veracity, and volume of data to derive meaningful insights and correlations that affect their business. This is where augmented analytics comes into play, by addressing the shortage of data science talent and helping to boost the productivity of data scientists.

References:

Keerthi Prasad D

Presales Consultant, Wipro HOLMES^®

Keerthi is a passionate technologist with 9+ years of experience in Cloud Infrastructure Services. He has performed various roles in Managed Services Solutions, Projects, and Automation Consulting. Keerthi is also experienced in a wide range of Enterprise AI Automation and Tools. He has executed multiple projects in automation advisory as well as design of public and private cloud-based automation and transformation solutions.

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